Free AI Courses from Google, Microsoft, IBM & Other Tech Giants
Introduction
In 2026, mastering Artificial Intelligence will no longer require expensive degrees or paid bootcamps—tech titans including as Google, Microsoft, IBM, and others are delivering world-class AI courses online for free, replete with certifications that can increase your résumé and professional prospects.
- Why Free AI Courses Are Game Changers
- How to Navigate This Guide
- Google – Free AI Courses
- Microsoft – Free AI Courses
- IBM – Free AI Courses
- AWS – Free AI Courses
- DeepLearning.AI – Free AI Courses
- fast.ai – Free AI Courses
- University of Helsinki – Elements of AI
- NVIDIA – Free AI Courses
- Huawei ICT Academy – Free AI Courses
- Harvard & Google – TinyML Specialization
- Tips to Maximize Learning
- Conclusion
Why Free AI Courses Are Game Changers in 2026
Artificial Intelligence is everywhere—from everyday productivity tools to core workflows at top companies. Free AI courses from trusted tech giants let you build in-demand skills without tuition costs. Many include completion certificates, giving your résumé a credible boost.
How to Navigate This Guide
- Beginners welcome: Many courses require no coding.
- 100% free access: Audit options or entirely free curricula.
- Certificates: Noted where commonly available.
- Direct links: Replace the placeholder links with the official URLs you prefer.
Google – Free AI Courses
Course 1: Introduction to Generative AI
Platform: Grow with Google / Google Cloud Skills Boost
Duration: ~45 minutes
Format: Short videos + quizzes
Key topics: Text generation, AI creativity, use cases
Why it’s great: Beginner-friendly, quick start, Google-certified
Course 2: Introduction to Responsible AI
Duration: ~30 minutes
Key topics: Ethical AI design, fairness, transparency, bias
Why it’s great: Understand AI ethics from the source
Course 3: Attention Mechanisms & Transformers
Duration: ~90 minutes (2 modules)
Key topics: How AI models “focus” on data, architecture of GPT-like models
Extra: Includes a practical lab for building AI models
Course 4: AI Essentials & Prompting Essentials
Duration: 5–6 hours each
Key topics: AI basics + how to write effective AI prompts
Why it’s great: Builds practical prompt engineering skills
Microsoft – Free AI Courses
Course 5: Generative AI for Beginners
Duration: 18 lessons (~10 hours)
Format: Video + code labs (Python, TypeScript)
Key topics: Prompt engineering, LLMs, vector databases, agents, RAG, responsible AI
Course 6: AI for Beginners (GitHub Curriculum)
Duration: 12 weeks (24 lessons)
Format: Hands-on labs + quizzes
Key topics: AI fundamentals, computer vision, NLP, ML basics
Course 7: Azure AI Fundamentals Learning Path
Platform: Microsoft Learn
Duration: Self-paced (~6–8 hours)
Key topics: Azure AI services, building AI-powered apps
Certificate: Free Microsoft Learn badge
IBM – Free AI Courses
Course 8: AI for Everyone – Master the Basics
Platform: edX (IBM Skills Network)
Duration: ~6 hours
Key topics: AI concepts, applications, ethics
Course 9: AI Chatbots Without Programming
Platform: CognitiveClass.ai (IBM)
Duration: ~4 hours
Key topics: Build chatbots using Watson Assistant, no coding needed
Course 10: Machine Learning with Python – A Practical Introduction
Platform: CognitiveClass.ai (IBM)
Duration: ~20 hours
Key topics: ML models, Python libraries, real datasets
AWS – Free AI & Generative AI Training
Course: Introduction to Generative AI – Art of the Possible
Duration: ~1 hour
Key topics: Basics of generative AI, risks & benefits, AWS services overview
Introduction to Generative AI – Art of the Possible (Coursera, free to audit)
Course: Generative AI Learning Plan (AWS Skill Builder)
Format: Collection of curated modules
Focus: Building generative AI apps using Amazon Bedrock, Amazon Q, RAG, and more
Course: AWS Cloud Quest: Generative AI Practitioner
Format: Game-based learning role
Focus: Interactive challenges using LLMs, embeddings, RAG, Amazon Titan & more
DeepLearning.AI – Free AI Courses
About: Founded by Andrew Ng, DeepLearning.AI specializes in high-quality, beginner-to-advanced AI training with a focus on practical applications. Their short, focused courses are perfect for students who want to quickly grasp key AI skills.
Course 1: Generative AI for Everyone
Duration: ~3 hours
Format: Video lectures + quizzes
Key topics: What generative AI is, how it works, its capabilities, limitations, and impact on society.
Why it’s great: No coding required; helps beginners understand AI in plain language.
Course 2: ChatGPT Prompt Engineering for Developers
Duration: ~1 hour
Format: Hands-on coding with Python + OpenAI API
Key topics: Crafting effective prompts, using ChatGPT for code generation, content creation, and automation.
Why it’s great: Teaches prompt engineering from a developer’s perspective with real-world use cases.
Fast.ai – Free AI Courses
About: Fast.ai is a nonprofit AI research group offering free, practical courses for learners to build real-world AI applications without heavy math or cost.
Course 1: Practical Deep Learning for Coders (Part 1)
Duration & Format: 9 video lessons (~90 mins each) with interactive Jupyter notebooks.
Key topics: Image classification, NLP, recommendation systems, training techniques (ResNet, SGD), deployment, data ethics.
Why it stands out: Code-first approach; math only when needed.
Course 2: Practical Deep Learning for Coders (Part 2)
Duration: 30+ hours of video content.
Focus: Advanced topics: diffusion models, attention, CLIP embeddings, U-Nets, ResNets, Stable Diffusion projects.
Ideal for: Learners who completed Part 1 or know PyTorch and deep learning basics.
Course 3: Introduction to Machine Learning for Coders
Duration: ~24 hours (~12 weeks at 8 hours/week)
Topics: Random forests, logistic regression, gradient descent, validation, embeddings, feature importance, ethics.
Why it’s useful: Builds strong ML foundations with a practical, code-first approach.
NVIDIA Deep Learning Institute (DLI) – Free AI Courses
About: NVIDIA DLI provides GPU-accelerated AI training for developers, data scientists, engineers, and students.
Course 1: Fundamentals of Deep Learning
Duration: ~8 hours (self-paced)
Format: Interactive GPU labs with guided theory
Key topics: Neural networks, activation functions, forward/backward propagation, image classification, transfer learning, deployment
Why it’s great: Balanced theory and hands-on practice, ideal for beginners.
Course 2: Fundamentals of Accelerated Computing with CUDA Python
Duration: ~8 hours (self-paced)
Key topics: CUDA programming, parallelism, memory optimization, GPU acceleration for AI/data science
Why it’s great: Boost computation speed using NVIDIA GPUs.
Course 3: Generative AI with Diffusion Models
Duration: ~8 hours
Key topics: Diffusion model theory, denoising, latent space, conditioning, AI image synthesis projects
Why it’s great: Build custom AI image generators for art, design, and content creation.
Course 4: AI for Anomaly Detection
Duration: ~6 hours
Key topics: Anomaly detection, supervised/unsupervised approaches, time-series analysis, applications in cybersecurity, IoT, manufacturing
Why it’s great: Solve real-world problems with deployable AI strategies.
Certification
DLI courses award free, verifiable digital badges. Add to LinkedIn, resume, or portfolio to showcase GPU-accelerated AI, CUDA, and deep learning skills recognized across the tech industry.
6. University of Helsinki / MinnaLearn
About: A free, globally recognized AI MOOC offering accessible AI fundamentals and practical skills, available in 20+ languages for everyone.
Course: Elements of AI
Duration: Self-paced (two parts, each ~20–30 hours).
Format: Online theory lessons, quizzes, and coding exercises in Python (optional for beginners).
Key topics: AI concepts, real-world applications, ethics, algorithms, machine learning basics, and small Python-based projects.
Why it’s great: No prerequisites, available in multiple languages, and blends theory with hands-on exercises for all skill levels.
Huawei ICT Academy – Free AI & IoT Courses
About: Huawei ICT Academy, in partnership with global universities, offers self-paced and instructor-led ICT training programs—spanning AI, algorithms, IoT, and certification pathways across multiple languages.:contentReference[oaicite:4]{index=4}
Course 1: Overview of AI
Level: Beginner
Key topics: Evolution of AI, major AI schools of thought, current trends, and real-world applications, with practical simulations and a digital badge upon completion.:contentReference[oaicite:5]{index=5}
Course 2: Artificial Intelligence Technology and Applications
Level: Beginner to Intermediate
Key topics: Fundamentals of ML & DL, Python programming, Huawei’s AI frameworks (MindSpore, Ascend), AI deployment, exam prep, and professional certificate eligibility.:contentReference[oaicite:6]{index=6}
Course 3: Internet of Things Technology and Applications
Level: Intermediate
Key topics: IoT fundamentals including NB-IoT, gateway systems, data collection methods, and development practices for embedded systems.:contentReference[oaicite:7]{index=7}
Harvard & Google – TinyML Specialization
About: A professional certificate series co-created by Harvard University and Google (TensorFlow team), this TinyML program teaches you how to build and deploy AI on tiny, resource-limited devices—covering everything from embedded machine learning basics to real-world applications.
Course 1: Fundamentals of TinyML
Platform: edX
Duration: ~5 weeks (2–4 hours/week)
Key topics: ML & deep learning basics, embedded devices, data collection, model training, and responsible AI design.
Course 2: Applications of TinyML
Platform: edX
Duration: ~6 weeks (estimated 2–4 hours/week)
Key topics: Practical TinyML use cases like keyword spotting, visual wake words, gesture recognition, and real-world deployment challenges.
Course 3: Deploying TinyML
Platform: edX
Duration: ~6 weeks (estimated 2–4 hours/week)
Key topics: Programming with TensorFlow Lite for Microcontrollers, deploying ML to Arduino-based devices using the TinyML Program Kit.
Advanced Topic: MLOps for Scaling TinyML
Platform: edX
Key topics: Principles of deploying, monitoring, and managing TinyML systems at scale using MLOps techniques.
Tips to Maximize Learning from These Courses
- Study 10–15 minutes daily to build consistency.
- Alternate theory and hands-on practice; track progress in a simple log.
- Keep a public GitHub portfolio of mini-projects & notebooks.
- Ask and answer questions in discussion forums for deeper learning.
Conclusion: Fast Track Your AI Journey
With high-quality free courses from Google, Microsoft, IBM, AWS, NVIDIA, DeepLearning.AI, and universities, you can build practical AI skills without cost. Pick one course today; in a few weeks, you’ll have portfolio-ready skills and certifications that move your career forward.