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657 tutors in 31 topics, built by 75 teachers. Each one follows a lesson plan its teacher wrote.

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Foundations 89

All Foundations89 AI basics25 How language models work23 AI safety and ethics49

Everyday AI 91

All Everyday AI91 Prompting20 ChatGPT and Claude at work25 AI for writing29 AI for research and study33

Building with AI 162

All Building with AI162 Python for AI21 Building with LLM APIs30 RAG and search21 Evaluation and testing21 AI agents31 AI automation25 AI coding assistants30

Machine learning 149

All Machine learning149 Machine learning35 Data science and statistics30 Math for AI23 Deep learning28 NLP14 Computer vision14 Fine tuning and training12 MLOps and deployment18

Creative AI 77

All Creative AI77 Image and video AI58 Audio and voice AI12 AI for design33

AI at work 145

All AI at work145 AI for business34 AI for marketing and sales23 AI for product and operations28 AI for education28 AI for professionals29 AI careers18

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Machine learning Clear all

Naive Bayes Classifiers

Naive Bayes Classifiers

Build a fast text classifier from counts and Bayes rule, and know its blind spotsBeginnerMachine learning4.0(3)58 lessonsSampleKavya Raman$3 Data Science Interview Practice

Data Science Interview Practice

Practise statistics, ML, SQL and case questions with structured feedback on your answersAll levelsData science and statistics4.3(3)57 lessonsSampleKojo Amankwah$8 Random Forests and Bagging

Random Forests and Bagging

Understand why averaging many trees works and how to tune a forest sensiblyIntermediateMachine learning4.3(3)54 lessonsSampleKavya Raman$6 Anomaly Detection in Practice

Anomaly Detection in Practice

Find unusual events in data without drowning your team in false alarmsAdvancedMachine learning4.7(3)51 lessonsSampleKenta Arai$10 Overfitting, regularisation and dropout

Overfitting, regularisation and dropout

Recognise overfitting from your curves and pick the right fix, from more data to dropoutBeginnerDeep learning4.7(3)50 lessonsSampleMira Okafor$5 Fairness Checks for ML Models

Fairness Checks for ML Models

Measure how a model treats different groups and understand the trade offs between fairness metricsIntermediateMachine learning4.7(3)50 lessonsSampleKojo Amankwah$7 Loss Functions and What They Reward

Loss Functions and What They Reward

Choose a loss that matches what you actually care about, and know what each one optimisesIntermediateMachine learning4.3(3)50 lessonsSampleLeandro Ferraz$7 Train, Validation and Test Splits

Train, Validation and Test Splits

Split your data so your model's score means something outside your laptopBeginnerMachine learning4.7(3)48 lessonsSampleLukas BrennerFree Dimensionality Reduction in Practice

Dimensionality Reduction in Practice

Use PCA, t-SNE and UMAP well, and avoid reading too much into pretty plotsIntermediateMachine learning4.3(3)45 lessonsSampleKatarzyna Wolska$7 Support Vector Machines Explained

Support Vector Machines Explained

See margins, support vectors and kernels clearly, then tune C and gamma with confidenceIntermediateMachine learning4.5(2)45 lessonsSampleKavya Raman$7 Data Leakage Detective

Data Leakage Detective

Find the hidden leaks that make models look brilliant in testing and fail in productionAdvancedMachine learning4.7(3)43 lessonsSampleLukas Brenner$10 Reinforcement learning basics

Reinforcement learning basics

Understand agents, rewards and policies, and compute a Q learning update yourselfBeginnerDeep learning4.7(3)41 lessonsSampleNikolai SorinFree