◈ Homepage — https://www.deeplearning.ai/✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter Community Membership Start Learning AI PROMPTING FOR EVERYONE Enroll Now AI is the new electricity. You are the spark. Get the latest AI news, courses, events, and insights from Andrew Ng and other AI leaders. Subscribe First Name Last Name Where do you live? Select a country What is your role? Please select Keep me updated on the latest news, events, and courses Subscribe Email* Keep me updated on the latest news, courses, and workshops and events announcements Join over 7 million people learning how to use and build AI AI Courses and Specializations Build a foundation of machine learning and AI skills, and understand how to apply them in the real world. 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Read more FREE RESOURCES Get Started with AI and Machine Learning How to Build Your Career in AI A practical roadmap to building your career in AI from AI pioneer Andrew Ng Download Machine Learning Yearning An introductory book about developing ML algorithms. Download A Complete Guide to Natural Language Processing A comprehensive guide covering all you need to know about NLP. Read more Stay up to date on the latest news, courses, and AI events Subscribe First Name Last Name Where do you live? Select a country What is your role? Please select Keep me updated on the latest news, events, and courses Subscribe Courses The Batch Community Careers About Contact Help Terms of Use | Privacy Policy ◈ Interior Pages — 34 pages crawledSeedance Makes A Splash, Nvidia's AI-Guided Chip Designs, Helping Robots Not Forget ✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Weekly Issues Andrew's Letters Data Points ML Research Business Science Culture Hardware AI Careers About Subscribe The Batch Weekly Issues issue 352 Published May 8, 2026 Reading time 15 min read Published May 08, 2026 Reading time 15 min read Share Loading the Elevenlabs Text to Speech AudioNative Player... Dear friends, There will be no AI jobpocalypse.  The story that AI will lead to massive unemployment is stoking unnecessary fear. AI — like any other technology — does affect jobs, but telling overblown stories of large-scale unemployment is irresponsible and damaging. Let’s put a stop to it. I’ve expressed skepticism about the jobpocalypse in previous letters. I’m glad to see that the popular press is now pushing back on this narrative. The image below features some recent headlines.  Software engineering is the sector most affected by AI tools, as coding agents race ahead. Yet hiring of software engineers remains  strong ! So while there are examples of AI taking away jobs, the trends strongly suggest the net job creation is vastly greater than the job destruction — just like earlier waves of technology. Further, despite all the exciting progress in AI, the U.S. unemployment rate remains a healthy 4.3%. Why is the AI jobpocalypse narrative so popular? For one thing, frontier AI labs have a strong incentive to tell stories that make AI technology sound more powerful. At their most extreme, they promote science-fiction scenarios of AI “taking over” and causing human extinction. If a technology can replace many employees, surely that technology must be very valuable!  Also, a lot of SaaS software companies charge around $100-$1000 per user/year. But if an AI company can replace an employee who makes $100,000 — or make them 50% more productive — then charging even $10,000 starts to look reasonable. By anchoring not to typical SaaS prices but to salaries of employees, AI companies can charge a lot more. Additionally, businesses have a strong incentive to talk about layoffs as if they were caused by AI. After all, talking about how they’re using AI to be far more productive with fewer staff makes them look smart. This is a better message than admitting they overhired during the pandemic when capital was abundant due to low interest rates and a massive government financial stimulus. To be clear, I recognize that AI is causing a lot of people’s work to change. This is hard. This is stressful. (And to some, it can be fun.) I empathize with everyone affected. At the same time, this is very different from predicting a collapse of the job market.  Societies are capable of telling themselves stories for years that have little basis in reality and lead to poor society-wide decision making. For example, fears over nuclear plant safety led to under-investment in nuclear power. Fears of the “population bomb” in the 1960s led countries to implement harsh policies to reduce their populations. And worries about dietary fat led governments to promote unhealthy high-sugar diets for decades.  Now that mainstream media is openly skeptical about the jobpocalypse, I hope these stories will start to lose their teeth (much like fears of AI-driven human extinction have).  Contrary to the predictions of an AI jobpocalypse, I predict the opposite: There will be an AI jobapalooza! AI will lead to a lot more good AI engineering jobs, and I’m also optimistic about the future of the overall job market. What AI engineers do will be different from traditional software engineering, and many of these jobs will be in businesses other than traditional large employers of developers. In non-AI roles, too, the skills needed will change because of AI. That makes this a good time to encourage more people to become proficient in AI,  and make sure they’re ready for the different but plentiful jobs of the future! Keep building, Andrew  A MESSAGE FROM  DEEPLEARNING.AI Most agents respond with text. Learn to build agents that render charts, forms, and interactive user interfaces. In this course, you’ll connect a LangChain agent to a React front end and build across the generative user-interface spectrum, ending with a full-stack app that enables users and agents work on a shared state.  Enroll for free! News ByteDance Bids for Video Leadership As OpenAI prepares to shut down Sora, ByteDance made its own video generation model available to hundreds of millions of users. What’s new:  ByteDance added  Seedance 2.0 , its multimodal video generator, to its popular video-editing app  CapCut . Launched earlier this year in China, the model now reaches paying CapCut users in Southeast Asia, Latin America, Africa, the Middle East, parts of Europe, Japan, and the United States. Input/output:  Text, images, audio, and video in (up to 3 video clips, 9 images, and 3 audio clips), synchronized video and audio out (4 to 15 seconds at 480 or 720 pixels on the shorter edge in 6 aspect ratios: 21:9, 16:9, 4:3, 1:1, 3:4, and 9:16) Features:  Lip-synced dialogue in multiple languages, ambient sound, music, multiple camera shots with cuts in a single clip, camera and lighting controlled by prompts, outputs marked by invisible watermark, blocking of input images that contain real faces or copyrighted characters (via CapCut) Performance:  Within top two on Arena AI and Artificial Analysis video leaderboards Availability/price:  Via CapCut (Jianying in China) paid tier, Dreamina web interface, API via the ByteDance services BytePlus and Volcengine, and third-party providers including  Higgsfield.ai  for $0.30 per second of output (720 pixels, audio included) or $0.24 per second for faster processing by SeeDance 2.0 Fast Undisclosed:  Architecture, parameter count, training data and methods How it works:  Seedance 2.0  extends  ByteDance’s earlier work from synchronous generation of audio-video streams in parallel to joint generation within a unified system. ByteDance’s  launch announcement  characterizes the architecture as “sparse.” The model accepts video-audio reference input for four tasks: (i) Referenced-based generation applies subject, motion, visual effects, and/or style cues to new output. (ii) Editing modifies specified regions, characters, actions, and/or audio within existing video. (iii) Extension produces output that precedes or succeeds existing video. (iv) Combination modes pair these (for example, replacing the subject in an existing video with one from a reference image). Audio is generated simultaneously with video, producing stereo dialogue, sound effects, and background audio. The model generates sequential shots and cuts in a single pass rather than generating and assembling separate clips, which helps to maintain character and scene consistency. Performance:  Seedance 2.0 ranks first and second on two independent leaderboards that rank models through blind votes of human preference in head-to-head matchups. Alibaba’s HappyHorse-1.0 is the closest challenger on both leaderboards. On  arena.ai , Seedance 2.0 achieved 1,460 Elo on text-to-video performance and 1,454 Elo on  image-to-video  performance, narrowly leading both categories over HappyHorse-1.0 (1,444 Elo on each). However, the leaderboard labels Seedance 2.0 and HappyHorse-1.0 results as preliminary. On  Artificial Analysis , Alibaba’s HappyHorse-1.0 leads three of four video categories (image-to-video without audio and text-to-video with and without audio), while Seedance 2.0 ranks second. Seedance 2.0 leads image-to-video performance wi Blog - DeepLearning.AI ✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Blog Breaking Into AI Working AI AI Heroes Ambassador Spotlight News & Events News & Events Thank You for Learning With Us This Year. A message from DeepLearning.AI Dear Friends, This year was special. Millions of you enrolled, experimented, tried new tools, pushed through tough concepts, shared projects, and kept returning for the next challenge. And because so many of you wanted a… Dec 22, 2025 News & Events Engineering Multi-Agent Systems: A Path from Prototype to Production At DeepLearning.AI, we recently partnered with CrewAI to build the course Design, Develop, and Deploy Multi-Agent Systems with CrewAI. In it, instructor João Moura (Co-founder and CEO of CrewAI) shows how developers can move beyond… Nov 19, 2025 Community Inside AI Dev 25 X NYC DeepLearning.AI welcomed 1200 attendees (and a total audience of more than 1400) to its developer conference in Manhattan. We packed Convene Brookfield Place to capacity for speakers and representatives from Google, Anthropic, Amazon, Vercel, Groq,… Nov 17, 2025 Community Introducing DeepLearning.AI Pro With exclusive courses, tools, and community, DeepLearning.AI Pro is the one membership that keeps you at the forefront of AI. Oct 31, 2025 News & Events From coding in a notebook to a clickable demo: a practical playbook for GenAI fast prototyping Neeraj Kumar is driven by a passion for exploring the intersection of artificial intelligence (AI) and robotics. Sep 19, 2025 News & Events Python for Data Analysis: Key Stats and Trends Neeraj Kumar is driven by a passion for exploring the intersection of artificial intelligence (AI) and robotics. Apr 9, 2025 Breaking Into AI Learn Fast and Build Things: How Julia Kryuchkova Went From No-Code to Writing Python Apps Recently, I completed the short course AI Python for Beginners by DeepLearning.AI, taught by Andrew Ng. I am not a programmer, so I was thrilled when immediately after completing the first course in the sequence — which took about an hour — I managed to create my first program in Python. Oct 17, 2024 Ambassador Spotlight 2024 Pie & AI Ambassador Spotlight: Neeraj Kumar K, Bangalore, India Neeraj Kumar is driven by a passion for exploring the intersection of artificial intelligence (AI) and robotics. Mar 14, 2024 Ambassador Spotlight 2023 Pie & AI Ambassador Spotlight: Armielyn Obinguar, Manila, Philippines Armielyn Obinguar aims to grow the DeepLearning.AI community in the Philippines and keep positively impacting society. Nov 17, 2023 Ambassador Spotlight 2023 Pie & AI Ambassador Spotlight: Cheng Zhang, Winnipeg, Canada Cheng Zhang moved from the eastern hemisphere to the west, and now hosts events for Chinese AI developers in Canada. Nov 10, 2023 Ambassador Spotlight 2023 Pie & AI Ambassador Spotlight: Markey Tan, Shenzhen, China Markey Tan believes that regardless of one’s background, anyone can make a meaningful contribution to the field of AI. Nov 3, 2023 Ambassador Spotlight 2023 Pie & AI Ambassador Spotlight: Shilpi Agarwal, Silicon Valley, USA Shilpi Agarwal’s mission is to build an ethics-first data and AI world, and the Pie & AI events are helping her expand it. Oct 20, 2023 Ambassador Spotlight 2023 Pie & AI Ambassador Spotlight: George V Scripcariu, Bucharest George Scripcariu believes AI will become even more present and embedded in all aspects of our lives and is preparing his community for it. Oct 13, 2023 Ambassador Spotlight 2023 Pie & AI Ambassador Spotlight: Dmitriy Starson, Menlo Park Dmitriy Starson has hosted many DeepLearning.AI events, including a joint panel on AI for Good and a series with representatives from Sequoia Capital. Oct 6, 2023 Ambassador Spotlight 2023 Pie & AI Ambassador Spotlight: Tarun R Jain, Bengaluru I started my journey in Machine Learning and Deep Learning with Professor Andrew Ng’s Coursera courses. Without these courses, I would probably have zero knowledge in this field. Being a part of this community allowed me to access valuable resources and courses. It also provided a supportive network of peers who shared my passion for AI. As I continued to learn and grow, I found myself increasingly engaged in organizing events, workshops, and discussions to help others navigate the complexities of deep learning. Sep 28, 2023 Ambassador Spotlight 2023 Pie & AI Ambassador Spotlight: Godwin France, Accra I want to assist my community of young Africans to be in the know about cutting-edge technologies. The DeepLearning AI Event Ambassador role has given me so many opportunities within the technology and AI community. I have been able to connect with like-minded individuals from across the globe. As a STEM educator, I believe that now is the time for many young Africans to exponentially grow their acquisition of digital skills. AI will feed into every aspect of our lives, and DeepLearning AI is the right place to get started. Sep 21, 2023 Ambassador Spotlight Pie & AI Ambassador Spotlight 2023: Meet the Leaders Building a Network of Global AI Hubs Since 2019, Pie & AI ambassadors have hosted over 700 events. Each year we celebrate their efforts. Sep 14, 2023 Community Answers from the instructor: AI for Good’s Robert Monarch I’m currently a machine learning professional and have been working in Silicon Valley for about 15 years. I’ve been with big companies, like Amazon Web Services and Apple, and startups focused on data labeling and machine learning. Jun 29, 2023 Community A conversation with Microsoft’s AI for Good Lab Director: Juan Lavista Ferres I have been interested in AI for Good-type work for the majority of my career. Almost 20 years ago, a good friend of mine lost a child to SIDS (sudden infant death syndrome). As a result, I started volunteering with Seattle Children’s Hospital, helping to understand the causes and preventative steps that could be taken to reduce the risk of SIDS. This volunteer engagement led to the opportunity to create the Microsoft AI for Good Lab. I could have never imagined the scope and scale of the work we’re fortunate to do today. From improving health outcomes to biodiversity conservation and humanitarian response, the relevance and need for AI in every sector has never been more pronounced. Jun 29, 2023 Ambassador Spotlight 2023 Pie & AI Ambassador Spotlight: Vikram Mark Radhakrishnan, The Hague During my time as a PhD student, I realized that my biggest professional and personal interests are artificial intelligence and community building. As a PhD student in astronomy, I used to organize communities for astronomy enthusiasts, bringing together scientists, amateur astronomers, and astrophotographers to connect and learn from each other. And, since my research required knowledge of deep learning algorithms, I completed the courses on deep learning and TensorFlow from Deeplearning.AI and subscribed to the newsletter. During that time, I realized that my biggest professional and personal interests are artificial intelligence and community building. May 26, 2023 Page 1 of 7 Older Posts Courses The Batch Community Careers About Contact Help Terms of Use | Privacy Policy Machine Learning Specialization - DeepLearning.AI ✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog ✨ AI Dev x SF 26 Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog ✨ AI Dev x SF 26 Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Overview Course Outline Instructors All Courses Professional Certificate Machine Learning Specialization All Courses Professional Certificate Machine Learning Specialization Professional Certificate Beginner 94 hours Machine Learning Specialization Instructor : Andrew Ng Earn a certificate with PRO Enroll Now Also available on Coursera Beginner 94 hours 151 Video Lessons 32 Code Examples 42 Graded Assignments PRO Earn a certificate with PRO Instructor: Andrew Ng DeepLearning.AI Stanford Online Learn more about Membership PRO Plan Start Learning Also available on Coursera How the Machine Learning Specialization can help you Newly rebuilt and expanded into 3 courses, the updated Specialization teaches foundational AI concepts through an intuitive visual approach, before introducing the code needed to implement the algorithms and the underlying math. I’m a complete beginner I enrolled in but didn’t complete the original Machine Learning course I’ve already completed the original Machine Learning course Learner reviews Luo Yuzheng “As a Behavioral Scientist, I was able to adopt methods to understand my customers better, overcome the traditional ‘one-size-fits-all’ approach, and design interventions which account for personality and individual differences.” View More Chirag Godawat “I gained confidence in my knowledge of machine learning. Since then, I’ve become a machine learning mentor, got a research paper published in IEEE, decided to pursue my Masters in Machine Learning, and was able to land a job at JP Morgan Chase.” View More Hsin-Wen Chang “The Machine Learning course became a guiding light. Andrew Ng explains concepts with simple visualizations and plots. I learned how to evaluate my training results and explain the outcomes to my colleagues, boss, and even the vice president of our company.” View More Nicholas Muchinguri “The Machine Learning course helped develop my problem-solving skills, inspired an attitude of experimentation, and shaped a passion for machine learning. Since then, I have automated several investment processes and experimented with investment data.” View More Aakash Saroop “The Machine Learning course by Andrew Ng expanded my knowledge, so I could write a research paper on Facial Emotion Recognition and land an internship at Morgan Stanley.” Shahid Mahmood “In 2017, the Machine Learning course helped me gain a deep insight into Natural Language Processing and got me thinking about a new ML project with a friend. We built an ML app with a cloud instance to enhance recorded autobiographies with NLP-generated insights!” View More Chrysovalantis Constantinou “I’m a Computational Scientist with a Ph. D. in theoretical nuclear phsyics. I was working on a research project that involved archeological datasets that eventually led to a publication. The foundations of machine learning from Andrew’s class were essential in making it happen.” View More Youness Boutaib “Andrew’s teaching style helped me grasp foundational concepts. The newly acquired knowledge from the Machine Learning course helped me land a second postdoctoral position, run tutorials for two data science courses, and write my first machine learning paper!” View More Nektarios Kalogridis “Enrolling in the Machine Learning course was the most consequential decision I’ve ever made. When I was facing unemployment, I turned things around by combining deep learning and Wall Street data to materialize my start-up dream!” View More Previous slide Next slide Learner Reviews close Instructor Andrew Ng Founder, DeepLearning.AI; Co-founder, Coursera Course Outline Course 1 Course 2 Course 3 Supervised Machine Learning: Regression and Classification Week 1: Introduction to Machine Learning Overview of Machine Learning Supervised vs. Unsupervised Machine Learning Practice Quiz: Supervised vs unsupervised learning Regression Model Practice Quiz: Regression Model Train the model with gradient descent Practice quiz: Train the model with gradient descent Week 2: Regression with multiple input variables Multiple linear regression Practice quiz: Multiple linear regression Gradient descent in practice Practice quiz: Gradient descent in practice Week 2 practice lab: Linear regression Week 3: Classification Classification with logistic regression Practice quiz: Classification with logistic regression Cost function for logistic regression Practice quiz: Cost function for logistic regression Gradient descent for logistic regression Practice quiz: Gradient descent for logistic regression The problem of overfitting End of Access to Lab Notebooks Practice quiz: The problem of overfitting Week 3 practice lab: logistic regression Conversations with Andrew (Optional) Acknowledgments Advanced Learning Algorithms Week 1: Neural Networks Neural networks intuition Practice quiz: Neural networks intuition Neural network model Practice quiz: Neural network model TensorFlow implementation Practice quiz: TensorFlow implementation Neural network implementation in Python Practice quiz: Neural network implementation in Python Speculations on artificial general intelligence (AGI) Vectorization (optional) Practice Lab: Neural networks Week 2: Neural network training Neural Network Training Practice quiz: Neural Network Training Activation Functions Practice quiz: Activation Functions Multiclass Classification Practice quiz: Multiclass Classification Additional Neural Network Concepts Practice quiz: Additional Neural Network Concepts Back Propagation (Optional) Practice Lab: Neural network training Week 3: Advice for applying machine learning Advice for applying machine learning Practice quiz: Advice for applying machine learning Bias and variance Practice quiz: Bias and variance Machine learning development process Practice quiz: Machine learning development process Skewed datasets (optional) Practice Lab: Advice for applying machine learning Week 4: Decision trees Decision trees Practice quiz: Decision trees Decision tree learning Practice quiz: Decision tree learning Tree ensembles Practice quiz: Tree ensembles End of Access to Lab Notebooks Practice Lab: Decision Trees Conversations with Andrew (Optional) Acknowledgments Unsupervised Learning, Recommenders, Reinforcement Learning Week 1: Unsupervised learning Welcome to the course! Clustering Practice Quiz: Clustering Practice Lab 1 Anomaly detection Practice quiz: Anomaly detection Practice Lab 2 Week 2: Recommender systems Collaborative filtering Practice quiz: Collaborative filtering Recommender systems implementation detail Practice lab 1 Practice quiz: Recommender systems implementation Content-based filtering Practice Quiz: Content-based filtering Practice lab 2 Principal Component Analysis Week 3: Reinforcement learning Reinforcement learning introduction Practice quiz: Reinforcement learning introduction State-action value function Quiz: State-action value function Continuous state spaces Quiz: Continuous state spaces End of Access to Lab Notebooks Practice Lab: Reinforcement Learning Summary and thank you Conversations with Andrew (Optional) Acknowledgments Elevate your learning experience with Pro Upgrade to Pro and gain unlimited accomplishments on your resume Learn More Skills you will gain Linear Regression Logistic Regression Neural Networks Decision Trees Recommender Systems Supervised Learning Logistic Regression for Classification Gradient Descent Regularization to Avoid Overfitting Tensorflow Tree Ensembles XGBoost Advice for Model Development Unsupervi LangChain for LLM Application Development - DeepLearning.AI ✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog ✨ AI Dev x SF 26 Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog ✨ AI Dev x SF 26 Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Overview Course Outline Instructors All Courses Short Course LangChain for LLM Application Development All Courses Short Course LangChain for LLM Application Development Short Course Beginner 1 hour 38 mins LangChain for LLM Application Development Instructors : Harrison Chase, Andrew Ng LangChain 🦜🔗 Earn an accomplishment with PRO Enroll for Free Beginner 1 hour 38 mins 8 Video Lessons 6 Code Examples 1 Graded Assignment PRO Earn an accomplishment with PRO Instructors: Harrison Chase, Andrew Ng LangChain 🦜🔗 Learn more about Membership PRO Plan Start Learning What you'll learn Learn LangChain directly from the creator of the framework, Harrison Chase Apply LLMs to your proprietary data to build personal assistants and specialized chatbots Use agents, chained calls, and memories to expand your use of LLMs About this course In LangChain for LLM Application Development, you will gain essential skills in expanding the use cases and capabilities of language models in application development using the LangChain framework. In this course you will learn and get experience with the following topics: Models, Prompts and Parsers: calling LLMs, providing prompts and parsing the response Memories for LLMs: memories to store conversations and manage limited context space Chains: creating sequences of operations Question Answering over Documents: apply LLMs to your proprietary data and use case requirements Agents: explore the powerful emerging development of LLM as reasoning agents. This one-hour course, instructed by the creator of LangChain Harrison Chase as well as Andrew Ng will vastly expand the possibilities for leveraging powerful language models, where you can now create incredibly robust applications in a matter of hours. Who should join? LangChain for LLM Application Development is a beginner-friendly course. Basic Python knowledge will help you get the most out of this course. Course Outline 8 Lessons・ 6 Code Examples Introduction Video ・ 3 mins Models, Prompts and parsers Video with Code Example ・ 18 mins Memory Video with Code Example ・ 17 mins Chains Video with Code Example ・ 13 mins Question and Answer Video with Code Example ・ 15 mins Evaluation Video with Code Example ・ 15 mins Agents Video with Code Example ・ 14 mins Conclusion Video ・ 1 min Quiz Graded ・Quiz ・ 10 mins Elevate your learning experience with Pro Upgrade to Pro and gain unlimited accomplishments on your resume Learn More Instructors Harrison Chase Co-Founder and CEO of LangChain Andrew Ng Founder, DeepLearning.AI; Co-founder, Coursera LangChain for LLM Application Development Beginner 1 hour 38 mins 8 Video Lessons 6 Code Examples 1 Graded Assignment PRO Earn an accomplishment with PRO Instructors: Harrison Chase, Andrew Ng LangChain 🦜🔗 Learn more about Membership PRO Plan Start Learning Course access is free for a limited time during the DeepLearning.AI learning platform beta! Enroll for Free Want to learn more about Generative AI? Keep learning with updates on curated AI news, courses, events, as well as Andrew’s thoughts from DeepLearning.AI! Start Learning Courses The Batch Community Careers About Contact Help Terms of Use │ Privacy Policy Ambassador Spotlight | AI Events - DeepLearning.AI ✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Breaking Into AI Working AI AI Heroes Ambassador Spotlight News & Events Ambassador Spotlight Meet the global team of organizers behind Pie and AI Collection of 36 Post s Ambassador Spotlight 2024 Pie & AI Ambassador Spotlight: Neeraj Kumar K, Bangalore, India Neeraj Kumar is driven by a passion for exploring the intersection of artificial intelligence (AI) and robotics. Mar 14, 2024 Ambassador Spotlight 2023 Pie & AI Ambassador Spotlight: Armielyn Obinguar, Manila, Philippines Armielyn Obinguar aims to grow the DeepLearning.AI community in the Philippines and keep positively impacting society. Nov 17, 2023 Ambassador Spotlight 2023 Pie & AI Ambassador Spotlight: Cheng Zhang, Winnipeg, Canada Cheng Zhang moved from the eastern hemisphere to the west, and now hosts events for Chinese AI developers in Canada. Nov 10, 2023 Ambassador Spotlight 2023 Pie & AI Ambassador Spotlight: Markey Tan, Shenzhen, China Markey Tan believes that regardless of one’s background, anyone can make a meaningful contribution to the field of AI. Nov 3, 2023 Ambassador Spotlight 2023 Pie & AI Ambassador Spotlight: Shilpi Agarwal, Silicon Valley, USA Shilpi Agarwal’s mission is to build an ethics-first data and AI world, and the Pie & AI events are helping her expand it. Oct 20, 2023 Ambassador Spotlight 2023 Pie & AI Ambassador Spotlight: George V Scripcariu, Bucharest George Scripcariu believes AI will become even more present and embedded in all aspects of our lives and is preparing his community for it. Oct 13, 2023 Ambassador Spotlight 2023 Pie & AI Ambassador Spotlight: Dmitriy Starson, Menlo Park Dmitriy Starson has hosted many DeepLearning.AI events, including a joint panel on AI for Good and a series with representatives from Sequoia Capital. Oct 6, 2023 Ambassador Spotlight 2023 Pie & AI Ambassador Spotlight: Tarun R Jain, Bengaluru I started my journey in Machine Learning and Deep Learning with Professor Andrew Ng’s Coursera courses. Without these courses, I would probably have zero knowledge in this field. Being a part of this community allowed me to access valuable resources and courses. It also provided a supportive network of peers who shared my passion for AI. As I continued to learn and grow, I found myself increasingly engaged in organizing events, workshops, and discussions to help others navigate the complexities of deep learning. Sep 28, 2023 Ambassador Spotlight 2023 Pie & AI Ambassador Spotlight: Godwin France, Accra I want to assist my community of young Africans to be in the know about cutting-edge technologies. The DeepLearning AI Event Ambassador role has given me so many opportunities within the technology and AI community. I have been able to connect with like-minded individuals from across the globe. As a STEM educator, I believe that now is the time for many young Africans to exponentially grow their acquisition of digital skills. AI will feed into every aspect of our lives, and DeepLearning AI is the right place to get started. Sep 21, 2023 Ambassador Spotlight Pie & AI Ambassador Spotlight 2023: Meet the Leaders Building a Network of Global AI Hubs Since 2019, Pie & AI ambassadors have hosted over 700 events. Each year we celebrate their efforts. Sep 14, 2023 Ambassador Spotlight 2023 Pie & AI Ambassador Spotlight: Vikram Mark Radhakrishnan, The Hague During my time as a PhD student, I realized that my biggest professional and personal interests are artificial intelligence and community building. As a PhD student in astronomy, I used to organize communities for astronomy enthusiasts, bringing together scientists, amateur astronomers, and astrophotographers to connect and learn from each other. And, since my research required knowledge of deep learning algorithms, I completed the courses on deep learning and TensorFlow from Deeplearning.AI and subscribed to the newsletter. During that time, I realized that my biggest professional and personal interests are artificial intelligence and community building. May 26, 2023 Ambassador Spotlight 2023 Pie & AI Ambassador Spotlight: Maxwell Nzekwe, Lagos I joined the Deeplearning.AI community as an event ambassador because my primary goal is to help promote the community and its events and to provide information and assistance to those who are interested in learning more about deep learning and AI. I noticed that a lot of students and professionals want to get into tech, and that inspired me to help them. I am passionate about building the Deeplearning.AI community in Nigeria. May 19, 2023 Ambassador Spotlight 2023 Pie & AI Ambassador Spotlight: Irvi Aini, Tegal Being an Event Ambassador for the DeepLearning.AI community provides an opportunity to play a crucial role in bridging the gap between academia, industry, and enthusiasts. It offers me a platform to share the knowledge, experience, and insights I’ve gained from pursuing my Master’s degree in data science. May 12, 2023 Ambassador Spotlight 2023 Pie & AI Ambassador Spotlight: Brennan Pursell, Pennsylvania Andrew Ng persuaded me that AI is the new electricity. We are plunging into the fourth industrial revolution, and as a professional educator I need to prepare my students to contribute to, and compete in, the new economic landscape unfolding before us. As an organization, what DeepLearning.AI cares about most is sharing knowledge about this remarkable technology, which makes it a rare organization in a sector marked by sensational corporate profits and enormous societal costs. May 5, 2023 Ambassador Spotlight 2022 Pie & AI Ambassador Spotlight: David Quintanilla I joined the DeepLearning AI Community because here in El Salvador, there were not many communities that talked about this topic, nor is it taught in our universities. I saw that DeepLearning.AI could help me to spread knowledge and connect with people interested in this field. Oct 18, 2022 Ambassador Spotlight 2022 Pie & AI Ambassador Spotlight: Suang Lim I felt that DeepLearning.AI is a great platform to share the work of Singapore’s AI researchers and students and also to help them connect with the wider global community. Furthermore, with new exciting developments happening almost on a daily basis in the field of AI and Data Science, the DeepLearning.AI community offers a convenient and effective channel for us to learn from each other. Oct 17, 2022 Ambassador Spotlight 2022 Pie & AI Ambassador Spotlight: Arushi Bagchi I realized how deeply ingrained AI is in our everyday lives. Most people may not be aware of that, hence I wanted to bring that awareness and knowledge to them. By becoming an event ambassador, I am able to help spread AI awareness to others, as well as continue to learn from the AI experts who I host for the panel discussions. It amazes me how much people in the larger community are hungry to learn and how much appetite for AI there is out there. Oct 10, 2022 Ambassador Spotlight 2022 Pie & AI Ambassador Spotlight: Patrick Fleith I am a Data Scientist specializing in Space Systems Engineering. Part of my work is done at the European Space Agency. My job is to make space design and operations better with machine learning. For instance, developing anomaly detection systems to alert spacecraft operators about an abnormal satellite behavior. I also work on forecasting models, such as to predict the onboard power consumption, the temperatures of different equipment on the satellite, or to predict the remaining useful life of components. Other applications relate to natural language processing (NLP), such as question answering systems to help space systems engineers or flight control teams to Ambassador - DeepLearning.AI ✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Become an Event Ambassador Why organize a Pie & AI event? As DeepLearning.AI Event ambassadors, you get the opportunity to network with a diverse community of peers and mentors, get discounts on DeepLearning.AI programs for your attendees, and other special perks. Our Event Ambassadors are enthusiastic about helping more cities become Al hubs and inspiring their local community to break into Al. Help build the global Al community by representing your area. Apply Now Your Eligibility You’re passionate about AI, machine learning, and deep learning. You’re currently working in the AI field or looking to transition to an AI career path. You have previous experience with organizing at least one developer-centric event. Your Responsibilities All our Event Ambassadors are volunteers; you’re financially responsible for your event. If you require sponsorship to cover the cost, we recommend you thoroughly vet any potential sponsors to make sure they are appropriate for the event. You’re expected to independently coordinate your virtual / in-person events. You’re expected to host meetups for your group at least once every quarter. Support by DeepLearning.AI Promotion via DeepLearning.AI marketing channels (website, event page, email list, social media, etc.) Discounts on courses for ambassadors and attendees Connection to speakers and the ambassador community A greeting video from Andrew and the DeepLearning.AI team Ongoing support via email Apply Now Courses The Batch Community Careers About Contact Help Terms of Use | Privacy Policy AI Python for Beginners - DeepLearning.AI ✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog ✨ AI Dev x SF 26 Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog ✨ AI Dev x SF 26 Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Overview Course Outline Instructors All Courses Course AI Python for Beginners All Courses Course AI Python for Beginners Course Beginner 10 hours 20 mins AI Python for Beginners Instructor : Andrew Ng DeepLearning.AI Earn a certificate with PRO Enroll for Free Beginner 10 hours 20 mins 35 Video Lessons 27 Code Examples 8 Graded Assignments PRO Earn a certificate with PRO Instructor: Andrew Ng DeepLearning.AI Learn more about Membership PRO Plan Start Learning What you'll learn Learn Python programming fundamentals and how to integrate AI tools for data manipulation, analysis, and visualization. Discover how Python can be applied in various domains such as business, marketing, and journalism to solve real-world problems and enhance efficiency through practical applications. Leverage AI assistants to debug code, explain concepts, and enhance your learning, mirroring real-world software development practices. About this course AI Python for Beginners is designed to help you leverage the power of Python programming, even if your goal isn’t to become a software developer or AI engineer. This four-part course teaches you to code practical AI applications from day one, whether you’re an experienced programmer, or writing “Hello, World!” for the first time. You’ll learn with support from an AI chatbot that can provide you with immediate feedback, answer your questions, quickly identify and work through bugs, and keep you on track while learning new skills. You’ll gain a foundational understanding of Python while using it to build AI-powered tools like custom recipe generators, smart to-do lists, and vacation planners, learning essential programming concepts such as variables, functions, loops, and data structures along the way. As you progress, you’ll work with your own data, extracting valuable insights from text files and structured data, including planning a dream vacation by having AI analyze travel blogs and generate personalized itineraries! By the end of this course series, you’ll be able to write Python scripts that interact with large language models, automate tasks, and analyze your own data. You’ll even learn how to extend Python’s capabilities using popular third-party packages for data analysis and visualization, and how to access real-time information through APIs. These are skills that are increasingly valuable across industries from tech and finance to healthcare and creative fields. Hands-on projects Throughout this course, you’ll engage in practical, hands-on exercises that reinforce your learning and demonstrate the wide range of possibilities for what you can create with Python and AI. Here are some of the projects you’ll work on: Custom Recipe Generator: Create an AI-powered tool that generates unique recipes based on available ingredients. You’ll use variables, f-strings, and AI prompts to craft personalized culinary creations. Smart To-Do List: Build an intelligent task manager that not only stores your to-do items but also prioritizes them using AI. You’ll apply your knowledge of lists, dictionaries, and decision-making code to enhance productivity. Travel Blog Analyzer: Develop a program that reads travel blog entries and uses AI to extract key information like restaurant names and popular dishes. This exercise showcases your ability to work with files and leverage AI for text analysis. Dream Vacation Planner: Create a sophisticated itinerary generator that takes a multi-city trip plan and uses AI to suggest daily activities, including restaurant recommendations. You’ll work with CSV files, dictionaries, and AI prompts to build this comprehensive travel tool. Data Visualization Project: Using popular Python libraries like matplotlib, you’ll create visual representations of data. This could involve plotting price trends of used cars or visualizing travel statistics from your vacation planner. Web Data Extraction: Use the BeautifulSoup library to scrape web pages and extract useful information, opening up a world of data for your projects. Real-time Data Application: Build a program that interacts with web APIs to fetch and process real-time data, such as current weather information or live currency exchange rates. These hands-on projects not only reinforce your Python skills but also demonstrate how AI and programming can be applied to solve real-world problems and enhance everyday tasks. By the end of the course, you’ll have a portfolio of practical AI-powered applications that you’ve built from scratch! You’ll learn directly from Andrew Ng, a globally recognized AI leader known for his engaging teaching style. Andrew has educated around 8 million people worldwide through his online courses, helping them build foundational skills in AI and machine learning. Accelerated learning with AI Experience a new kind of learning with AI chatbot integration. This intelligent assistant helps you write, test, and debug code, providing instant feedback and personalized guidance, making sure you’re never coding alone. With the rise of AI tools, the effort and time required to learn helpful coding skills have significantly decreased, making it more accessible and beneficial for everyone. You’ll be amazed at how quickly you can go from writing your first line of code to creating AI-powered applications that can process and analyze real-world data. Who should join? This course is for anyone curious about AI and programming with Python, from complete beginners learning to code for the first time to professionals seeking to boost productivity and learn how to properly integrate AI into their coding process. Ideal for students, career changers, knowledge workers, lifelong learners, and educators. If traditional coding courses haven’t worked or have felt intimidating, our hands-on, AI-focused approach will help you in your journey. Whether you want to automate repetitive tasks, extract insights from large datasets, or create AI-powered tools to enhance your work or personal projects, this course will give you the skills to get started. By the end, you’ll not only understand Python basics but also know how to leverage powerful data analysis libraries, interact with web APIs, and set up Python on your own computer to continue your learning journey. Skills you will gain Python Programming AI-Assisted Coding Effective LLM Prompts Data Structures Function Creation Variable Management Debugging File Handling API Interaction Course Outline AI Python for Beginners Module 1: Basics of AI Python Coding Introduction Video ・ 3 mins What is computer programming? Video ・ 5 mins Writing code with chatbots Video ・ 6 mins Navigating the learning platform Video ・ 3 mins Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas! Reading ・ 1 min Running your first program Video with Code Example ・ 8 mins How to succeed in coding Video ・ 2 mins Data in Python Video with Code Example ・ 10 mins Combining text and calculations Video with Code Example ・ 7 mins Variables Video with Code Example ・ 7 mins Building LLM prompts with variables Video with Code Example ・ 4 mins Functions: Actions on Data Video with Code Example ・ 7 mins Quiz 1 Graded ・Quiz ・ 10 mins Working with a Virtual Library Graded ・Code Assignment ・ 1 hour 30 mins Lecture Notes M1 Reading ・ 1 min Module 2: Automating Tasks with Python Introduction Video ・ 3 mins Completing a task list with AI Video with Code Example ・ 13 mins Repeating tasks with for loops Video with Code Example ・ 12 mins Prioritizin DeepLearning.AI ✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog ✨ AI Dev x SF 26 Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog ✨ AI Dev x SF 26 Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning AI Courses Grow your AI career with foundational specializations and skill-specific short courses taught by leaders in the field. Most Popular Course DeepLearning.AI AI Prompting for Everyone AI Prompting for Everyone Become an AI power user in this new course taught by Andrew Ng. From finding information to building apps, you'll develop the prompting skills that get real, useful results from today's most powerful AI models. Beginner Prompt Engineering Prompt Engineering Learn More Course DeepLearning.AI Build with Andrew Build with Andrew If you've never written code before, this course is for you. In less than 30 minutes, you'll learn to describe an idea in words and let AI transform it into an app for you. Beginner AI Coding AI Coding Learn More Course DeepLearning.AI Agentic AI Agentic AI In this course taught by Andrew Ng, you'll build agentic AI systems that take action through iterative, multi-step workflows. Intermediate Agents Agents Learn More Previous slide Next slide Course Type Short Course: Quickly learn practical skills and industry tools through hands-on projects. Course: Gain new knowledge on topics in AI with flexible online learning. Earn a shareable certificate. Professional Certificate: Master career skills through long form courses and projects. Earn a shareable certificate. Short Course 97 Course 13 Professional Certificate 10 Level Beginner 63 Intermediate 57 Popular Topics GenAI Applications 57 Prompt Engineering 46 Agents 40 RAG 31 Generative Models 28 LLMOps 26 AI Frameworks 21 Chatbots 21 Search and Retrieval 21 Evaluation and Monitoring 20 NLP 19 Task Automation 19 Embeddings 18 Fine-Tuning 17 Transformers 16 AI Coding 14 Deep Learning 14 Data Processing 13 Vector Databases 13 Document Processing 11 AI in Software Development 10 Machine Learning 9 MultiModal 9 AI Safety 8 Computer Vision 8 Supervised Learning 7 MLOps 5 Data Engineering 4 LLM Serving 4 Compression and Quantization 3 Diffusion Models 3 Anomaly Detection 2 Event-Driven AI 2 Mathematical Foundations 2 On-Device AI 1 Synthetic Data 1 Time Series 1 Unsupervised Learning 1 Collaborator DeepLearning.AI 19 Google Cloud 6 Hugging Face 5 LangChain 5 Anthropic 4 LlamaIndex 4 Meta 4 OpenAI 4 Google 3 Microsoft 3 Snowflake 3 AMD 2 AMD, formerly Lamini 2 Databricks 2 Flower Labs 2 Neo4j 2 Predibase 2 Qdrant 2 crewAI 2 AGI Inc 1 AI Dungeon 1 AI21 labs 1 AWS 1 Arize AI 1 Astronomer 1 Box 1 Chroma 1 CircleCI 1 Cohere 1 Comet 1 CopilotKit 1 CrewAI 1 DotTxt 1 E2B 1 Gemini CLI 1 Giskard 1 GuardrailsAI 1 Haystack 1 IBM Research 1 Jay Alammar, Maarten Grootendorst 1 JetBrains 1 LMSys 1 LandingAI 1 Letta 1 LiveKit 1 Mistral AI 1 MongoDB 1 NexusFlow 1 Nexusflow 1 Nvidia 1 Add filters Course Type Short Course: Quickly learn practical skills and industry tools through hands-on projects. Course: Gain new knowledge on topics in AI with flexible online learning. Earn a shareable certificate. Professional Certificate: Master career skills through long form courses and projects. Earn a shareable certificate. Short Course 97 Course 13 Professional Certificate 10 Level Beginner 63 Intermediate 57 Popular Topics GenAI Applications 57 Prompt Engineering 46 Agents 40 RAG 31 Generative Models 28 LLMOps 26 AI Frameworks 21 Chatbots 21 Search and Retrieval 21 Evaluation and Monitoring 20 NLP 19 Task Automation 19 Embeddings 18 Fine-Tuning 17 Transformers 16 AI Coding 14 Deep Learning 14 Data Processing 13 Vector Databases 13 Document Processing 11 AI in Software Development 10 Machine Learning 9 MultiModal 9 AI Safety 8 Computer Vision 8 Supervised Learning 7 MLOps 5 Data Engineering 4 LLM Serving 4 Compression and Quantization 3 Diffusion Models 3 Anomaly Detection 2 Event-Driven AI 2 Mathematical Foundations 2 On-Device AI 1 Synthetic Data 1 Time Series 1 Unsupervised Learning 1 Collaborator DeepLearning.AI 19 Google Cloud 6 Hugging Face 5 LangChain 5 Anthropic 4 LlamaIndex 4 Meta 4 OpenAI 4 Google 3 Microsoft 3 Snowflake 3 AMD 2 AMD, formerly Lamini 2 Databricks 2 Flower Labs 2 Neo4j 2 Predibase 2 Qdrant 2 crewAI 2 AGI Inc 1 AI Dungeon 1 AI21 labs 1 AWS 1 Arize AI 1 Astronomer 1 Box 1 Chroma 1 CircleCI 1 Cohere 1 Comet 1 CopilotKit 1 CrewAI 1 DotTxt 1 E2B 1 Gemini CLI 1 Giskard 1 GuardrailsAI 1 Haystack 1 IBM Research 1 Jay Alammar, Maarten Grootendorst 1 JetBrains 1 LMSys 1 LandingAI 1 Letta 1 LiveKit 1 Mistral AI 1 MongoDB 1 NexusFlow 1 Nexusflow 1 Nvidia 1 Course Type Short Course: Quickly learn practical skills and industry tools through hands-on projects. Course: Gain new knowledge on topics in AI with flexible online learning. Earn a shareable certificate. Professional Certificate: Master career skills through long form courses and projects. Earn a shareable certificate. Short Course 97 Course 13 Professional Certificate 10 Level Beginner 63 Intermediate 57 Popular Topics GenAI Applications 57 Prompt Engineering 46 Agents 40 RAG 31 Generative Models 28 LLMOps 26 AI Frameworks 21 Chatbots 21 Search and Retrieval 21 Evaluation and Monitoring 20 NLP 19 Task Automation 19 Embeddings 18 Fine-Tuning 17 Transformers 16 AI Coding 14 Deep Learning 14 Data Processing 13 Vector Databases 13 Document Processing 11 AI in Software Development 10 Machine Learning 9 MultiModal 9 AI Safety 8 Computer Vision 8 Supervised Learning 7 MLOps 5 Data Engineering 4 LLM Serving 4 Compression and Quantization 3 Diffusion Models 3 Anomaly Detection 2 Event-Driven AI 2 Mathematical Foundations 2 On-Device AI 1 Synthetic Data 1 Time Series 1 Unsupervised Learning 1 Collaborator DeepLearning.AI 19 Google Cloud 6 Hugging Face 5 LangChain 5 Anthropic 4 LlamaIndex 4 Meta 4 OpenAI 4 Google 3 Microsoft 3 Snowflake 3 AMD 2 AMD, formerly Lamini 2 Databricks 2 Flower Labs 2 Neo4j 2 Predibase 2 Qdrant 2 crewAI 2 AGI Inc 1 AI Dungeon 1 AI21 labs 1 AWS 1 Arize AI 1 Astronomer 1 Box 1 Chroma 1 CircleCI 1 Cohere 1 Comet 1 CopilotKit 1 CrewAI 1 DotTxt 1 E2B 1 Gemini CLI 1 Giskard 1 GuardrailsAI 1 Haystack 1 IBM Research 1 Jay Alammar, Maarten Grootendorst 1 JetBrains 1 LMSys 1 LandingAI 1 Letta 1 LiveKit 1 Mistral AI 1 MongoDB 1 NexusFlow 1 Nexusflow 1 Nvidia 1 Add filters Course Type Short Course: Quickly learn practical skills and industry tools through hands-on projects. Course: Gain new knowledge on topics in AI with flexible online learning. Earn a shareable certificate. Professional Certificate: Master career skills through long form courses and projects. Earn a shareable certificate. Short Course 97 Course 13 Professional Certificate 10 Level Beginner 63 Intermediate 57 Popular Topics GenAI Applications 57 Prompt Engineering 46 Agents 40 RAG 31 Generative Models 28 LLMOps 26 AI Frameworks 21 Chatbots 21 Search and Retrieval 21 Evaluation and Monitoring 20 NLP 19 Task Automation 19 Embeddings 18 Fine-Tuning 17 Transformers 16 AI Coding 14 Deep Learning 14 Data Processing 13 Vector Databases 13 Document Processing 11 AI in Software Development 10 Machine Learning 9 MultiModal 9 AI Safety 8 Computer Vision 8 Supervised Learning 7 MLOps 5 Data Engineering 4 LLM Serving 4 Compression and Quantization 3 Diffusion Models 3 Anomaly Detection 2 Event-Driven AI 2 Mathematical Foundations 2 On-Device AI 1 Synthetic Data 1 Time Series 1 Unsupervised Learning 1 Collaborator DeepLearning.AI 19 Google Cloud 6 Hugging Face 5 LangChain 5 Anthropic 4 LlamaIndex 4 Meta 4 OpenAI 4 Google 3 Microsoft 3 Snowflake 3 AMD 2 AMD, formerly Lamini 2 Databricks 2 Flower Labs 2 Neo4j 2 Predibase 2 Qdrant 2 crewAI 2 AGI Inc 1 AI Dungeon 1 AI21 labs 1 AWS 1 Arize AI 1 Astro Careers - DeepLearning.AI ✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Careers Join the team working to make AI education accessible to the entire world! Curriculum Product Manager Full time Mountain View, CA Product Learn more Finance Director Full time Mountain View, CA Operation Learn more GM of Events, AI Dev Conference Full time Mountain View, CA AI Dev Learn more Visiting Engineer - ContextHub Contract to hire Mountain View, CA Engineering Learn more Courses The Batch Community Careers About Contact Help Terms of Use | Privacy Policy GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, and more... ✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Weekly Issues Andrew's Letters Data Points ML Research Business Science Culture Hardware AI Careers About Subscribe The Batch Weekly Issues issue 350 Published Apr 24, 2026 Reading time 15 min read Published Apr 24, 2026 Reading time 15 min read Share Loading the Elevenlabs Text to Speech AudioNative Player... Dear friends, Coding agents are accelerating different types of software work to different degrees. When we architect teams, understanding these distinctions helps us to have realistic expectations. Listing functions from most accelerated to least, my order is: frontend development, backend, infrastructure, and research. Frontend development  — say, building a web page to serve descriptions of products for an ecommerce site — is dramatically sped up because coding agents are fluent in popular frontend languages like TypeScript and JavaScript and frameworks like React and Angular. Additionally, by examining what they have built by operating a web browser, coding agents are now very good at closing the loop and iterating on their own implementations. Granted, LLMs today are still weak at visual design, but given a design (or if a polished design isn’t important), the implementation is fast! Backend development  — say, building APIs to respond to queries requesting product data — is harder. It takes more work by human developers to steer modern models to think through corner cases that might lead to subtle bugs or security flaws. Further, a backend bug can lead to non-intuitive downstream effects like a corrupted database that occasionally returns incorrect results, which can be harder to debug than a typical frontend bug. Finally, although database migrations can be easier with coding agents, they’re still hard and need to be handled carefully to prevent data loss. While backend development is much faster with coding agents, they accelerate it less, and skilled developers still design and implement far better backends than inexperienced ones who use coding agents. Infrastructure . Agents are even less effective in tasks like scaling an ecommerce site to 10K active uses while maintaining 99.99% reliability. LLMs' knowledge is still relatively limited with respect to infrastructure and the complex tradeoffs good engineers must make, so I rarely trust them for critical infra decisions. Building good infrastructure often requires a period of testing and experimentation, and coding agents can help with that, but ultimately that’s a significant bottleneck where fast AI coding does not help much. Lastly, finding infrastructure bugs — say, a subtle network misconfiguration — can be incredibly difficult and requires deep engineering expertise. Thus, I’ve found that coding agents accelerate critical infrastructure even less than backend development. Research . Coding agents accelerate research work even less. Research involves thinking through new ideas, formulating hypotheses, running experiments, interpreting them to potentially modify the hypotheses, and iterating until we reach conclusions. Coding agents can speed up the pace at which we can write research code. (I also use coding agents to help me orchestrate and keep track of experiments, which makes it easier for a single researcher to manage more experiments.) But there is a lot of work in research other than coding, and today’s agents help with research only marginally. Categorizing software work into frontend, backend, infra, and research is an extreme simplification, but having a simple mental model for how much different tasks have sped up has been useful for how I organize software teams. For example, I now ask front-end teams to implement products dramatically faster than a year ago, but my expectations for research teams have not shifted nearly as much. I am fascinated by how to organize software teams to use coding agents to achieve speed, and will keep sharing my findings in future letters. Keep building! Andrew A MESSAGE FROM DEEPLEARNING.AI In “Building Multimodal Data Pipelines” you’ll learn to build pipelines that handle images, audio, and video end to end. You’ll turn unstructured data into something you can query. Enroll for free News GLM 5.1 Aims for Long-Running Tasks Z.ai updated its flagship open-weights large language model to work autonomously on single tasks for up to eight hours. What’s new:   GLM-5.1  is designed for coding and agentic tasks. Z.ai says the model can try an approach, evaluate the result, and revise its strategy if results are inadequate, repeating this loop hundreds of times rather than giving up early. Input/output:  Text in (up to 200,000 tokens), text out (up to 128,000 tokens) Architecture:  Mixture-of-experts transformer, 754 billion parameters total, 40 billion parameters active per token Features:  Reasoning, function calling, structured output Performance:  Highest-scoring open-weights model on Artificial Analysis Intelligence Index, third on Arena Code leaderboard, led SWE-Bench Pro (in Z.ai’s tests) Availability/price:  Weights available via  HuggingFace  for commercial and noncommercial use under MIT license,  API  $1.40/$0.26/$4.40 per million input/cached/output tokens, coding plans $48.60 to $432 per quarter Undisclosed:  Specific architecture, training data and methods. How it works:  Z.ai has not published a technical report specific to GLM-5.1, which appears to follow  GLM-5 ’s basic architecture, attention mechanism, pretraining, and input/output size limits. The key improvement is sustained productivity in long-running tasks. Where GLM-5 and many other models produce final output within a certain token budget or until they determine that further reasoning won’t change the results, GLM-5.1 cycles through planning, execution, evaluation of intermediate results, and evaluation of its approach until it judges the task to be complete. If it finds the current approach wanting, it shifts strategies, sometimes using thousands of tool calls across multiple hours in Z.ai’s tests. The company said it  optimized  GLM-5.1 for agentic coding but did not specify how. Performance:  GLM-5.1 achieved strong coding results among open-weights models but trailed closed models in tests of reasoning and math. On Artificial Analysis’ Intelligence Index, a composite of 10 tests of economically useful tasks, GLM-5.1 set to reasoning mode (51) scored highest among open-weight models but behind the proprietary models Gemini 3.1 Pro Preview set to reasoning and GPT-5.4 set to xhigh reasoning (tied at 57) as well as Claude Opus 4.6 set to max reasoning (53). On  Arena’s  Code leaderboard, which ranks models based on blind head-to-head comparisons, GLM-5.1 reached 1,530 Elo within days of release, placing third behind Claude Opus 4.6 (1,542 Elo) and Claude Opus 4.6 set to reasoning (1,548 Elo). In Z.ai’s own tests, GLM-5.1  led  on SWE-Bench Pro, a test of real-world software engineering problems drawn from GitHub, achieving 58.4 percent compared to GPT-5.4 (57.7 percent), Claude Opus 4.6 (57.3 percent), and Gemini 3.1 Pro (54.2 percent). On CyberGym, which tests cybersecurity reasoning, GLM-5.1 (68.7) achieved the highest among models tested by Z.ai — prior to the advent of  Claude Mythos  (83.1 as reported by Anthropic) — including Claude Opus 4.6 (66.6) and GPT-5.4 (66.3). Gemini 3.1 Pro and GPT-5.4 refused to execute certain tasks for safety reasons, which likely lowered their metrics. On KernelBench Level 3, which measures how much a model can accelerate machine learning code run About - DeepLearning.AI ✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning About us Our Mission To grow and connect the global AI community DeepLearning.AI is an education technology company that is empowering the global workforce to build an AI-powered future through world-class education, hands-on training, and a collaborative community. Gain world-class education to expand your technical knowledge Get hands-on training to acquire practical skills Learn from a collaborative community of peers and mentors Our Story DeepLearning.AI was founded in 2017 by machine learning and education pioneer Andrew Ng to fill a need for world-class AI education. DeepLearning.AI has created high-quality AI programs on Coursera that have gained an extensive global following. By providing a platform for education and fostering a tight-knit community, DeepLearning.AI has become the pathway for anyone looking to build an AI career. Contact Us Our Ecosystem We’re part of an ecosystem of companies trying to build an AI-powered future through education, business, and more. Workera AI Fund Kira Learning Woebot Health Bearing AI Factored FourthBrain LandingAI Don’t know where to start? Test your current abilities and create your personalized learning pathway to become a machine learning expert. Already an ML practitioner? 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Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog ✨ AI Dev x SF 26 Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog ✨ AI Dev x SF 26 Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Overview Course Outline Instructors All Courses Course AI for Everyone All Courses Course AI for Everyone Course Beginner 6 hours 54 mins AI for Everyone Instructor : Andrew Ng DeepLearning.AI Earn a certificate with PRO Enroll Now Also available on Coursera Beginner 6 hours 54 mins 35 Video Lessons 4 Graded Assignments PRO Earn a certificate with PRO Instructor: Andrew Ng DeepLearning.AI Learn more about Membership PRO Plan Start Learning Also available on Coursera Skills you will gain Workflow of Machine Learning Projects AI Terminology AI Strategy Workflow of Data Science Projects AI is not only for engineers. “AI for Everyone”, a non-technical course, will help you understand AI technologies and spot opportunities to apply AI to problems in your own organization. You will see examples of what today’s AI can – and cannot – do. Finally, you will understand how AI is impacting society and how to navigate through this technological change. If you are a non-technical business professional, “AI for Everyone” will help you understand how to build a sustainable AI strategy. If you are a machine learning engineer or data scientist, this is the course to ask your manager, VP or CEO to take if you want them to understand what you can (and cannot!) do. Course Outline AI for Everyone Week 1: What is AI? What is AI? Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas! Reading ・ 1 min Lecture Notes (Optional) Week 2: Building AI Projects Building AI Projects Lecture Notes (Optional) Week 3: Building AI in Your Company Building AI in Your Company Lecture Notes (Optional) Week 4: AI and Society AI and Society Lecture Notes (Optional) Elevate your learning experience with Pro Upgrade to Pro and gain unlimited accomplishments on your resume Learn More Instructor Andrew Ng Founder, DeepLearning.AI; Co-founder, Coursera Learner reviews Jennifer John “I directly applied the concepts and skills I learned from my courses to an exciting new project at work.” Ujjwal Jain “Courses on Coursera played a major role in my career transition. I learned skills that helped me immensely during my interviews.” Emanuela Manea “The Specialization I completed was an eye-opening learning experience. It changed my perspective on the ways I could help advance my field.” Previous slide Next slide Learner Reviews close Course Slides You can download the annotated version of the course slides below. Download The Course Slides *Note: The slides might not reflect the latest course video slides. Please refer to the lecture videos for the most up-to-date information. We encourage you to make your own notes. Download The Course Slides close Frequently Asked Questions Who is the course for? AI For Everyone is truly a course for everyone. Whether you are an engineer, a CEO, a product manager, a marketer, or just someone who wants to understand the terminology of AI, this course will teach you how to navigate the AI-powered future. What will I learn? You will learn the meaning of basic AI terms including machine learning, data science, and neural networks. You will also learn what ML and DS project workflows look like and how to implement AI projects in your company. Finally, you will learn about AI’s impact on society. Are there any prerequisites? No, you do not need to have any technical or business background prior to taking this course. How do I take the course? You can enroll in AI For Everyone on Coursera’s platform. You will watch videos and complete assignments on Coursera as well. How much does the course cost? The course costs $49 for 180 days of certificate eligibility. Can I apply for financial aid? Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the “Enroll” button on the left. You’ll be prompted to complete an application and will be notified if you are approved. Learn more . Will I receive a certificate at the end of the course? You will receive a certificate if you pay the $49 course price. How long is the course? It typically takes 4 weeks, 2-3 hours per week to complete the course. AI for Everyone Beginner 6 hours 54 mins 35 Video Lessons 4 Graded Assignments PRO Earn a certificate with PRO Instructor: Andrew Ng DeepLearning.AI Learn more about Membership PRO Plan Start Learning Also available on Coursera Sign Up Be notified of new courses Start Learning Also available on Coursera Courses The Batch Community Careers About Contact Help Terms of Use │ Privacy Policy Letters from Andrew Ng | The Batch ✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Weekly Issues Andrew's Letters Data Points ML Research Business Science Culture Hardware AI Careers About Subscribe Letters from Andrew Ng Personal messages to the AI community All Personal Insights Technical Insights Business Insights Tech & Society DeepLearning.AI News AI Careers Just For Fun Learning & Education Letters AI Will Not Destroy the Job Market: Here's why fear of an AI “jobpocaypse” is overblown. There will be no AI jobpocalypse. May 8, 2026 Letters Learn foundational prompting techniques in AI Prompting For Everyone!: Whatever your skill level, AI Prompting For Everyone teaches the best ways to prompt ChatGPT, Claude, Gemini, and other models. The ways we prompt AI are very different in 2026 than 2022 when ChatGPT came out. May 1, 2026 Letters Coding Agents Accelerate Some Software Tasks More Than Others: Knowing how much coding agents accelerate different software-development tasks can help you put together the fastest teams Coding agents are accelerating different types of software work to different degrees. Apr 24, 2026 Letters AI-Native Software Development Needs Generalists: As AI accelerates software development, teammates must play a wider variey of roles AI-native software engineering teams operate very differently than traditional teams. Apr 20, 2026 Letters Open Questions About the Future of Software Engineering: The future of software engineering — the theme of our AI Developer Conference April 28-29 in San Francisco — is in flux. Here are some top open questions. As AI agents accelerate coding, what is the future of software engineering? Apr 10, 2026 Letters Building Voice-Enabled Apps is Easier Than You May Think: Voice-enabled AI applications are improving quickly. Get ready for the next wave of AI! Voice-based AI that you can talk to is improving rapidly, yet most people still don’t appreciate how pervasive voice UIs (user interfaces) will become. Apr 3, 2026 Letters How Anti-AI Propaganda Hurts the Public: The anti-AI coalition is beoming more sophisticated in its efforts to block progress The anti-AI coalition continues to maneuver to find arguments to slow down AI progress. Mar 27, 2026 Letters That Feeling Of Job Insecurity (And What You Can Do About It): In times of economic and technological uncertainty, build on things that tend to remain stable. Community and skills are likely to weather shifts in business and society. I’ve been hearing from people at all levels of seniority about a feeling of job insecurity. Mar 20, 2026 Letters Let’s Help Agents Share Their Work: AI Social Networks Can Be More than Fun and Games Should there be a Stack Overflow for AI coding agents to share their learnings with each other? Mar 16, 2026 Letters Context for Coding Agents: Agentic coding systems often make mistakes because they’re not aware of tools, API calls, and the like that came out after they were trained. Context Hub gives them the documentation they need to write correct code. I’m thrilled to announce Context Hub, a new tool to give to your coding agents the API documentation they need to write correct code. Mar 9, 2026 Letters Introducing the DeepLearning.AI Skill Builder Tool!: Want to strengthen your AI skills? Whether you’re a beginner or an experienced developer, DeepLearning.AI’s free Skill Builder can tell you what to do next. We just released a Skill Builder tool to help you understand in which areas of AI you’re strong, where you can learn more, and what to do next to keep building your skills. Feb 27, 2026 Letters How AI Will Create Jobs: Whenever technology unlocks human creativity, it creates far more jobs than it destroys. AI-driven job growth has already started. Will AI create new job opportunities? My daughter Nova loves cats, and her favorite color is yellow. Feb 20, 2026 Letters Why Hollywood Worries About AI: Filmmakers at At the Sundance Film Festival expressed anxiety about AI. But Hollywood can benefit from AI, and the two communities have interests in common. I recently spoke at the Sundance Film Festival on a panel about AI. Feb 13, 2026 Letters How AI Is Affecting the Job Market — And What You Can Do About It: Worries that AI is taking peoples’ jobs have been overblown, but AI is changing the job market. Here’s what to expect and what employers are looking for. Job seekers in the U.S. and many other nations face a tough environment. Feb 6, 2026 Letters The Rise of Sovereign AI: Nations want to protect their access to AI. Rising interest in sovereign AI may weaken U.S. influence but increase competition and strengthen open source. U.S. policies are driving allies away from using American AI technology. Jan 30, 2026 Page 1 of 23 Older Posts Subscribe to The Batch Stay updated with weekly AI News and Insights delivered to your inbox Courses The Batch Community Careers About Contact Help Terms of Use | Privacy Policy DeepLearning.AI Pro: Become an AI Builder close Try Skill Builder Have a friendly voice chat about how you're using AI, get feedback on your skills, and find out what to learn or build next. Got It Take Me There close Quick Guide & Tips 💻 Accessing Utils File and Helper Functions In each notebook on the top menu: 1: Click on "File" 2: Then, click on "Open" You will be able to see all the notebook files for the lesson, including any helper functions used in the notebook on the left sidebar. 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Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Resources DeepLearning.AI's resource center to help you get started and level up your skills as an AI practitioner or Machine Learning Engineer | eBooks, Guides, Course Slides, AI Notes, and more. Guides eBooks Course Slides AI Notes Other Resources Guides Your Guide to Generative AI Courses Discover the right generative AI short course for your interests with our project-based guide. Build practical skills and learn how to develop at the cutting edge of AI and machine learning. Read more A Complete Guide to Natural Language Processing This comprehensive guide covers multiple questions including; What is Natural Language Processing? Why does NLP matter? What is NLP used for? Top NLP techniques, six important NLP Models and more Read more eBooks How to Build Your Career in AI This book delivers insights from AI pioneer Andrew Ng about learning foundational skills, working on projects, finding jobs, and joining the machine learning community. A practical roadmap to building your career in AI. Download Now Machine Learning Yearning This is an introductory book about developing ML algorithms. You will learn to diagnose errors in an ML project, prioritize the most promising directions, work within complex settings like mismatched training/test sets, and know when and how to apply various techniques. Download Now Course Slides Machine Learning Specialization — Course Slides [Download] the annotated version of the course slides for the popular Machine Learning Specialization by Andrew Ng & Stanford Online Download Now Deep Learning Specialization — Course Slides Download the annotated version of the course slides for Deep Learning Specialization — Our foundational online course by machine learning pioneer Andrew Ng. Download Now Mathematics For Machine Learning & Data Science — Course Slides Download the course slides for the Mathematics For Machine Learning & Data Science Specialization. A specialization that teaches you the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability. Download Now Generative Adversarial Networks (GANs) Specialization — Course Slides Download the course slides for the Generative Adversarial Network (GANs) specialization. A specialization that teaches you image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. Download Now Natural Language Processing (NLP) Specialization — Course Slides Download the course slides for the Natural Language Processing (NLP) specialization. A specialization that teaches you how to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages, summarize text, and even build chatbots. Download Now AI for Everyone — Course Slides Download the course slides for AI for Everyone — a non-technical course that helps you understand AI technologies and spot opportunities to apply AI to problems in your own organization. Download Now AI Notes This is a series of long-form tutorials that supplement what you learned in the Deep Learning Specialization. With interactive visualizations, these tutorials will help you build intuition about foundational deep learning concepts like initializing neural networks and parameter optimization. Get AI Notes Initializing neural networks Initialization can have a significant impact on convergence in training deep neural networks. Simple initialization schemes have been found to accelerate training, but they require some care to avoid common pitfalls. In this post, we'll explain how to initialize neural network parameters effectively. Read more Parameter optimization in neural networks Training a machine learning model is a matter of closing the gap between the model's predictions and the observed training data labels. But optimizing the model parameters isn't so straightforward. Through interactive visualizations, we'll help you develop your intuition for setting up and solving this optimization problem. Read more Other Resources MLOps: From Model-centric to Data-centric AI by Andrew Ng In these slides, Andrew Ng shares the skills he sees as fundamental to the next generation of machine learning practitioners. Download Now Courses The Batch Community Careers About Contact Help Terms of Use | Privacy Policy GPT-5.5 Outperforms (and Hallucinates), Kimi K2.6 Leads Open LLMs, AI Strains Climate Pledges, and more... ✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Weekly Issues Andrew's Letters Data Points ML Research Business Science Culture Hardware AI Careers About Subscribe The Batch Weekly Issues issue 351 Published May 1, 2026 Reading time 16 min read Published May 01, 2026 Reading time 16 min read Share Loading the Elevenlabs Text to Speech AudioNative Player... Dear friends, The ways we prompt AI are very different in 2026 than 2022 when ChatGPT came out. Some people are still using LLMs primarily by asking them short questions. But the models can do much more, like think for minutes, ingest many documents as context, and use web search and other tools. I’m teaching a new course,  AI Prompting for Everyone , to help everyone become an AI power user — whatever their current skill level — and prompt LLMs to take advantage of their latest capabilities. The course covers skills that apply to ChatGPT, Gemini, Claude, and other AI tools: How to use deep research mode for well-researched reports on complex questions. How to give AI the right context, including more documents and images than most people realize they can provide. When to ask AI to think hard for several minutes on important decisions like what car to buy, what to study, or what job to take. How to use AI to generate images, analyze data, and build simple games and websites. I also cover intuitions about how these models work under the hood, so learners know when to trust their output and when not to. Along the way: you’ll see flying squirrels, a creativity test, some of my old family photos, and fireworks. Please join me !  The course assumes no technical background, so please share it with friends or family who could benefit. Keep prompting! Andrew A MESSAGE FROM DEEPLEARNING.AI Learn how to get more accurate answers, better writing, and more useful outputs from AI tools like ChatGPT, Claude, and Gemini. Taught by Andrew Ng, this course covers finding information, brainstorming, and building simple apps.  Enroll today News GPT-5.5 Outperforms, Hallucinates The latest update of OpenAI’s flagship model sets new states of the art in important benchmarks but has difficulty distinguishing between what it does and doesn't know. What’s new:   GPT-5.5  is a closed vision-language models that’s built for agentic coding, computer use, and knowledge work. GPT-5.5 Pro is the same model but processes reasoning tokens in parallel during inference. OpenAI set the API prices at roughly double the per-token rates of GPT-5.4. Input/output:  Text and images in (up to 1 million tokens via API, 400,000 tokens in Codex), text out (up to 128,000 tokens) Features:  Five levels of reasoning (xhigh, high, medium, low, none), tool use, web search, structured outputs, tool search (API only, loads tools on demand rather than all at once), Fast mode (Codex only, generates tokens 1.5 times faster at 2.5 times the price) Performance:  Tops Artificial Analysis Intelligence Index and ARC-AGI-2 Availability/price:  GPT-5.5 available in ChatGPT with Plus, Pro, Business, or Enterprise subscription and in Codex for those tiers plus Edu and Go; GPT-5.5 Pro available in ChatGPT with Pro, Business, or Enterprise subscription: GPT-5.5 API $5/$0.50/$30 per million tokens of input/cached/output, GPT-5.5 Pro API $30/$180 per million tokens of input/output with no cached discount Undisclosed:  Architecture, parameter count, training data and methods How it works:  OpenAI  disclosed  few details about how it built GPT-5.5. As is typical of high-performance models, the training data was a mix of publicly available data scraped from the web, licensed from partners, and collected from users and human trainers. The model was trained via reinforcement learning to reason before responding. Performance:  GPT-5.5 generally delivers top performance in objective benchmarks, especially in tests of knowledge, agentic tasks, and abstract visual reasoning. However, it falls behind competitors on subjective evaluations. It’s also more likely to confidently deliver incorrect output. GPT-5.5 set to xhigh reasoning tops the indepedent Artificial Analysis Intelligence Index, a composite of 10 tests of economically useful tasks, with a score of 60 points. Claude Opus 4.7 set to max reasoning and Gemini 3.1 Pro Preview set to reasoning are tied at 57 points. On ARC-AGI-2, visual puzzles that test abstract reasoning, GPT-5.5 set to xhigh (85.0 percent at $1.87 per task) displaced the previous leader Gemini 3 Deep Think (84.6 percent at $13.62 per task) at a substantially lower cost per task. In OpenAI’s tests, GPT-5.5 set state-of-the-art scores on Terminal-Bench 2.0 (command-line workflows that require planning and tool use), OSWorld-Verified (autonomous operation of real computer interfaces), and Tau2-bench Telecom (multi-turn customer-service workflows). On AA-Omniscience Accuracy, a knowledge benchmark that rewards factual recall, GPT-5.5 set to xhigh reasoning posted the highest accuracy at 57 percent. However, on the AA-Omniscience Index, which rewards models for answering correctly and acknowledging ignorance but penalizes them for confidently making mistakes, GPT-5.5 set to xhigh reasoning (20 points) ranked third, behind Gemini 3.1 Pro Preview (33 points) and Claude Opus 4.7 set to max reasoning (26 points). On Arena.ai’s  leaderboards , which rank models by blind head-to-head comparisons, GPT-5.5 falls well behind competitors. Claude Opus models occupy the top spots across most categories. For instance, as of April 27, GPT-5.5-high ranked seventh in Text Arena and ninth in Code Arena WebDev.  Yes, but:  GPT-5.5 knows more than its peers, but it answers incorrectly more often and acknowledges ignorance less often. The AA-Omniscience benchmark poses 6,000 expert-level questions across business, law, health, humanities, science/engineering, and software engineering. It includes a “hallucination rate” that is the ratio of wrong answers to the sum of wrong answers, partially wrong answers, and abstentions. By this measure, GPT-5.5 set to high reasoning hit 85.53 percent, notably worse than Claude Opus 4.7 set to max reasoning (36.18 percent) and Gemini 3.1 Pro Preview at (49.87 percent). Apollo Research separately  found  that GPT-5.5 lied about completing an impossible programming task in 29 percent of samples, a significant jump from GPT-5.4’s 7 percent. OpenAI’s internal monitoring of coding-agent traffic  showed  a similar pattern. Security implications:  OpenAI released results of VulnLMP, an internal evaluation that  tests  whether a model can develop exploits against widely deployed software. GPT-5.5 undertook multi-day research campaigns and identified potential memory-related vulnerabilities in a variety of targets, but it did not produce an exploit that was confirmed by OpenAI’s evaluation harness. Under OpenAI’s Preparedness Framework, this evidence places GPT-5.5 within the “high” tier of cybersecurity threats, short of the “critical” tier label that would describe models that independently produce working exploits against real targets. Why it matters:  Evaluations of objective performance and human preferences are telling different stories about GPT-5.5. OpenAI regained the lead on the Artificial Analysis Intelligence Index, but the picture flips when it comes to subjective, head-to-head comparisons. Claude Opus models occupy the top spots in LMArena’s Text, Vision, Document, Search, and Code rankings, while GPT-5.5 doesn’t crack the top five on most. Benchmarks measure what models c Apr 24, 2026 | The Batch | AI News & Insights ✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Weekly Issues Andrew's Letters Data Points ML Research Business Science Culture Hardware AI Careers About Subscribe Apr 24, 2026 6 Post s Apr 24, 2026 Assistants That Assist Consistently: Large language models can drift drift from helpful personas to harmful ones, but new research aims to stabilize them Typically, large language models are trained to act as helpful, harmless, honest assistants. However, during long or emotionally charged conversations, traits can emerge that are less beneficial. Researchers devised a way to steady the assistant personas of LLMs. Apr 24, 2026 Apr 24, 2026 Anti-Data-Center Revolt Gains Traction: Public opposition to construction of new data centers in the U.S. has spurred political action and violence Resistance to new data centers is mounting across the United States. Apr 24, 2026 Apr 24, 2026 Humanoid Robots Work Factory Floors: Agiliy Digits humanoid robots fetch and carry bins at a Schaeffler auto-parts factory, displacing humans into higher-level jobs A small number of humanoid robots have made their way into industrial settings, where they’re roughly matching the cost of human labor and propelling some workers into higher-level roles. Apr 24, 2026 Apr 24, 2026 GLM 5.1 Aims for Long-Running Tasks: Z.ai’s GLM 5.1 evaluates interim results and may change its approach hundreds of times before it delivers final output Z.ai updated its flagship open-weights large language model to work autonomously on single tasks for up to eight hours. Apr 24, 2026 Apr 24, 2026 Coding Agents Accelerate Some Software Tasks More Than Others: Knowing how much coding agents accelerate different software-development tasks can help you put together the fastest teams Coding agents are accelerating different types of software work to different degrees. Apr 24, 2026 Apr 24, 2026 GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work The Batch AI News and Insights: Coding agents are accelerating different types of software work to different degrees. Apr 24, 2026 Subscribe to The Batch Stay updated with weekly AI News and Insights delivered to your inbox Courses The Batch Community Careers About Contact Help Terms of Use | Privacy Policy May 01, 2026 | The Batch | AI News & Insights ✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Weekly Issues Andrew's Letters Data Points ML Research Business Science Culture Hardware AI Careers About Subscribe May 01, 2026 6 Post s May 01, 2026 Learn foundational prompting techniques in AI Prompting For Everyone!: Whatever your skill level, AI Prompting For Everyone teaches the best ways to prompt ChatGPT, Claude, Gemini, and other models. The ways we prompt AI are very different in 2026 than 2022 when ChatGPT came out. May 1, 2026 May 01, 2026 Strategic Thinking in LLMs vs. Humans: Researchers at UT-Austin and Google model human decision-making in Rock-Paper-Scissors While large language models can behave in human-like ways, the similarities are superficial. A simple strategy game revealed clear differences in their strategic approaches. May 1, 2026 May 01, 2026 Kimi K2.6 Challenges Open-Weights Champs: Kimi K2.6 matches open Qwen3.6 Max andDeepSeek V4, falls just behind top closed models. Moonshot AI’s updated Kimi model handles longer autonomous coding sessions and scales up its multi-agent orchestration relative to its predecessor. May 1, 2026 May 01, 2026 Big AI’s Plans Strain CO2 Pledges: Tech giants including Alphabet, Amazon, Meta, and Microsoft acknowledge AI’s strain on environment Commitments by large AI companies to limit emissions of greenhouse gases are at risk as those companies pursue a massive build-out of data centers, many of which will be powered by fossil fuels in the near term and possibly beyond. May 1, 2026 May 01, 2026 GPT-5.5 Outperforms, Hallucinates: OpenAI’s latest model tops leaderboards for coding, visual puzzles, and overall intelligence The latest update of OpenAI’s flagship model sets new states of the art in important benchmarks but has difficulty distinguishing between what it does and doesn't know. May 1, 2026 May 01, 2026 GPT-5.5 Outperforms (and Hallucinates), Kimi K2.6 Leads Open LLMs, AI Strains Climate Pledges, Strategic Thinking in LLMs vs. Humans The Batch AI News and Insights: The ways we prompt AI are very different in 2026 than 2022 when ChatGPT came out. May 1, 2026 Subscribe to The Batch Stay updated with weekly AI News and Insights delivered to your inbox Courses The Batch Community Careers About Contact Help Terms of Use | Privacy Policy Contact - DeepLearning.AI ✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Contact us For DeepLearning.AI Pro membership inquiries, please visit our Help Center or create a support ticket . For all other questions, please fill out the form below. Courses The Batch Community Careers About Contact Help Terms of Use | Privacy Policy Generative AI for Everyoneㅤ - DeepLearning.AI ✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog ✨ AI Dev x SF 26 Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog ✨ AI Dev x SF 26 Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Overview Course Outline Instructors All Courses Course Generative AI for Everyoneㅤ All Courses Course Generative AI for Everyoneㅤ Course Beginner 5 hours Generative AI for Everyoneㅤ Instructor : Andrew Ng DeepLearning.AI Earn a certificate with PRO Enroll Now Also available on Coursera Beginner 5 hours 32 Video Lessons 2 Code Examples 6 Graded Assignments PRO Earn a certificate with PRO Instructor: Andrew Ng DeepLearning.AI Learn more about Membership PRO Plan Start Learning Also available on Coursera What you'll learn Learn directly from Andrew Ng about the technology of generative AI, how it works, and what it can (and can’t) do Get an overview of AI tools, and learn from real-world examples of generative AI in use today Understand the impacts of generative AI on business and society to develop effective AI strategies and approaches Instructed by AI pioneer Andrew Ng, Generative AI for Everyone offers his unique perspective on empowering you and your work with generative AI. Andrew will guide you through how generative AI works and what it can (and can’t) do. It includes hands-on exercises where you’ll learn to use generative AI to help in day-to-day work and receive tips on effective prompt engineering, as well as learning how to go beyond prompting for more advanced uses of AI. You’ll delve into real-world applications and learn common use cases, and get hands-on time with generative AI tools to put your knowledge into action, and gain insight into AI’s impact on both business and society. This course was created to ensure everyone can be a participant in our AI-powered future. Skills you will gain Generative AI Tools AI Strategy for Work and Business AI Strategy How Generative AI Works AI Productivity AI Beyond Prompting Instructor Andrew Ng Founder, DeepLearning.AI; Co-founder, Coursera Who is this course for? **Generative AI for Everyone** is for anyone who’s interested in learning about the uses, impacts, and underlying technologies of generative AI, today and in the future. It doesn’t require any coding skills or prior knowledge of AI. For business leaders For professionals For everyone Learner reviews from other DeepLearning.AI courses Selami A. What I loved about the “AI for Everyone” course was the comprehensive coverage of essential AI topics, guided by the expertise of Andrew Ng. The course provided a clear roadmap for initiating and managing AI projects, from project selection to implementation. It also offered insights into building AI teams and introduced the technical tools necessary for AI success View More Chris C. Simple enough to make it easy to understand in spite of being a complex topic, inspiring speaker. Time well spent, and a good fit with “lifelong learning” approach. Adeel B. What stood out to me about this course was the clarity and simplicity with which complex AI concepts were explained. The real-life examples and case studies helped me grasp the practical implications of AI in different sectors. The interactive nature of the course made learning engaging and enjoyable. View More Krystal L. I am an educator and looking to incorporate AI into my career and help my colleagues to do the same. The course did a great job explaining AI concepts to people like myself who are just learning about any of this for the first time. View More John S. I took this course purely out of curiosity. After becoming aware of ChatGPT and Midjourney and then taking a short course on engineering the prompts to get the desired result, I became more intrigued with the topic of AI. I found this most helpful with regards to getting an idea about what AI actually is as opposed to what Hollywood conditioned me to believe it might be. View More Muhammad S. Loved the content. It brought simplicity to the complex topic of AI, separated signal from noise, presented a great flow and covered the most relevant topics. Andrew’s knowledge and passion about the subject of AI was amazing. It was inspiring to listen to him, even via recorded videos. Its really great to be in this era of technology, as it makes it possible to get access to the wealth of knowledge so easily. View More Previous slide Next slide Learner Reviews close Course Outline Generative AI for Everyoneㅤ Week 1: Introduction to Generative AI What is Generative AI? Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas! Reading ・ 1 min Generative AI Applications Resources Lecture Notes (Optional) Week 2: Generative AI Projects Software applications Advanced technologies: Beyond prompting Lecture Notes (Optional) Week 3: Generative AI in Business and Society Generative AI and business Generative AI and society Resources Lecture Notes (Optional) Acknowledgements Elevate your learning experience with Pro Upgrade to Pro and gain unlimited accomplishments on your resume Learn More Course Slides You can download the annotated version of the course slides below. Download The Course Slides *Note: The slides might not reflect the latest course video slides. Please refer to the lecture videos for the most up-to-date information. We encourage you to make your own notes. Download The Course Slides close Frequently Asked Questions How does this course differ from AI for Everyone? The world of AI has changed rapidly in the past 18 months. Large language models (LLMs) and other generative AI applications have taken the world by storm, and continue to evolve. Our new course teaches the most up-to-date information regarding these revolutionary tools and technologies. Who is this course for? This course is designed to provide value to anyone interested in learning about generative AI, its impacts, and how to use it. Business leaders will learn how generative AI can impact business, and how to develop a generative AI strategy to increase productivity, professionals will gain practical experience using AI in the workplace, and AI tools and techniques to apply at work. Developers working outside the world of AI will get a foundational knowledge of the current state of AI that will help in upskilling or reskilling in the realm of AI. AI is a transformative technology that will impact everyone. Understanding how it works and how to use it puts you in the driver’s seat. Do I need prior experience? No, this course doesn’t require any prior knowledge of AI or coding skills. Who is Andrew Ng? Dr. Andrew Ng is a globally recognized leader in AI (Artificial Intelligence). He is the founder of DeepLearning.AI, Founder & CEO of Landing AI, General Partner at AI Fund, Chairman & Co-Founder of Coursera and an Adjunct Professor at Stanford University’s Computer Science Department. What exercises will be done in this course? Learn how to utilize prompt engineering with the GPT-3.5 language model to classify and summarize restaurant reviews efficiently. How long does the course take to complete? 3 weeks of study, 1-2 hours/week Generative AI for Everyoneㅤ Beginner 5 hours 32 Video Lessons 2 Code Examples 6 Graded Assignments PRO Earn a certificate with PRO Instructor: Andrew Ng DeepLearning.AI Learn more about Membership PRO Plan Start Learning Also available on Coursera Stay up to date with AI news! Keep learning with updates on curated news, courses, and events, as well as Andrew’s thoughts from DeepLearning.AI! Start Learning Also available on Coursera Courses The Batch Community Careers About Contact Help Terms of Use │ Privacy Policy Privacy Policy - DeepLearning.AI ✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning PRIVACY POLICY Last updated: April 21, 2023 This Privacy Policy describes Our policies and procedures on the collection, use and disclosure of Your information when You use the Service and tells You about Your privacy rights and how the law protects You. We use Your Personal data to provide and improve the Service. By using the Service, You agree to the collection and use of information in accordance with this Privacy Policy. 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Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog ✨ AI Dev x SF 26 Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog ✨ AI Dev x SF 26 Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Overview Course Outline Instructors All Courses Short Course Multi AI Agent Systems with crewAI All Courses Short Course Multi AI Agent Systems with crewAI Short Course Beginner 2 hours 41 mins Multi AI Agent Systems with crewAI Instructor : João Moura Earn an accomplishment with PRO Enroll for Free Beginner 2 hours 41 mins 18 Video Lessons 7 Code Examples 1 Graded Assignment PRO Earn an accomplishment with PRO Instructor: João Moura crewAI Learn more about Membership PRO Plan Start Learning What you'll learn Exceed the performance of prompting a single LLM by designing and prompting a team of AI agents through natural language. Use an open source library, crewAI, to automate repeatable, multi-step tasks like tailoring a resume to a job description; and automate business processes that are typically done by a group of people, like event planning. By creating a team of AI agents, you can define a specific role, goal, and backstory for each agent, which breaks down complex multi-step tasks and assigns them to agents that are customized to perform those tasks. About this course Learn key principles of designing effective AI agents, and organizing a team of AI agents to perform complex, multi-step tasks. Apply these concepts to automate 6 common business processes. Learn from João Moura, founder and CEO of crewAI, and explore key components of multi-agent systems: Role-playing: Assign specialized roles to agents Memory: Provide agents with short-term, long-term, and shared memory Tools: Assign pre-built and custom tools to each agent (e.g. for web search) Focus: Break down the tasks, goals, and tools and assign to multiple AI agents for better performance Guardrails: Effectively handle errors, hallucinations, and infinite loops Cooperation: Perform tasks in series, in parallel, and hierarchically Throughout the course, you’ll work with crewAI, an open source library designed for building multi-agent systems. You’ll learn to build agent crews that execute common business processes, such as: Tailor resumes and interview prep for job applications Research, write and edit technical articles Automate customer support inquiries Conduct customer outreach campaigns Plan and execute events Perform financial analysis By the end of the course, you will have designed several multi-agent systems to assist you in common business processes, and also studied the key principles of AI agent systems. Who should join? If you’ve taken some prompt engineering courses, have some familiarity with basic coding, and want to incorporate LLMs in your professional work, then this course is designed for you! Course Outline 18 Lessons・ 7 Code Examples Introduction Video ・ 3 mins Overview Video ・ 11 mins AI Agents Video ・ 8 mins Create agents to research and write an article Video with Code Example ・ 15 mins Key elements of AI agents Video ・ 11 mins Multi agent customer support automation Video with Code Example ・ 18 mins Mental framework for agent creation Video ・ 3 mins Key elements of agent tools Video ・ 7 mins Tools for a customer outreach campaign Video with Code Example ・ 16 mins Recap of tools Video ・ 1 min Key elements of well defined tasks Video ・ 4 mins Automate event planning Video with Code Example ・ 15 mins Recap on tasks Video ・ 1 min Multi agent collaboration Video ・ 5 mins Mutli agent collaboration for financial analysis Video with Code Example ・ 12 mins Build a crew to tailor job applications Video with Code Example ・ 17 mins Next steps with AI agent systems Video ・ 5 mins Conclusion Video ・ 1 min Quiz Graded ・Quiz ・ 10 mins Next Step: Build Advanced Multi AI Agent Code Example ・ 10 mins Elevate your learning experience with Pro Upgrade to Pro and gain unlimited accomplishments on your resume Learn More Instructor João Moura Founder and CEO of CrewAI Multi AI Agent Systems with crewAI Beginner 2 hours 41 mins 18 Video Lessons 7 Code Examples 1 Graded Assignment PRO Earn an accomplishment with PRO Instructor: João Moura crewAI Learn more about Membership PRO Plan Start Learning Course access is free for a limited time during the DeepLearning.AI learning platform beta! Enroll for Free Want to learn more about Generative AI? Keep learning with updates on curated AI news, courses, and events, as well as Andrew’s thoughts from DeepLearning.AI! Start Learning Courses The Batch Community Careers About Contact Help Terms of Use │ Privacy Policy Natural Language Processing (NLP) [A Complete Guide] ✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning A Complete Guide to Natural Language Processing Last updated on Jan 11, 2023 Table of Contents Relevant Courses Natural Language Processing Specialization Machine Learning Specialization Deep Learning Specialization Introduction Natural Language Processing (NLP) is one of the hottest areas of artificial intelligence (AI) thanks to applications like text generators that compose coherent essays, chatbots that fool people into thinking they’re sentient, and text-to-image programs that produce photorealistic images of anything you can describe. Recent years have brought a revolution in the ability of computers to understand human languages, programming languages, and even biological and chemical sequences, such as DNA and protein structures, that resemble language. The latest AI models are unlocking these areas to analyze the meanings of input text and generate meaningful, expressive output. What is Natural Language Processing (NLP) Natural language processing (NLP) is the discipline of building machines that can manipulate human language — or data that resembles human language — in the way that it is written, spoken, and organized. It evolved from computational linguistics, which uses computer science to understand the principles of language, but rather than developing theoretical frameworks, NLP is an engineering discipline that seeks to build technology to accomplish useful tasks. NLP can be divided into two overlapping subfields: natural language understanding (NLU), which focuses on semantic analysis or determining the intended meaning of text, and natural language generation (NLG), which focuses on text generation by a machine. NLP is separate from — but often used in conjunction with — speech recognition, which seeks to parse spoken language into words, turning sound into text and vice versa. Why Does Natural Language Processing (NLP) Matter? NLP is an integral part of everyday life and becoming more so as language technology is applied to diverse fields like retailing (for instance, in customer service chatbots) and medicine (interpreting or summarizing electronic health records). Conversational agents such as Amazon’s Alexa and Apple’s Siri utilize NLP to listen to user queries and find answers. The most sophisticated such agents — such as GPT-3, which was recently opened for commercial applications — can generate sophisticated prose on a wide variety of topics as well as power chatbots that are capable of holding coherent conversations. Google uses NLP to improve its search engine results , and social networks like Facebook use it to detect and filter hate speech .  NLP is growing increasingly sophisticated, yet much work remains to be done. Current systems are prone to bias and incoherence, and occasionally behave erratically. Despite the challenges, machine learning engineers have many opportunities to apply NLP in ways that are ever more central to a functioning society. What is Natural Language Processing (NLP) Used For? NLP is used for a wide variety of language-related tasks, including answering questions, classifying text in a variety of ways, and conversing with users.  Here are 11 tasks that can be solved by NLP: Sentiment analysis is the process of classifying the emotional intent of text. Generally, the input to a sentiment classification model is a piece of text, and the output is the probability that the sentiment expressed is positive, negative, or neutral. Typically, this probability is based on either hand-generated features, word n-grams, TF-IDF features, or using deep learning models to capture sequential long- and short-term dependencies. Sentiment analysis is used to classify customer reviews on various online platforms as well as for niche applications like identifying signs of mental illness in online comments. Toxicity classification is a branch of sentiment analysis where the aim is not just to classify hostile intent but also to classify particular categories such as threats, insults, obscenities, and hatred towards certain identities. The input to such a model is text, and the output is generally the probability of each class of toxicity. Toxicity classification models can be used to moderate and improve online conversations by silencing offensive comments , detecting hate speech , or scanning documents for defamation .  Machine translation automates translation between different languages. The input to such a model is text in a specified source language, and the output is the text in a specified target language. Google Translate is perhaps the most famous mainstream application. Such models are used to improve communication between people on social-media platforms such as Facebook or Skype. Effective approaches to machine translation can distinguish between words with similar meanings . Some systems also perform language identification; that is, classifying text as being in one language or another.  Named entity recognition aims to extract entities in a piece of text into predefined categories such as personal names, organizations, locations, and quantities. The input to such a model is generally text, and the output is the various named entities along with their start and end positions. Named entity recognition is useful in applications such as summarizing news articles and combating disinformation . For example, here is what a named entity recognition model could provide:  Spam detection is a prevalent binary classification problem in NLP, where the purpose is to classify emails as either spam or not. Spam detectors take as input an email text along with various other subtexts like title and sender’s name. They aim to output the probability that the mail is spam. Email providers like Gmail use such models to provide a better user experience by detecting unsolicited and unwanted emails and moving them to a designated spam folder.  Grammatical error correction models encode grammatical rules to correct the grammar within text. This is viewed mainly as a sequence-to-sequence task, where a model is trained on an ungrammatical sentence as input and a correct sentence as output. Online grammar checkers like Grammarly and word-processing systems like Microsoft Word use such systems to provide a better writing experience to their customers. Schools also use them to grade student essays .  Topic modeling is an unsupervised text mining task that takes a corpus of documents and discovers abstract topics within that corpus. The input to a topic model is a collection of documents, and the output is a list of topics that defines words for each topic as well as assignment proportions of each topic in a document. Latent Dirichlet Allocation (LDA), one of the most popular topic modeling techniques, tries to view a document as a collection of topics and a topic as a collection of words. Topic modeling is being used commercially to help lawyers find evidence in legal documents .  Text generation , more formally known as natural language generation (NLG), produces text that’s similar to human-written text. Such models can be fine-tuned to produce text in different genres and formats — including tweets, blogs, and even computer code . Text generation has been performed using Markov processes , LSTMs , BERT , GPT-2 , LaMDA , and other approaches. It’s particularly useful for autocomplete and chatbots. Autocomplete predicts what word comes next, and autocomplete systems of varying complexity are used in chat applications like WhatsApp. Google uses autocomplete to predict search queries. One of the most famous models for autocomplete is GPT-2, which has been used to write articles , song lyrics , and much more.&nb Machine Learning Research | The Batch ✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Weekly Issues Andrew's Letters Data Points ML Research Business Science Culture Hardware AI Careers About Subscribe Machine Learning Research 579 Post s Machine Learning Research Robots That Adapt to New Tasks: Sony and university researchers train robots to learn without catastrophic forgetting Neural networks can forget how to perform earlier tasks as they learn new ones. May 8, 2026 Machine Learning Research How Nvidia Uses AI to Design Chips: Chipmaker's models design circuits, verify designs, and test new layouts Nvidia’s chief scientist dreams of telling an AI model to design a new GPU, then skiing for a couple days while the system does the job. May 8, 2026 Machine Learning Research ByteDance Bids for Video Leadership: ByteDance adds state-of-the-art Seedance 2.0 video to Capcut, while OpenAI retreats As OpenAI prepares to shut down Sora, ByteDance made its own video generation model available to hundreds of millions of users. May 8, 2026 Machine Learning Research Strategic Thinking in LLMs vs. Humans: Researchers at UT-Austin and Google model human decision-making in Rock-Paper-Scissors While large language models can behave in human-like ways, the similarities are superficial. A simple strategy game revealed clear differences in their strategic approaches. May 1, 2026 Machine Learning Research Kimi K2.6 Challenges Open-Weights Champs: Kimi K2.6 matches open Qwen3.6 Max andDeepSeek V4, falls just behind top closed models. Moonshot AI’s updated Kimi model handles longer autonomous coding sessions and scales up its multi-agent orchestration relative to its predecessor. May 1, 2026 Machine Learning Research GPT-5.5 Outperforms, Hallucinates: OpenAI’s latest model tops leaderboards for coding, visual puzzles, and overall intelligence The latest update of OpenAI’s flagship model sets new states of the art in important benchmarks but has difficulty distinguishing between what it does and doesn't know. May 1, 2026 Machine Learning Research Assistants That Assist Consistently: Large language models can drift drift from helpful personas to harmful ones, but new research aims to stabilize them Typically, large language models are trained to act as helpful, harmless, honest assistants. However, during long or emotionally charged conversations, traits can emerge that are less beneficial. Researchers devised a way to steady the assistant personas of LLMs. Apr 24, 2026 Machine Learning Research Humanoid Robots Work Factory Floors: Agiliy Digits humanoid robots fetch and carry bins at a Schaeffler auto-parts factory, displacing humans into higher-level jobs A small number of humanoid robots have made their way into industrial settings, where they’re roughly matching the cost of human labor and propelling some workers into higher-level roles. Apr 24, 2026 Machine Learning Research GLM 5.1 Aims for Long-Running Tasks: Z.ai’s GLM 5.1 evaluates interim results and may change its approach hundreds of times before it delivers final output Z.ai updated its flagship open-weights large language model to work autonomously on single tasks for up to eight hours. Apr 24, 2026 Machine Learning Research Simulating Diverse Human Cohorts: Persona generation simulates human characters across a controllable range of points of view If you want to understand how the public will respond to your offerings, large language models can simulate users who answer questions about capabilities, features, promotions, or prices. Apr 17, 2026 Machine Learning Research US States Move Forward With AI Laws: Most states are regulating AI despite President Trump’s opposition to state-level laws U.S. states are continuing to enact laws that regulate AI, despite President Trump’s efforts to discourage state-by-state legislation in favor of national laws. Apr 17, 2026 Machine Learning Research Big Pharma Bets Big on AI: Pharmaceutical kingpin Eli Lilly gave Insilico $2.75 billion for AI-driven drug development Generative AI has proven that it can produce text, images, audio, video, and code. The world’s most valuable pharmaceutical company is betting billions that it can produce drugs as well. Apr 17, 2026 Machine Learning Research Life After Llama: With Muse Spark, Meta pivots away from its open-weights Llama strategy Meta pivoted from its open-weights strategy to deliver a closed alternative. Apr 17, 2026 Machine Learning Research How Liquids and Gases Behave: A dynamic fluids model appears to solve transformers’ pixellation problem Simulating complex physical systems through traditional numerical methods is slow and expensive, and simulations based on machine learning are usually specialized for a specific type of system, such as water in a pipe or atmosphere surrounding a planet. Apr 10, 2026 Machine Learning Research Dark DNA Unveiled: Google’s AlphaGenome interprets DNA that regulates genetic expression An open-weights model could help scientists compare the impact of genetic variations, identify mutations that cause diseases, and develop treatments. 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May 8, 2026 Subscribe to The Batch Stay updated with weekly AI News and Insights delivered to your inbox Courses The Batch Community Careers About Contact Help Terms of Use | Privacy Policy Events - DeepLearning.AI ✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning 50+ countries, 700+ events, 70K+ participants Participate in DeepLearning.AI events for the opportunity to network with a diverse community of peers, learn best practices from industry leaders, get advice and hands-on practice from mentors, benefit from thought-provoking discussions, and more! 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Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Weekly Issues Andrew's Letters Data Points ML Research Business Science Culture Hardware AI Careers About Subscribe Data Points Your accelerated guide to AI news Subscribe First Name Last Name Where do you live? Select a country What is your role? Please select Keep me updated on the latest news, events, and courses Subscribe Data Points How Anthropic aligns its models: OpenAI improves realtime audio capabilities Hermes, aka the new OpenClaw. DCI, aka the new RAG. NLAs, or how to peek inside Claude. TokenSpeed, aka the new TensorRT. May 11, 2026 Data Points US government to vet new models: ChatGPT gets a model refresh Anthropic’s latest products for banking and finance. Drug manufacturers get AI gains they didn’t expect. US military augments Anthropic with new partners. Google DeepMind’s UK unionization vote. May 6, 2026 Data Points China shuts down Manus acquisition: All the details from Microsoft’s new deal with OpenAI Nvidia’s laptop-sized omnimodal model. Grok cuts prices, boosts context. OpenAI’s AGI principles. IBM’s latest suite of open Granite models. May 4, 2026 Data Points OpenAI and Microsoft sever their exclusive relationship: DeepSeek V4 makes a smaller splash Google’s and Amazon’s huge bets on Anthropic. Google employees’ resistance to secret government use of AI. The U.S. threat to punish Chinese companies that distill U.S. AI models. An agentic system that autonomously designed a working CPU. Apr 29, 2026 Data Points Insurance companies carve out exceptions for AI risks: U.S. military re-engages with Anthropic A San Francisco retail store run by an AI agent. OpenAI’s specialized life-sciences model. Anthropic’s designs on the market for graphic design. Wall Street’s AI-driven workforce adjustments. Apr 28, 2026 Data Points Hackers break into Claude Mythos: OpenAI launches a security-specialized competitor to Mythos Maine votes to pause construction of data centers. Journalists at major U.S. newspapers push back on publisher’s demand to use AI. AI-generated talking-head videos that support President Trump flood social media. Humanoid robots outrun human world record in a half-marathon race. Apr 22, 2026 Data Points Anthropic’s claims for Claude Mythos raise questions: 40 percent of U.S. data-center projects may be delayed OpenAI’s big bet on an alternative to Nvidia. Meta’s plan to build a virtual CEO. Financial risks of lending money to companies affected by AI. Luma’s AI-driven film production studio. Apr 20, 2026 Data Points Nvidia’s open AI models go quantum: Suspect arrested for attack on OpenAI HQ and CEO’s home Muse Spark, Meta’s model for AI glasses, social media, and more. Rubber Duck, Copilot’s tool to make GPT-5.4 check Claude’s work. Claude Managed Agents, a built-in harness for enterprise tools. GPT-5.4-Cyber, OpenAI’s new not-for-public cybersecurity model. Apr 15, 2026 Data Points China’s showdown with NeurIPS conference: Claude’s emotion vectors aren’t feelings, but affect behavior Google’s updates to its open model family. Cursor 3, a brand-new interface built for agents. Microsoft AI’s latest speech and image models. Anthropic’s clampdown on OpenClaw. Apr 6, 2026 Data Points Sora no more; OpenAI shuts down video maker: Tencent introduces ClawBot, an OpenClaw wrapper for WeChat Anthropic’s multi-agent harness design. A new voice agent benchmark. Arm’s chip design for data center CPU. U.S. White House’s AI proposal to Congress. Mar 25, 2026 Data Points MiniMax’s M2.7 model competes with Gemini and Opus: Cursor’s Composer 2 does the same, but for code Microsoft’s latest image model. Google’s Stitch, a platform for designers. Claude Code Channels, another step towards OpenClaw parity. Anthropic’s denial of a back door into defense operations. Mar 23, 2026 Data Points Nvidia’s enterprise-focused NemoClaw gives OpenClaw a security boost: Claude Dispatch lets paid users authorize remote work GPT-5.4 Mini and Nano, optimized for speed. Nvidia’s own tiny-sized open-weights model. Midjourney updates to v8 in Alpha. Mamba releases third version of its state space language model. Mar 18, 2026 Data Points Perplexity Computer hits desktop, mobile, Pro: Google releases Aletheia, its top natural language math agent DeepSeek testing favors local chipmakers. Nvidia’s reasoning-added retrieval method. Why OpenAI and Oracle shut down Stargate expansion. Meta’s giant deal with AI cloud provider Nebius. Mar 16, 2026 Data Points DeerFlow 2.0 puts new spin on Claw-like agents: Anthropic sues the U.S., which shows no signs of backing down Meta’s purchase of Moltbook, a social network for AI agents. Iran’s attacks on data centers in the UAE and elsewhere. Google’s new multimodal embedding model. Grammarly’s AI ventriloquism using famous writers. Mar 11, 2026 Data Points GPT-5.4 Pro challenges Gemini 3.1 Pro Preview: Luma’s new state-of-the-art image editor/interpreter Microsoft’s new open-weights vision reasoning model. Yuan 3.0 Ultra, a document-retrieving juggernaut. OpenAI’s hardware leader’s resignation. Black Forest’s new training method for image models. Mar 9, 2026 Load More Subscribe to Data Points Your accelerated guide to AI news and research Courses The Batch Community Careers About Contact Help Terms of Use | Privacy Policy ChatGPT Prompt Engineering for Developers - DeepLearning.AI ✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog ✨ AI Dev x SF 26 Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog ✨ AI Dev x SF 26 Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Overview Course Outline Instructors All Courses Short Course ChatGPT Prompt Engineering for Developers All Courses Short Course ChatGPT Prompt Engineering for Developers Short Course Beginner 1 hour 30 mins ChatGPT Prompt Engineering for Developers Instructors : Isa Fulford, Andrew Ng Earn an accomplishment with PRO Learn for Free Beginner 1 hour 30 mins 9 Video Lessons 7 Code Examples 1 Graded Assignment PRO Earn an accomplishment with PRO Instructors: Isa Fulford, Andrew Ng OpenAI Learn more about Membership PRO Plan Start Learning What you'll learn Learn prompt engineering best practices for application development Discover new ways to use LLMs, including how to build your own custom chatbot Gain hands-on practice writing and iterating on prompts yourself using the OpenAI API About this course In ChatGPT Prompt Engineering for Developers, you will learn how to use a large language model (LLM) to quickly build new and powerful applications. Using the OpenAI API, you’ll be able to quickly build capabilities that learn to innovate and create value in ways that were cost-prohibitive, highly technical, or simply impossible before now. This short course taught by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI) will describe how LLMs work, provide best practices for prompt engineering, and show how LLM APIs can be used in applications for a variety of tasks, including: Summarizing (e.g., summarizing user reviews for brevity) Inferring (e.g., sentiment classification, topic extraction) Transforming text (e.g., translation, spelling & grammar correction) Expanding (e.g., automatically writing emails) In addition, you’ll learn two key principles for writing effective prompts, how to systematically engineer good prompts, and also learn to build a custom chatbot. All concepts are illustrated with numerous examples, which you can play with directly in our Jupyter notebook environment to get hands-on experience with prompt engineering In partnership with OpenAI We are excited to collaborate with OpenAI in offering this course, designed to help developers effectively utilize LLMs. This course reflects the latest understanding of best practices for using prompts for the latest LLM models. Who should join? ChatGPT Prompt Engineering for Developers is beginner-friendly. Only a basic understanding of Python is needed. But it is also suitable for advanced machine learning engineers wanting to approach the cutting-edge of prompt engineering and use LLMs. Course Outline 9 Lessons・ 7 Code Examples Introduction Video ・ 6 mins Guidelines Video with Code Example ・ 17 mins Iterative Video with Code Example ・ 13 mins Summarizing Video with Code Example ・ 7 mins Inferring Video with Code Example ・ 11 mins Transforming Video with Code Example ・ 12 mins Expanding Video with Code Example ・ 6 mins Chatbot Video with Code Example ・ 12 mins Conclusion Video ・ 2 mins Quiz Graded ・Quiz ・ 10 mins Elevate your learning experience with Pro Upgrade to Pro and gain unlimited accomplishments on your resume Learn More Instructors Isa Fulford Member of Technical Staff, OpenAI Andrew Ng Founder, DeepLearning.AI; Co-founder, Coursera Generative AI offers many opportunities for AI engineers to build, in minutes or hours, powerful applications that previously would have taken days or weeks. I’m excited about sharing these best practices to enable many more people to take advantage of these revolutionary new capabilities. – Andrew Ng ChatGPT Prompt Engineering for Developers Beginner 1 hour 30 mins 9 Video Lessons 7 Code Examples 1 Graded Assignment PRO Earn an accomplishment with PRO Instructors: Isa Fulford, Andrew Ng OpenAI Learn more about Membership PRO Plan Start Learning Course access is free for a limited time during the DeepLearning.AI learning platform beta! Learn for Free Want to learn more about Generative AI? Keep learning with updates on curated AI news, courses, events, as well as Andrew’s thoughts from DeepLearning.AI! Start Learning Courses The Batch Community Careers About Contact Help Terms of Use │ Privacy Policy Terms of Use - DeepLearning.AI ✨ New course! 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Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning The DeepLearning.AI Community The DeepLearning.AI community is dedicated to providing the most educational resource of the AI industry. Our community stands out by encouraging AI engineers and professionals to not only learn, but also build their own AI projects and products. Our members regularly showcase their innovative AI projects, ask questions for help, and provide feedback across the community. Our community forum is a lively space where members can discuss their learnings, seek advice, meet each other, and share insights on everything AI. We also have a team of enthusiastic mentors who are industry experts and specialists, guiding learners throughout their AI journey. Join the DeepLearning.AI Community today and let’s build the future of AI, together! 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Courses The Batch Community Careers About Contact Help Terms of Use | Privacy Policy The Batch | DeepLearning.AI | AI News & Insights ✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning What Matters in AI Right Now Subscribe First Name Last Name Where do you live? Select a country What is your role? Please select Keep me updated on the latest news, events, and courses Subscribe 🗞️ Stay updated with weekly AI News and Insights delivered to your inbox Weekly Issues Andrew's Letters Data Points ML Research Business Science Culture Hardware AI Careers About May 08, 2026 Seedance Makes A Splash, Nvidia's AI-Guided Chip Designs, Helping Robots Not Forget The Batch AI News and Insights: There will be no AI jobpocalypse. Read more Popular Articles Introducing DeepLearning.AI Pro Oct 31, 2025 1 min read Reinforcement Learning Heats Up, White House Orders Muscular AI Policy, Computer Use Gains Momentum, Fine Control of Fine-Tuning Jan 29, 2025 13 min read DeepSeek Sharpens Its Reasoning: DeepSeek-R1, an affordable rival to OpenAI’s o1 Jan 22, 2025 4 min read May 01, 2026 GPT-5.5 Outperforms (and Hallucinates), Kimi K2.6 Leads Open LLMs, AI Strains Climate Pledges, Strategic Thinking in LLMs vs. Humans The Batch AI News and Insights: The ways we prompt AI are very different in 2026 than 2022 when ChatGPT came out. Apr 24, 2026 GLM 5.1 Thinks Strategically, Data-Center Revolt Intensifies, When Helpful LLMs Turn Unhelpful, Humanoid Robots Get to Work The Batch AI News and Insights: Coding agents are accelerating different types of software work to different degrees. Apr 17, 2026 Meta Pivots From Open Weights, Big Pharma Bets On AI, Regulatory Patchwork, Simulating Human Cohorts The Batch AI News and Insights: AI-native software engineering teams operate very differently than traditional teams. Apr 10, 2026 Anthropic’s Claude Mythos Problem, Dark DNA Unveiled, Pitfalls for Assistive Models, Simulating Fluid Dynamics The Batch AI News and Insights: As AI agents accelerate coding, what is the future of software engineering? Apr 03, 2026 Claude Code’s Source Leaks, OpenAI Exits Video Generation, Gemini Adds Music Generation, LLMs Learn at Inference The Batch AI News and Insights: Voice-based AI that you can talk to is improving rapidly, yet most people still don’t appreciate how pervasive voice UIs (user interfaces) will become. Mar 27, 2026 Nvidia’s Open Salvo, OpenAI’s Amazon Deal, Grok Cuts Video Prices, Recursive Language Models The Batch AI News and Insights: The anti-AI coalition continues to maneuver to find arguments to slow down AI progress. Mar 20, 2026 Attacks On Data Centers, Qwen3.5 In All Sizes, DeepSeek’s Huawei Play, Apple’s Multimodal Tokenizer The Batch AI News and Insights: I’ve been hearing from people at all levels of seniority about a feeling of job insecurity. Mar 13, 2026 GPT-5.4 Makes A Splash, AI’s Growth on Mobile, Data Centers Go Off-Grid, Apple’s Diffusion Research The Batch AI News and Insights: Should there be a Stack Overflow for AI coding agents to share their learnings with each other? Mar 06, 2026 Anthropic vs. the U.S. Government, Nano Banana’s Makeover, Frontier Agent Management, Google’s Mathematics Solutions The Batch AI News and Insights: I’m thrilled to announce Context Hub, a new tool to give to your coding agents the API documentation they need to write correct code. Feb 27, 2026 Gemini Seizes the Lead, Investors Panic Over Agentic AI, Optimism at Global AI Summit, Local Versus Cloud The Batch AI News and Insights: We just released a Skill Builder tool to help you understand in which areas of AI you’re strong, where you can learn more, and what to do next to keep building your skills. Feb 20, 2026 The New Open-Weights Leader, Big AI’s Political Influence, Predicting Illness, Faster Reasoning The Batch AI News and Insights: Will AI create new job opportunities? My daughter Nova loves cats, and her favorite color is yellow. Feb 13, 2026 Claude Opus 4.6 Thinks Smarter, xAI Joins SpaceX, AI Outperforms Doctors, Standardized AI Audits The Batch AI News and Insights: I recently spoke at the Sundance Film Festival on a panel about AI. Feb 06, 2026 OpenClaw Runs Amok, Kimi’s Open Model, Ministral Distilled, Wikipedia’s Partners The Batch AI News and Insights: Job seekers in the U.S. and many other nations face a tough environment. Jan 30, 2026 Agents Go Shopping, Intelligence Redefined, Better Text in Pictures, Higher Engagement Means Worse Alignment The Batch AI News and Insights: U.S. policies are driving allies away from using American AI technology. Jan 23, 2026 Self-Driving Reasoning Models, ChatGPT Adds Ads, Apple’s Deal with Google, 3D Generation Pronto The Batch AI News and Insights: How can businesses go beyond using AI for incremental efficiency gains to create transformative impact? Page 1 of 25 Older Posts Subscribe to The Batch Stay updated with weekly AI News and Insights delivered to your inbox Courses The Batch Community Careers About Contact Help Terms of Use | Privacy Policy DeepLearning.AI ✨ New course! Enroll in Transformers in Practice Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog ✨ AI Dev x SF 26 Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning Explore Courses AI Newsletter The Batch Andrew's Letter Data Points ML Research Blog ✨ AI Dev x SF 26 Community Forum Events Ambassadors Ambassador Spotlight Resources Membership Start Learning AI Courses Grow your AI career with foundational specializations and skill-specific short courses taught by leaders in the field. Most Popular Course DeepLearning.AI AI Prompting for Everyone AI Prompting for Everyone Become an AI power user in this new course taught by Andrew Ng. From finding information to building apps, you'll develop the prompting skills that get real, useful results from today's most powerful AI models. Beginner Prompt Engineering Prompt Engineering Learn More Course DeepLearning.AI Build with Andrew Build with Andrew If you've never written code before, this course is for you. In less than 30 minutes, you'll learn to describe an idea in words and let AI transform it into an app for you. Beginner AI Coding AI Coding Learn More Course DeepLearning.AI Agentic AI Agentic AI In this course taught by Andrew Ng, you'll build agentic AI systems that take action through iterative, multi-step workflows. Intermediate Agents Agents Learn More Previous slide Next slide Course Type Short Course: Quickly learn practical skills and industry tools through hands-on projects. Course: Gain new knowledge on topics in AI with flexible online learning. Earn a shareable certificate. Professional Certificate: Master career skills through long form courses and projects. Earn a shareable certificate. Short Course 97 Course 13 Professional Certificate 10 Level Beginner 63 Intermediate 57 Popular Topics GenAI Applications 57 Prompt Engineering 46 Agents 40 RAG 31 Generative Models 28 LLMOps 26 AI Frameworks 21 Chatbots 21 Search and Retrieval 21 Evaluation and Monitoring 20 NLP 19 Task Automation 19 Embeddings 18 Fine-Tuning 17 Transformers 16 AI Coding 14 Deep Learning 14 Data Processing 13 Vector Databases 13 Document Processing 11 AI in Software Development 10 Machine Learning 9 MultiModal 9 AI Safety 8 Computer Vision 8 Supervised Learning 7 MLOps 5 Data Engineering 4 LLM Serving 4 Compression and Quantization 3 Diffusion Models 3 Anomaly Detection 2 Event-Driven AI 2 Mathematical Foundations 2 On-Device AI 1 Synthetic Data 1 Time Series 1 Unsupervised Learning 1 Collaborator DeepLearning.AI 19 Google Cloud 6 Hugging Face 5 LangChain 5 Anthropic 4 LlamaIndex 4 Meta 4 OpenAI 4 Google 3 Microsoft 3 Snowflake 3 AMD 2 AMD, formerly Lamini 2 Databricks 2 Flower Labs 2 Neo4j 2 Predibase 2 Qdrant 2 crewAI 2 AGI Inc 1 AI Dungeon 1 AI21 labs 1 AWS 1 Arize AI 1 Astronomer 1 Box 1 Chroma 1 CircleCI 1 Cohere 1 Comet 1 CopilotKit 1 CrewAI 1 DotTxt 1 E2B 1 Gemini CLI 1 Giskard 1 GuardrailsAI 1 Haystack 1 IBM Research 1 Jay Alammar, Maarten Grootendorst 1 JetBrains 1 LMSys 1 LandingAI 1 Letta 1 LiveKit 1 Mistral AI 1 MongoDB 1 NexusFlow 1 Nexusflow 1 Nvidia 1 Add filters Course Type Short Course: Quickly learn practical skills and industry tools through hands-on projects. Course: Gain new knowledge on topics in AI with flexible online learning. Earn a shareable certificate. Professional Certificate: Master career skills through long form courses and projects. Earn a shareable certificate. Short Course 97 Course 13 Professional Certificate 10 Level Beginner 63 Intermediate 57 Popular Topics GenAI Applications 57 Prompt Engineering 46 Agents 40 RAG 31 Generative Models 28 LLMOps 26 AI Frameworks 21 Chatbots 21 Search and Retrieval 21 Evaluation and Monitoring 20 NLP 19 Task Automation 19 Embeddings 18 Fine-Tuning 17 Transformers 16 AI Coding 14 Deep Learning 14 Data Processing 13 Vector Databases 13 Document Processing 11 AI in Software Development 10 Machine Learning 9 MultiModal 9 AI Safety 8 Computer Vision 8 Supervised Learning 7 MLOps 5 Data Engineering 4 LLM Serving 4 Compression and Quantization 3 Diffusion Models 3 Anomaly Detection 2 Event-Driven AI 2 Mathematical Foundations 2 On-Device AI 1 Synthetic Data 1 Time Series 1 Unsupervised Learning 1 Collaborator DeepLearning.AI 19 Google Cloud 6 Hugging Face 5 LangChain 5 Anthropic 4 LlamaIndex 4 Meta 4 OpenAI 4 Google 3 Microsoft 3 Snowflake 3 AMD 2 AMD, formerly Lamini 2 Databricks 2 Flower Labs 2 Neo4j 2 Predibase 2 Qdrant 2 crewAI 2 AGI Inc 1 AI Dungeon 1 AI21 labs 1 AWS 1 Arize AI 1 Astronomer 1 Box 1 Chroma 1 CircleCI 1 Cohere 1 Comet 1 CopilotKit 1 CrewAI 1 DotTxt 1 E2B 1 Gemini CLI 1 Giskard 1 GuardrailsAI 1 Haystack 1 IBM Research 1 Jay Alammar, Maarten Grootendorst 1 JetBrains 1 LMSys 1 LandingAI 1 Letta 1 LiveKit 1 Mistral AI 1 MongoDB 1 NexusFlow 1 Nexusflow 1 Nvidia 1 Course Type Short Course: Quickly learn practical skills and industry tools through hands-on projects. Course: Gain new knowledge on topics in AI with flexible online learning. Earn a shareable certificate. Professional Certificate: Master career skills through long form courses and projects. Earn a shareable certificate. Short Course 97 Course 13 Professional Certificate 10 Level Beginner 63 Intermediate 57 Popular Topics GenAI Applications 57 Prompt Engineering 46 Agents 40 RAG 31 Generative Models 28 LLMOps 26 AI Frameworks 21 Chatbots 21 Search and Retrieval 21 Evaluation and Monitoring 20 NLP 19 Task Automation 19 Embeddings 18 Fine-Tuning 17 Transformers 16 AI Coding 14 Deep Learning 14 Data Processing 13 Vector Databases 13 Document Processing 11 AI in Software Development 10 Machine Learning 9 MultiModal 9 AI Safety 8 Computer Vision 8 Supervised Learning 7 MLOps 5 Data Engineering 4 LLM Serving 4 Compression and Quantization 3 Diffusion Models 3 Anomaly Detection 2 Event-Driven AI 2 Mathematical Foundations 2 On-Device AI 1 Synthetic Data 1 Time Series 1 Unsupervised Learning 1 Collaborator DeepLearning.AI 19 Google Cloud 6 Hugging Face 5 LangChain 5 Anthropic 4 LlamaIndex 4 Meta 4 OpenAI 4 Google 3 Microsoft 3 Snowflake 3 AMD 2 AMD, formerly Lamini 2 Databricks 2 Flower Labs 2 Neo4j 2 Predibase 2 Qdrant 2 crewAI 2 AGI Inc 1 AI Dungeon 1 AI21 labs 1 AWS 1 Arize AI 1 Astronomer 1 Box 1 Chroma 1 CircleCI 1 Cohere 1 Comet 1 CopilotKit 1 CrewAI 1 DotTxt 1 E2B 1 Gemini CLI 1 Giskard 1 GuardrailsAI 1 Haystack 1 IBM Research 1 Jay Alammar, Maarten Grootendorst 1 JetBrains 1 LMSys 1 LandingAI 1 Letta 1 LiveKit 1 Mistral AI 1 MongoDB 1 NexusFlow 1 Nexusflow 1 Nvidia 1 Add filters Course Type Short Course: Quickly learn practical skills and industry tools through hands-on projects. Course: Gain new knowledge on topics in AI with flexible online learning. Earn a shareable certificate. Professional Certificate: Master career skills through long form courses and projects. Earn a shareable certificate. Short Course 97 Course 13 Professional Certificate 10 Level Beginner 63 Intermediate 57 Popular Topics GenAI Applications 57 Prompt Engineering 46 Agents 40 RAG 31 Generative Models 28 LLMOps 26 AI Frameworks 21 Chatbots 21 Search and Retrieval 21 Evaluation and Monitoring 20 NLP 19 Task Automation 19 Embeddings 18 Fine-Tuning 17 Transformers 16 AI Coding 14 Deep Learning 14 Data Processing 13 Vector Databases 13 Document Processing 11 AI in Software Development 10 Machine Learning 9 MultiModal 9 AI Safety 8 Computer Vision 8 Supervised Learning 7 MLOps 5 Data Engineering 4 LLM Serving 4 Compression and Quantization 3 Diffusion Models 3 Anomaly Detection 2 Event-Driven AI 2 Mathematical Foundations 2 On-Device AI 1 Synthetic Data 1 Time Series 1 Unsupervised Learning 1 Collaborator DeepLearning.AI 19 Google Cloud 6 Hugging Face 5 LangChain 5 Anthropic 4 LlamaIndex 4 Meta 4 OpenAI 4 Google 3 Microsoft 3 Snowflake 3 AMD 2 AMD, formerly Lamini 2 Databricks 2 Flower Labs 2 Neo4j 2 Predibase 2 Qdrant 2 crewAI 2 AGI Inc 1 AI Dungeon 1 AI21 labs 1 AWS 1 Arize AI 1 Astro