Python
Write reliable Python for data and AI: functions, collections, files, debugging and complete small projects.
7 articles
Free AI learning resources
Everything you can read on SchoolWhool without paying: the terms, one-page references, short answers, longer worked examples, and the interview questions and projects from every module.
Explore by subject
Follow a subject from its foundations to worked examples, practical decisions and projects.
Data Tools
Work with NumPy arrays, pandas tables, visualizations and scikit-learn pipelines using checked examples.
10 articles
Maths
Understand the geometry, calculus and numerical methods behind machine learning through worked calculations.
6 articles
Statistics
Interpret data, uncertainty and evidence with worked examples, probability calculations and simulations.
7 articles
Machine Learning
Frame modeling problems, build baselines, evaluate predictions and diagnose errors on real workflows.
23 articles
Deep Learning
Understand neural networks, PyTorch, computer vision and training behavior through reproducible examples.
9 articles
Generative AI
Build reliable LLM workflows with retrieval, validation and evaluation grounded in inspectable examples.
9 articles
Resources
AI applications
See how AI is used in your field, with sourced examples and relevant Machine Learning, Deep Learning and Generative AI courses.
AI glossary
Every key term the lessons introduce, defined in plain words and linked to the lesson that teaches it.
853 terms
Cheat sheets
One page per course module: the terms, rules and common mistakes, with the output each rule produces.
27 sheets, 5 free to read
Guides
Direct answers to common questions, such as what deep learning is or how to choose a machine learning course.
13 guides
Articles
Worked examples, beginner projects with complete code and learning plans, written by Gaurav Jain.
71 articles
Interview questions
The questions interviewers ask on each module's topics, from first principles to the follow-ups.
515 questions across 27 modules
Projects
A project for each module, set as a problem to solve on your own and checked against test values or a notebook.
24 projects, 11 in the free sample
Which one to use
- Meet a word you do not know
- Look it up in the AI glossary.
- Want a question answered quickly
- Read a guide: each starts with the short answer.
- Want to work through an example
- Read an article, with code you can run.
- Revising a module
- Use its cheat sheet and its interview questions.
Interview questions, by module
The questions are free to read. Model answers open with each module's material: with a free account in the free sample, and with Pro for the rest.
Module projects
Each module ends in a project you solve on your own, with no template. Projects in the free sample open with a free account; the rest are part of Pro.
Python
- Bill SplitterModule 1: Getting StartedFree sample
- Loan Repayment ScheduleModule 2: Control FlowFree sample
- The Most Common Words in Product ReviewsModule 3: Data StructuresFree sample
- The Word Counter, as FunctionsModule 4: FunctionsFree sample
- Review-stats, FinishedModule 5: Organising CodeFree sample
- Tiny-modelsModule 6: Objects and ClassesPro
- Log-toolsModule 7: Time and Space ComplexityPro
Maths
- One More Prediction, TracedModule 0: Why Maths MattersFree sample
- Reading Ridge and LassoModule 1: Functions, Logarithms and NotationFree sample
- Nearest Cars, Raw and StandardisedModule 2: VectorsPro
- PCA by Hand on the LotModule 3: MatricesPro
- Linear Regression on the Lot, Three WaysModule 4: Calculus and OptimisationPro
- Naive Bayes from Products and from Sums of LogsModule 5: Numbers on a ComputerPro
Statistics
- A Dispatcher Decision BriefModule 0: Why Statistics MattersFree sample
- A One-Page Retail Invoice ReportModule 1: Describing Data and Reading Statistical PlotsFree sample
- A One-Page, Training-Only Credit EDA ReportModule 2: EDA, Evidence and Misleading StatisticsPro
- A One-Page Fraud Review-Queue BriefModule 3: ProbabilityPro
- A One-Page Retail Sampling-and-Value BriefModule 4: Distributions and DependencePro
- A One-Page Retail Uncertainty BriefModule 5: EstimationPro
Machine Learning
- Better Than 25 mpg, From One ThresholdModule 1: What Machine Learning IsFree sample
- A Data Report for UCI Online Shoppers Purchasing IntentionModule 2: Exploring a Dataset for ModellingPro
- A Fuel-Economy Model, Built and DefendedModule 3: Linear Regression and Gradient DescentPro
- A Model-Selection ReportModule 4: The First Classifier, Generalisation and RegularisationPro
Learn it in order
The courses take each idea from the intuition and the maths to code you write yourself. Open lessons' parts and key terms are free to read, with no account.