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, a quiz, and a deep dive podcast. You can listen to the lesson summary, test your understanding with a short quiz, and go deeper with a longer podcast episode — all on the same page.
  • 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.