Tutorials

Below is a collection of free / open tutorials and tutorial notebooks at the interface between chemistry, materials and machine learning / AI.

Foundations

• 3Blue1Brown Neural Networks Video Series
  Beginner

  Visual and intuitive video series explaining neural networks and deep learning concepts with minimal math.

  Prior knowledge: Basic calculus, linear algebra
  Estimated time: 5 hours

  neural networksvideovisual explanations

• LLM Visualization
  Intermediate

  Interactive 3D visualisation of a GPT-style large language model, showing every layer and operation during inference. You can explore the full LLM computation pipeline step by step in 3D, making the inner workings of LLMs much more tangible.

  Prior knowledge: Basic understanding of neural networks and transformers is helpful, plus some linear algebra intuition
  Estimated time: 1–2 hours

  large language models3D visualizationinteractive tutorial

• A Gentle Introduction to Graph Neural Networks
  Beginner To Intermediate

  Interactive Distill article that introduces graph neural networks from first principles, with animations, visual explanations, and code snippets. Walks through graph data, message passing, and the components of a modern GNN in an intuitive, experimentable way.

  Prior knowledge: Familiarity with graphs and adjacency matrices
  Estimated time: 2–4 hours

  graph neural networksinteractive articlevisual explanation

• Machine Learning for Everyone (In Simple Words)
  Beginner

  Long-form blog tutorial explaining machine learning in absolute basic, simple words, with real-world analogies and zero formal math. Focuses on intuitions, everyday examples, and plain language rather than equations, making it accessible to non-technical readers.

  Prior knowledge: None; general curiosity about machine learning
  Estimated time: 1–2 hours

  machine learning basicsnon-technicalreal-world examples

• The Python Tutorial (Official Documentation)
  Beginner

  The official Python tutorial, maintained as part of the core Python documentation. Covers all essential Python concepts, from basic syntax to modules, classes, I/O, and error handling. A major advantage is that it is always updated for the very latest Python version.

  Prior knowledge: None; suitable for first-time programmers
  Estimated time: 10–20 hours (full read-through with exercises)

  python basicsofficial docsprogramming fundamentals

• Software Carpentry Lessons
  Beginner

  Collection of core Software Carpentry lessons teaching essential research computing skills: the Unix shell, version control with Git, and programming with Python or R. Designed as hands-on workshop material with exercises and instructor notes.

  Prior knowledge: Basic familiarity with files/folders; no prior coding experience required
  Estimated time: 10–20 hours (Unix shell, Git, and Python/R lessons)

  research computingshell/git/pythonhands-on lessons

• Homemade Machine Learning
  Beginner To Intermediate

  Collection of popular machine learning algorithms implemented from scratch in Python, with the underlying mathematics explained. Each algorithm is accompanied by interactive Jupyter Notebook demos so you can tweak data and hyperparameters and immediately see predictions and visualisations.

  Prior knowledge: Python (NumPy), basic calculus, linear algebra, and introductory ML concepts
  Estimated time: 15–30 hours (working through demos and notebooks)

  from-scratch implementationsjupyter notebooksclassic ML algorithms

Chemistry

• Reinforcement Learning for ChemEng
  Intermediate To Advanced

  Educational reinforcement learning implementation with tutorial notebooks aimed at chemical engineering applications.

  Prior knowledge: Chemical engineering, reinforcement learning basics, Python
  Estimated time: 5–10 hours (notebook tutorials)

  reinforcement learningchemical engineeringnotebooks

• Is Life Worth Living? — Cheminformatics Blog by @iwatobipen
  Beginner To Intermediate

  Blog covering cheminformatics topics such as RDKit, molecular similarity, data-pipelines, and workflows. Includes code snippets, practical examples, and explains tools in clear terms.

  Prior knowledge: Basic chemistry and Python; interest in cheminformatics
  Estimated time: Varies (many short posts, individual topics)

  cheminformaticsRDKitpython workflows

• Introduction to Python for Chemists (Imperial College)
  Beginner

  Introductory Python course starting at very basics tailored to chemists: basic syntax, data handling, and chemical-data examples. Contains Jupyter notebooks and worked examples to help chemists get started coding.

  Prior knowledge: High-school chemistry, basic mathematics
  Estimated time: ≈5–10 hours (notebooks + exercises)

  chemistrypython basicsnotebooks

Materials