beginnerFree

AI for Beginners

Name: AI for Beginners
Rating: 4.5 (1800 reviews)

by Dmitry Soshnikov & Team · Microsoft

4.5

(1,800 reviews)

80K+ enrolled12 weeksUpdated 2024-10

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Our Verdict

Worth it — with caveats

Microsoft's AI for Beginners is a free, open-source (MIT-licensed) 12-week, 24-lesson GitHub curriculum from Azure Cloud Advocates, led by Dmitry Soshnikov, PhD, that delivers a genuinely broad technical tour of AI: symbolic/GOFAI, neural networks, computer vision, NLP through Transformers and LLMs, plus genetic algorithms, reinforcement learning, multi-agent systems, and AI ethics. It is text-and-notebook based rather than video, with executable Jupyter notebooks for both PyTorch and TensorFlow/Keras, frequent quizzes, and optional labs. The standout value is breadth and price: with 48.2k GitHub stars it is one of the most popular free AI curricula, and unlike most beginner courses it covers older approaches (expert systems, GANs, VAEs) alongside modern deep learning. The honest catch is that the README itself says it deliberately skips the deep mathematics behind deep learning, classic machine learning, business cases, and conversational-AI/chatbot building, and it issues no certificate. Treat it as a strong self-study syllabus for someone who already knows basic Python and wants conceptual coverage with runnable code, not a hand-held, credential-bearing bootcamp.

Excellent free, broad, code-backed AI overview that is hard to beat on price and scope, but it is self-directed with no certificate, no instructor support beyond a Discord, and explicitly avoids the underlying math and classic ML, so it suits self-motivated learners with some Python rather than total beginners who need structure or a credential.

Best for: Self-motivated learners who already have basic Python (or are willing to learn it alongside) and want a single, broad, hands-on map of AI, from symbolic AI and neural nets to CNNs, Transformers, LLMs, GANs, reinforcement learning, and AI ethics, using runnable PyTorch/TensorFlow notebooks. It is also a ready-made teaching resource for educators (it ships sketchnotes, quizzes, and labs) and a useful conceptual refresher for developers who want to see how the major AI techniques fit together without paying for a course.

Skip if: People who need a certificate or career credential; complete non-programmers who want a gentle, video-led, hand-held path; learners who specifically want the deep mathematics of deep learning, classic machine learning (regression/clustering/scikit-learn), or to build production chatbots and conversational AI, all of which the curriculum explicitly states it does not cover; and anyone who relies on graded assignments, deadlines, or live instructor feedback to stay on track.

About This Course

12-week GitHub curriculum from Microsoft covering AI fundamentals, neural networks, NLP, computer vision, and generative AI.

What You'll Learn

Different approaches to AI, including the symbolic / 'good old-fashioned AI' (GOFAI) path: knowledge representation, reasoning, and expert systems

Neural network fundamentals from the perceptron and multi-layered perceptron up to building a small framework yourself, plus overfitting and using PyTorch and TensorFlow/Keras

Computer vision: OpenCV basics, CNNs and CNN architectures, transfer learning, autoencoders/VAEs, GANs and style transfer, object detection, and semantic segmentation (U-Net)

Natural language processing: bag-of-words/TF-IDF, Word2Vec/GloVe embeddings, language modeling, RNNs, Transformers and BERT, named-entity recognition, and Large Language Models with prompt programming and few-shot tasks

Less-common AI techniques most beginner courses skip: genetic algorithms, deep reinforcement learning, and multi-agent systems

Responsible AI and AI ethics as a dedicated lesson, plus an extra lesson on multi-modal networks (CLIP, VQGAN)

Curriculum

Part I-II: Introduction & Symbolic AI (Lessons 1-2)

Introduction and history of AI; knowledge representation and expert systems (GOFAI).

Part III: Introduction to Neural Networks (Lessons 3-5)

Perceptron; multi-layered perceptron and building your own framework; intro to PyTorch/TensorFlow frameworks and overfitting.

Part IV: Computer Vision (Lessons 6-12)

OpenCV intro; CNNs and architectures; pre-trained networks and transfer learning; autoencoders and VAEs; GANs and artistic style transfer; object detection; semantic segmentation with U-Net.

Part V: Natural Language Processing (Lessons 13-20)

Bag-of-words/TF-IDF; Word2Vec and GloVe; language modeling; recurrent and generative recurrent networks; Transformers and BERT; named-entity recognition; Large Language Models, prompt programming and few-shot tasks.

Part VI-VII + Extras (Lessons 21-25)

Genetic algorithms; deep reinforcement learning; multi-agent systems; AI ethics and responsible AI; plus an extra lesson on multi-modal networks (CLIP, VQGAN).

Prerequisites

Basic Python and general computing concepts (the repo includes a setup lesson and beginner-friendly examples, but coding fluency makes the notebooks far easier)
A working dev environment: VS Code or GitHub Codespaces to run the Jupyter notebooks (no paid cloud required)
Self-discipline, since the course is unguided self-study with no deadlines, grading, or required mentor

Instructor

Dmitry Soshnikov & Team

Instructor · Microsoft

Pros & Cons

Pros

Completely free and open-source under the MIT License, with no paywall, signup gate, or upsell, and one of the most popular AI curricula on GitHub (48.2k stars)
Unusually broad scope for a beginner course: it covers symbolic AI, classic deep learning, computer vision, NLP up to Transformers/LLMs, and rarely-taught topics like genetic algorithms, reinforcement learning, and multi-agent systems
Hands-on by design: every lesson ships executable Jupyter notebooks, available in both PyTorch and TensorFlow/Keras, with quizzes and optional labs, runnable in free GitHub Codespaces
Authoritative, maintained authorship (Microsoft Azure Cloud Advocates; primary author Dmitry Soshnikov, PhD), with 50+ community translations and a dedicated AI ethics lesson

Cons

By the authors' own statement the curriculum does NOT cover the deep mathematics behind deep learning, classic machine learning, AI business cases, or building conversational AI/chatbots, so it is conceptual breadth over rigorous depth
No certificate or formal credential is offered, which limits its value for resume or job-application purposes
Self-directed with no grading, deadlines, or live instructor support (only an optional Discord), so unmotivated or absolute-beginner learners can stall
Text/notebook format with no video lectures will not suit learners who prefer guided, on-camera instruction

Alternatives To Consider

Elements of AI

University of Helsinki

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Practical Deep Learning for Coders

fast.ai

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Azure AI Fundamentals

Microsoft Learn

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Frequently Asked Questions

Is AI for Beginners free?

Yes — AI for Beginners is free to access. Free and open-source (MIT License). No certificate, no paid tier; the only optional costs are your own compute if you run heavier notebooks outside the free GitHub Codespaces allotment.

Who is AI for Beginners for?

Self-motivated learners who already have basic Python (or are willing to learn it alongside) and want a single, broad, hands-on map of AI, from symbolic AI and neural nets to CNNs, Transformers, LLMs, GANs, reinforcement learning, and AI ethics, using runnable PyTorch/TensorFlow notebooks. It is also a ready-made teaching resource for educators (it ships sketchnotes, quizzes, and labs) and a useful conceptual refresher for developers who want to see how the major AI techniques fit together without paying for a course.

What will you learn in AI for Beginners?

Different approaches to AI, including the symbolic / 'good old-fashioned AI' (GOFAI) path: knowledge representation, reasoning, and expert systems; Neural network fundamentals from the perceptron and multi-layered perceptron up to building a small framework yourself, plus overfitting and using PyTorch and TensorFlow/Keras; Computer vision: OpenCV basics, CNNs and CNN architectures, transfer learning, autoencoders/VAEs, GANs and style transfer, object detection, and semantic segmentation (U-Net); Natural language processing: bag-of-words/TF-IDF, Word2Vec/GloVe embeddings, language modeling, RNNs, Transformers and BERT, named-entity recognition, and Large Language Models with prompt programming and few-shot tasks.

What are the prerequisites for AI for Beginners?

Basic Python and general computing concepts (the repo includes a setup lesson and beginner-friendly examples, but coding fluency makes the notebooks far easier); A working dev environment: VS Code or GitHub Codespaces to run the Jupyter notebooks (no paid cloud required); Self-discipline, since the course is unguided self-study with no deadlines, grading, or required mentor.

Is AI for Beginners worth it?

How we reviewed this course

This is an independent editorial assessment by Cursarium, based on Microsoft's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.

Sources

Free

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