Beginner friendly · No prior knowledge needed

AI & Machine Learning Course

Learn AI from the ground up, free, structured, and built for beginners. Master the tools that are reshaping how we work.

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35 lessons 9 units 10+ hrs Free certificate

What you'll learn

  • What AI and machine learning actually are and how they differ
  • How deep learning, neural networks, and transformers work
  • Python, NumPy, Pandas, TensorFlow, and PyTorch basics
  • How to build, evaluate, and deploy ML models
  • Generative AI, prompt engineering, RAG, and fine-tuning
  • AI ethics, governance, and real-world business applications
John Bowman

Your instructor

John Bowman

I built this course to share what I've learned about AI and machine learning in a way that actually makes sense. This course covers everything from first principles to deployment. The path I wish had existed when I started, and most importantly no fluff or filler content. Upon passing you'll receive your AI Certificate.

Course curriculum

1
Unit 1 - Introduction to Artificial Intelligence
4 lessons
What is AI? - History, definitions & the Turing Test 8 min
Categories of AI - Narrow, General & Super AI 6 min
How AI Learns - Supervised, Unsupervised & Reinforcement Learning 10 min
AI vs. Augmented Intelligence 5 min
2
Unit 2 - The Modern AI Landscape
4 lessons
Traditional AI vs. Generative AI - Architecture differences 9 min
Machine Learning, Deep Learning & Neural Networks - How they relate 10 min
Key AI Subfields - NLP, Computer Vision & Edge AI 8 min
AI Tools & Platforms - LLMs, Agents, RAG & the 2026 market 10 min
3
Unit 3 - Ethics, Governance & Responsible AI
3 lessons
Real-World AI Failures & Why Ethics Matter 9 min
Hallucinations, Bias & Intellectual Property 8 min
AI Governance Frameworks & Enterprise Guidelines 10 min
4
Unit 4 - Python for AI
2 lessons
Python Essentials for AI - Variables, data structures & control flow 12 min
Data Analysis with NumPy & Pandas 12 min
5
Unit 5 - Machine Learning
4 lessons
Linear Regression & Gradient Descent 11 min
Logistic Regression & Classification 10 min
Model Evaluation - Validation, Overfitting & Metrics 11 min
Key ML Algorithms - Decision Trees, SVMs & More 12 min
6
Unit 6 - Deep Learning & Neural Networks
7 lessons
Biological vs. Artificial Neurons 7 min
Artificial Neural Networks & Forward Propagation 9 min
Backpropagation & How Neural Networks Learn 11 min
Activation Functions & the Vanishing Gradient Problem 9 min
Deep Learning Libraries - TensorFlow, PyTorch & Keras 10 min
Convolutional Neural Networks (CNNs) with Keras 12 min
RNNs, Transformers & Autoencoders 13 min
7
Unit 7 - Generative AI & Practical Applications
3 lessons
Generative AI Models - VAEs, GANs & Diffusion 11 min
Prompt Engineering & AI Agents 10 min
RAG, Fine-Tuning & Enterprise AI Strategy 11 min
8
Unit 8 - AI in Production
4 lessons
Introduction to MLOps - From notebook to production 10 min
Model Deployment - APIs, containers & cloud services 11 min
Monitoring & Drift - Keeping models healthy in the real world 9 min
CI/CD for ML - Automating the machine learning pipeline 10 min
9
Unit 9 - AI Strategy & Business Applications
4 lessons
How Organisations Adopt AI - The adoption journey & ROI frameworks 10 min
Build vs. Buy - Evaluating AI tools as a business 8 min
AI Across Industries - Healthcare, marketing, finance & beyond 10 min
Making the Business Case for AI 9 min
Frequently Asked Questions
Is the AI & Machine Learning Course really free?
Yes, completely. All 35 lessons across 9 units are free to read without an account. You only need to sign up to track your progress and earn a certificate of completion.
Do I need a technical background to take this course?
No prior experience is required. The course starts from the absolute basics - what AI actually is - and builds up to advanced topics like deep learning, NLP, and MLOps. Each lesson links to the next so you can follow the path in order.
How long does the course take to complete?
That depends on your pace. Most people work through one or two lessons per session. At that rate, you can finish all 35 lessons in a few weeks. There's no time limit - you go at whatever pace suits you.
What certificate do I get?
Once you complete all 35 lessons and pass the final exam, you can download a certificate of completion from your dashboard. It's a JohnB.io certificate showing you've covered AI fundamentals through to deployment and ethics.
Can I take individual lessons without doing the whole course?
Yes. Every lesson is a standalone page you can read directly. If you just want to understand one topic - say, how transformers work or what MLOps means - go straight to that lesson. No account needed.
What topics does the course cover?
The course covers AI and machine learning fundamentals, supervised and unsupervised learning, deep learning and neural networks, NLP and large language models, Python and ML libraries, model evaluation and deployment, generative AI, AI ethics, and MLOps. Full curriculum is listed above.
What This AI Course Covers
  1. AI and machine learning fundamentals. Units 1 and 2 cover what AI actually is, how it differs from traditional software, the main subfields (machine learning, deep learning, NLP, computer vision, robotics), and the history behind modern AI systems.
  2. Core machine learning concepts. Supervised, unsupervised, and reinforcement learning. How models learn from data, how they generalise, and where they fail. Bias-variance tradeoff, overfitting, regularisation, and evaluation metrics explained clearly.
  3. Deep learning and neural networks. How multi-layer networks work, backpropagation, CNNs for image recognition, RNNs and LSTMs for sequences, and the transformer architecture that powers ChatGPT, Claude, and Gemini.
  4. Python and ML libraries. NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch - the core toolkit for any ML practitioner. Each lesson focuses on the concepts you need rather than turning into a coding tutorial.
  5. Generative AI, prompt engineering, and RAG. How LLMs work under the hood, how to write effective prompts, retrieval-augmented generation, fine-tuning, and the practical differences between the leading models.
  6. Model deployment, MLOps, and AI ethics. Taking models from notebook to production. CI/CD for ML, monitoring, data drift, responsible AI, bias in training data, governance frameworks, and the EU AI Act.
Key Points
  • 35 lessons across 9 structured units. The course follows a clear learning path from AI basics through to deployment and ethics - designed so each lesson builds on the last, but readable standalone if you just need one topic.
  • Free with optional progress tracking. All lesson content is publicly accessible. Create a free account to track which lessons you've completed, see your progress ring update, and unlock the final exam and certificate.
  • Each lesson includes audio and a quiz. You can listen to the lesson summary and test your understanding with a short quiz - all on the same page. Many lessons also include a deep dive podcast episode for going further.
  • Built for beginners, useful for practitioners. The early units assume no prior knowledge. By the later units - RAG, fine-tuning, MLOps - the content is detailed enough to be useful to people already working with AI tools.
  • Covers the tools and models you'll actually use. Rather than abstract theory, the course references ChatGPT, Claude, Gemini, PyTorch, TensorFlow, Hugging Face, and other real systems throughout.
Sources
  1. DeepLearning.AI - Andrew Ng's AI education platform. DeepLearning.ai. Accessed April 2026.
  2. PyTorch - Official documentation. PyTorch.org. Accessed April 2026.
  3. TensorFlow - Learning resources. TensorFlow.org. Accessed April 2026.
  4. Hugging Face - NLP course and model hub. HuggingFace.co. Accessed April 2026.
  5. Scikit-learn - User guide and API reference. Scikit-learn.org. Accessed April 2026.
  6. European Commission - EU AI Act regulatory framework. Digital-Strategy.ec.europa.eu. Accessed April 2026.
  7. Anthropic - AI safety and research. Anthropic.com. Accessed April 2026.
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