Free Online Learning Resources

Stop wasting thousands on overpriced AI bootcamps! You can master the exact same skills for free with these channels 👇 Andrej Karpathy → Founding member of OpenAI and the former Director of AI at Tesla. His "Zero to Hero" series is legendary. Posts once a year but that’s enough to create ripples in the AI industry. (Note: He taught Stanford's first Deep Learning course (CS231n), which trained the first generation of modern AI engineers) GenAI Explained → Turning complex AI breakthroughs into short, practical, and easy to understand videos that help you discover what matters, learn how it works and grow your skills in real-time. Backed by a global community of 13M+ builders and researchers Stanford Online → Ivy League education for free. Famous CS224N (Natural Language Processing) and CS231n lectures. Watch the world’s leading researchers (like Li Fei-Fei and Christopher Manning) deliver the same content their students pay thousands for. Serrano Academy → Luis Serrano (PhD in Math and Author) simplifies complex topics through hand-drawn cartoons and analogies. Learn attention mechanisms and LLMs with a friendly and high-energy vibe. Jeremy Howard → Top-down learning. Build the app first, figure out the math later. Teaches practical deep learning for coders. No PhD required. 3Blue1Brown → Visual poetry. Grant Sanderson uses his own custom animation engine (Manim) to visualize neural networks, backpropagation and linear algebra. He made the Transformer architecture (the T in ChatGPT) visually understandable for the first time. Hamel Husain → Learn the dirty work of AI: evaluations (evals), data curation and deployment. He is the Engineer's Engineer. Former Lead ML Engineer at GitHub and Airbnb and a major contributor to open-source tools like nbdev. Dave Ebbelaar → King of RAG (Retrieval-Augmented Generation). While others talk about models, he talks about pipelines and how to connect your private data (PDFs, Notion, SQL) to an LLM safely. Machine Learning Street Talk → Deeply technical and unedited. Interviews with AI rebels like Yann LeCun (former Meta's AI chief), who challenge the current direction of LLMs. Critical, non-hype perspective. Lex Fridman → Introspective interviews with everyone who matters in AI: Sam Altman, Demis Hassabis, Yann LeCun, Elon Musk. Vision and history of where the field is going. Get more GenAI resources for FREE: https://lnkd.in/e64Jvdrt

These YouTube channels got you covered if you’d like to start learning AI Learning Artificial Intelligence can feel confusing, as there are so many concepts to grasp, starting with math foundations to deploying successfully. But with the right channels, you can learn everything step by step, for free. Here’s a cheat sheet highlighting the best YouTube channels for AI in 2025, organized into categories like Math, Python, Core ML, Deep Learning, MLOps, and Research. Each channel simplifies tough concepts, shows practical coding examples, and keeps you updated with the latest AI breakthroughs. ✅ Channels Covered: 1. Math for ML → Essence of Linear Algebra (3Blue1Brown), Khan Academy, StatQuest with Josh Starmer. 2. Python for ML → Programming with Mosh, Tech with Tim, Sentdex, freeCodeCamp.org. 3. Core ML Fundamentals → Stanford CS229, Andrej Karpathy, CodeEmporium, Corey Schafer. 4. MLOps & Deployment → Krish Naik, CodeBasics, Cloud With Raj, MLOps Community. 5. Projects & Kaggle → Abhishek Thakur, Data Professor, CodeBasics (projects), Krish Naik (end-to-end ML). 6. Deep Learning → Andrej Karpathy, freeCodeCamp.org, DeepLearningAI, Two Minute Papers. 7. Staying Updated (Research) → Arxiv Insights, The Cutting Edge School, Two Minute Papers, Yannic Kilcher. Feel free to save this guide and share with others who have similar interests. #AI

If you’re trying to learn AI through real-world projects instead of endless theory, this GitHub repo is worth checking out 👀🔥 A lot of AI learning resources have the same problem: too much theory, disconnected tutorials, and examples that never translate into production use cases. That’s why this stood out to me. Oracle recently open-sourced a massive AI learning and implementation hub packed with: ✅ 10+ deployable AI applications ✅ 20+ hands-on notebooks ✅ Guided workshops ✅ Real enterprise AI agent architectures ✅ End-to-end implementation references And the best part? These are not toy demos. They’re practical systems designed around real workflows and production-style setups. Some interesting projects inside: 🔹 FitTracker — gamified fitness platform using FastAPI, Redis, and Oracle 26ai 🔹 agentic_rag — multi-agent RAG with PDF + web ingestion 🔹 finance-ai-agent-demo — AI finance assistant with memory capabilities 🔹 oci-generative-ai-jet-ui — full-stack AI app with Kubernetes + Terraform 🔹 tanstack-shoe-store — natural language database interaction 🔹 agent-reasoning — experiments with reasoning frameworks like CoT, ToT, and ReAct 🔹 limitless-workflow — Claude-powered autonomous workflows 🔹 Plus additional implementations using Java and Vector Databases The notebooks and workshops also cover: 📌 RAG fundamentals 📌 Agent memory systems 📌 Hybrid search 📌 Multi-agent orchestration 📌 Multi-cloud deployment patterns This is the kind of resource that helps bridge the gap between “learning AI” and actually building AI applications. ⭐ Definitely worth bookmarking or starring if you're serious about AI engineering. ( link in comment) Share this with someone who’s trying to break into AI ❤️

Free AI learning options from 10 official platforms, arranged as one roadmap. Do not collect all ten and call it progress. Follow the path. Learn one layer. Build something before moving on. Level 1: Foundations 1) Google AI fundamentals, Gemini tools, and prompt writing. https://grow.google/ai/ 2) OpenAI Academy LLM fundamentals, prompt design, and agent workflows. https://lnkd.in/gkbrNy2X 3) IBM SkillsBuild Machine learning, NLP, AI ethics, and digital credentials. https://lnkd.in/gk-VRqHM Level 2: Build and agents 4) Microsoft Learn Azure AI, agent workflows, evaluation, and production systems. https://lnkd.in/gbhwHQHp 5) DeepLearning.AI Prompt engineering, RAG systems, and practical LLM apps. https://lnkd.in/gvKn6t_z 6) Anthropic Learn Claude workflows, the Claude API, and MCP fundamentals. https://lnkd.in/g_zmEV5y Level 3: Models and production 7) Hugging Face Learn Transformers, fine-tuning, and open-source models. https://lnkd.in/g4wUDA7M 8) Meta AI PyTorch, open-source AI, and ML research. https://ai.meta.com/learn/ 9) NVIDIA Deep learning, accelerated computing, and model deployment. https://lnkd.in/gaUtAPVH 10) AWS Skill Builder Generative AI architecture, Amazon Bedrock, and cloud deployment. https://skillbuilder.aws/ The last step matters most. Build and ship one real AI project before buying another course. Pick one resource from each level. Take notes. Turn those notes into working software. Which platform are you starting with? ♻️ Repost this to help someone learn AI without spending money first. I'm Shrey Shah & I talk about harness engineering.

6 AI companies. 18 free AI courses. One uncomfortable conclusion: Access to AI education is no longer the bottleneck. Anthropic, OpenAI, Google, Microsoft, NVIDIA, and IBM have all put serious learning paths in the open. Not random YouTube playlists. Not recycled AI tips. Courses built by the companies shaping the market. I pulled together the top free courses from each platform. (Links to the full learning in the comments below) Anthropic: • Claude 101 • AI Fluency: Framework & Foundations • Claude Code 101 OpenAI: • AI Foundations • ChatGPT at Work • Professors Teaching with OpenAI Google: • Google AI Essentials • Intro to Generative AI • Generative AI Learning Path Microsoft: • Generative AI for Beginners • Azure AI Fundamentals • AI Skills Navigator NVIDIA: • Generative AI Explained • Agentic AI • Fundamentals of Deep Learning IBM: • AI Foundations • Generative AI: Intro and Applications • AI Engineering Professional Certificate But here is the part most people miss. Taking the course is the easy layer. The advantage was never knowing where the prompt box is. It comes from judgment: Which work should change. Which decisions should stay human. Which workflows need redesign. Where AI creates leverage instead of noise. That is not on any syllabus. When the education is free, the bottleneck moves from access to adoption. From “can my team learn AI?” to “can we apply it to the right work, in the right places, without breaking what already works?” That gap is where leaders get made. The ones remembered in this era won’t be the ones who finished the most courses. They’ll be the ones who turned the learning into changed work, measurable outcomes, and a business that runs differently than it did a year ago. That is the move from pilots to platforms. Share this with the person on your team who keeps saying, “I need to learn AI.” Then ask the better question: Learn it to do what?

If you lead a team, product, or company, AI is no longer optional. The only question is: Are you learning it fast enough? Here’s how to stop winging it and build real AI fluency: A curated list of 19 world-class (and mostly free) AI learning paths (including the exact ones I recommend to executives, analysts, and builders) 🧠 FOUNDATIONAL COURSES 1. Learn Generative AI - DeepLearning.AI + OpenAI The most recommended intro to GenAI today. 2. Prompt Engineering for ChatGPT - Vanderbilt University Go from casual to elite-level prompting. 3. AI for Everyone - Andrew Ng Non-technical, powerful, and practical. 4. Google's Generative AI Learning Path (Gemini + Vertex AI) Build smart apps, fine-tune models, and explore Google’s full GenAI stack. 5. Microsoft’s Generative AI Fundamentals Hands-on GenAI labs for business teams. 🎓 TAUGHT BY ME, Amit Rawal Real-world AI. Taught by a Google Director & former Apple AI lead. 6. AI for Data Analysis & Storytelling - Maven Turn raw data into insight, clarity, and action. 7. AI for Data Analysis - Section School Master AI-native workflows used by elite analysts. 8. AI for Research - Section School Learn how top 1% researchers work faster, better, deeper. 9. Design Your Life with AI - Supercharge Life AI Rebuild your identity, habits, and systems using AI. 🛠️ TOOLS + SPECIALIZED TRACKS 10. Build AI Agents - CrewAI, AutoGen, OpenDevin Step-by-step guides to building multi-agent workflows. 11. Learn LangChain - Official Docs + YouTube Master the framework behind most AI agents. 12. Claude AI Guide - Anthropic Master Claude’s capabilities for deep thinking and safe reasoning. 13. Meta’s LLaMA 2 Course - Coursera Go open-source. Build smarter. 14. AWS GenAI Learning Plan Use Amazon’s tools to build smart apps. 📚 ADVANCED / UNIVERSITY-GRADE 15. Harvard’s Machine Learning Course Top Ivy League education, totally free. 16. Stanford’s CS224N - NLP with Deep Learning For serious builders. YouTube playlist is gold. 17. FastAI’s Deep Learning for Coders Make neural nets with 10 lines of code. 18. Meta AI & FAIR Research Library Explore cutting-edge papers from the frontier of AI. 19. Google Research & Gemini SDKs Build, test, and deploy your own Gemini-powered AI. __ This isn’t about AI hacks. It’s about upgrading how you learn, lead, and execute. ___________________________________________ 👋 I’m Amit Rawal , Director of AI-led Business Transformation at Google Outside of work, I’m building SuperchargeLife.ai , a global movement to make AI education accessible and human-centered. 🧠 Join my free masterclass: Design Your Life with AI Learn how to work smarter, live longer, and grow richer, with AI as your co-pilot. ♻️ Repost if you believe AI isn’t about replacing us… It’s about retraining us to think better.

Stop scrolling. This will challenge how you are learning AI. Most people are chasing random AI tutorials. Prompt hacks. Viral threads. Surface-level tricks. That is not how real AI understanding is built. What actually matters is structured learning from the people building the technology itself. NVIDIA just released 11 official AI guides, many of them are completely free. These are not influencer tutorials. They are industry-grade resources used by engineers, developers, and enterprise AI teams. This is not hype content. This is how AI actually works. If you are: • A beginner trying to truly understand AI • A developer building real systems • A professional trying to stay relevant This is where the gap between users and builders starts to close. Here is what NVIDIA quietly made available 👇 1. Generative AI Explained A clear, plain-language breakdown of what generative AI can and cannot do. No coding. No math. Just fundamentals. https://lnkd.in/gWYkmDvu 2. Building a Brain in 10 Minutes A fast mental model for how neural networks learn and reason. If AI has ever felt confusing, start here. https://lnkd.in/gGqkT-Wz 3. AI for All: From Basics to GenAI Practice Machine learning basics that transition into hands-on GenAI workflows. Ideal for non-engineers and business leaders. https://lnkd.in/gjSpDWcB 4. Getting Started with AI on Jetson Nano Hands-on notebooks that guide you through building and running your first neural network. Perfect for applied learning. https://lnkd.in/gDSvADsW 5. Introduction to AI in the Data Center Explains GPUs, inference, and how AI is deployed at scale. A layer most courses completely skip. https://lnkd.in/g_nQsRaX 6. An Even Easier Introduction to CUDA Beginner-friendly entry into GPU parallel computing. Essential for understanding why GPUs power AI. https://lnkd.in/gvs-ni9D 7. Introduction to NVIDIA NIM Microservices How modern AI systems are packaged, deployed, and scaled in production. Highly relevant for enterprise use cases. https://lnkd.in/gXMnQ7t9 8. Generative AI in Digital Health Real examples of chatbots, clinical notes, and regulated AI workflows. Shows how GenAI moves beyond theory. https://lnkd.in/gKGxf4ye 9. AI Weather Models with NVIDIA Earth-2 A look into AI-driven climate modeling and large-scale simulations. This is AI at national infrastructure scale. https://lnkd.in/gReNCv8n Most people are learning AI through social media tips. These guides teach systems, economics, infrastructure, and deployment. That is the difference between: • Users and builders • Experiments and production • Hype and real leverage AI literacy is becoming a baseline skill, not a specialty. Would you rather keep paying for recycled tutorials, or learn directly from the people building the stack? Which guide would you start with and why? Drop your thoughts 👇 #AI #GenerativeAI #NVIDIA #MachineLearning #DeepLearning #Upskilling #TechCareers #FutureOfWork

I used to think learning AI meant collecting more courses. Another roadmap. Another certificate. Another “complete AI bootcamp.” Another saved YouTube playlist I never finished. But after spending more time around builders, startups, and AI events in Silicon Valley, my view changed. The people actually getting better at AI are not just consuming more content. They are learning from the source, then building small things every week. If I had to restart my AI learning journey today, I would not begin with a $2,000 course. I would do this instead: First, learn the basics from the companies building the infrastructure. Start with: 1. Anthropic for Claude, prompting, and evaluations 2. OpenAI Academy for APIs and applied AI use cases 3. Google AI for Gemini and AI fundamentals 4. Microsoft Learn for Copilot, Azure AI, and enterprise workflows 5. Amazon Web Services (AWS) Skill Builder for Bedrock and cloud AI systems 6. NVIDIA DLI for GPUs, deep learning, and deployment 7. Hugging Face for transformers, datasets, and open-source models 8. DeepLearning.AI for structured ML, LLM, and agent learning 9. Meta AI for Llama and open model research 10. IBM SkillsBuild for beginner-friendly AI foundations Then pick one YouTube educator for depth. Not ten at once. One. Andrej Karpathy if you want foundations. 3Blue1Brown if you want intuition. Umar Jamil if you want technical walkthroughs. GPU MODE if you want to understand systems and performance. Then build something small. A chatbot over your own notes. A RAG app over messy PDFs. An agent that uses one tool. A prompt evaluation sheet. A workflow that saves you 30 minutes a week. That is where the learning compounds. My honest take: ➡️ Free resources are not the problem anymore. ➡️The problem is that most people keep collecting resources because it feels productive. ➡️But AI is not learned by saving links. ➡️It is learned by building, testing, debugging, and explaining what you built. So before you buy another expensive AI course, ask yourself: ➡️Have I finished one free course? ➡️Have I built one project? ➡️Have I shared one lesson publicly? ➡️Have I tried to explain one concept simply? That will teach you more than another certificate sitting on your LinkedIn profile. If you are learning AI seriously this year, comment AI and I will share my free AI learning stack. ⚡️ Repost if this could help someone in your network 🔄 🎯 Follow me for Data & AI insights 📸 Instagram: https://lnkd.in/gDkwWZ8w ▶️ YouTube 🎧 Podcast: Latency & Latte https://lnkd.in/gvjuJuGp

If I were to learn AI/ML from scratch, here are 10 resources I’d jump into. 🎓 Build strong foundations → master systems → go deep into GenAI. 1️⃣ CS229 – Machine Learning (Stanford) 🔗 https://lnkd.in/gcwK6dJu 📊 Classic ML course, math + intuition balanced 🧠 From linear models to deep nets 🚀 Perfect foundation for everything ahead 2️⃣ Mitesh Khapra – Deep Learning (IIT Madras) 🔗 https://lnkd.in/gBqVSiNV 📦 CNNs, RNNs, Transformers explained deeply 🧠 Math meets implementation 🚀 Builds strong DL intuition 3️⃣ fast.ai – Practical Deep Learning 🔗 https://course.fast.ai/ 🧪 Code-first learning from day one 📦 Vision, NLP, transfer learning 🌍 Massive, helpful community 4️⃣ Neural Networks: Zero to Hero – Karpathy 🔗 https://lnkd.in/gsHjMHrK 🔥 Build GPT-like models from scratch 🧰 Learn backprop, training loops 🤯 Deep model-building intuition 5️⃣ Chip Huyen – ML Systems Design 🔗 https://lnkd.in/gnsN6Be9 🏗️ Data, deployment, monitoring 🧠 Real-world tradeoffs explained 📊 Must-read for production ML 6️⃣ Balaraman Ravindran – Reinforcement Learning 🔗 https://lnkd.in/gez_m2zc 🧭 From bandits to policy gradients 🧠 Foundation for agentic systems 🚀 Bridges classical RL to GenAI 7️⃣ CS224N – NLP with Deep Learning 🔗 https://lnkd.in/gkvBs9NP 📚 Word vectors to transformers 🧠 Attention explained deeply 🚀 Core prep before LLMs 8️⃣ CS886 – Advanced Topics in LLMs (Waterloo) 🔗 https://lnkd.in/gGzzeYPB 🧪 Read and dissect top LLM papers 🧠 Scaling, evals, reasoning 🚀 Research meets real-world GenAI 9️⃣ CS336 – Language Models (Stanford) 🔗 https://lnkd.in/gCmi_MMD 🧠 Tokenization, pretraining, fine-tuning 📏 Evals and safety fundamentals 🛠️ Engineering LLMs at scale 🔟 LangChain – RAG Tutorials 🔗 https://lnkd.in/gijTwKWV 🧩 Build retrieval pipelines 🛠️ Agents and tools in action 📈 Ship production-grade GenAI apps

Recently, I've been bombarded with ads for AI courses, certification programs, and "master classes" that promise to teach you everything about artificial intelligence. But here's what I've realized, especially being in the learning and development space as AI has taken off: most of these courses are obsolete within months. AI is moving so quickly that by the time someone creates a course, edits it, and packages it for sale, the goalpost has already moved. The solution? Go directly to the source. Why learn about ChatGPT from someone who's never worked at OpenAI when you can learn FROM OpenAI? Why take a generic "AI productivity" course when you can learn Gemini directly from Google? And in many cases (like all six examples I'm sharing), they're free. Here are six courses straight from the companies building the top tools that your company may be investing in: 𝗢𝗽𝗲𝗻𝗔𝗜 𝗔𝗰𝗮𝗱𝗲𝗺𝘆 1️⃣ 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 - Learn the fundamentals of ChatGPT and how to use it effectively. 2️⃣ 𝗕𝘂𝗶𝗹𝗱 𝗬𝗼𝘂𝗿 𝗢𝘄𝗻 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 - Tips & Tricks for Custom GPTs - Learn how to create custom AI assistants tailored to your specific needs. 3️⃣ 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗣𝘂𝗿𝗽𝗼𝘀𝗲 - Best-Practice Techniques for Harnessing ChatGPT - Master advanced prompting strategies to get better results. Check out live courses/events at: https://lnkd.in/et-naZpT 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰 𝗔𝗰𝗮𝗱𝗲𝗺𝘆 4️⃣ 𝗔𝗜 𝗙𝗹𝘂𝗲𝗻𝗰𝘆: 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 & 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 : https://lnkd.in/epSxbhKi Learn to collaborate with AI systems effectively, efficiently, ethically, and safely through a structured framework approach. Certificate of completion available after final assessment. 𝗚𝗼𝗼𝗴𝗹𝗲 5️⃣ 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗚𝗲𝗺𝗶𝗻𝗶 𝗳𝗼𝗿 𝗚𝗼𝗼𝗴𝗹𝗲 𝗪𝗼𝗿𝗸𝘀𝗽𝗮𝗰𝗲: https://lnkd.in/eNgcJHG9 Learn how to use Gemini's AI features across Google Workspace apps to boost your productivity and streamline daily tasks. Earn a completion badge and learn key features that improve productivity. 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗟𝗲𝗮𝗿𝗻 6️⃣ 𝗔𝗜 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗱𝗮𝗶𝗹𝘆 𝘄𝗼𝗿𝗸: 𝗯𝗮𝘀𝗶𝗰𝘀 𝗳𝗼𝗿 𝗯𝗼𝗼𝘀𝘁𝗶𝗻𝗴 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆 𝘄𝗶𝘁𝗵 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝟯𝟲𝟱 𝗖𝗼𝗽𝗶𝗹𝗼𝘁: https://lnkd.in/e-KxfpWA Learn how to integrate Microsoft 365 Copilot into your workflow to automate tasks and enhance productivity across Word, Excel, PowerPoint, and Teams. Not only are ALL these courses free and always up-to-date, you'll also get completion certificates from the actual companies for a few these too! Any courses you add to the list?