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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 Intermediate Clear all

Imbalanced Classes, Handled Carefully

Imbalanced Classes, Handled Carefully

Model rare events like fraud or failures without tricks that quietly backfireIntermediateMachine learning4.5(4)86 lessonsSampleLukas Brenner$7 Which Model When: Choosing an Algorithm

Which Model When: Choosing an Algorithm

Pick a sensible model family for your data, constraints and goals, then test it fairlyAll levelsMachine learning4.3(3)74 lessonsSampleKenta Arai$7 End to End Tabular ML Project

End to End Tabular ML Project

Take one tabular dataset from question to tested model to clear write upAll levelsData science and statistics4.3(4)72 lessonsSampleLukas Brenner$9 Feature Engineering for Tabular Data

Feature Engineering for Tabular Data

Create features that help models learn, without leaking the answerIntermediateMachine learning4.3(3)72 lessonsSampleLin Zhao$7 Model Interpretability and Explanations

Model Interpretability and Explanations

Explain what drives a model's predictions, globally and case by case, without overclaimingIntermediateMachine learning4.3(3)64 lessonsSampleKojo Amankwah$7 Time Series Forecasting Basics

Time Series Forecasting Basics

Forecast demand, traffic or sales with honest baselines, proper backtests and useful intervalsIntermediateData science and statistics4.3(3)62 lessonsSampleKenta Arai$8 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 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 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