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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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Classification Metrics Beyond Accuracy

Classification Metrics Beyond Accuracy

Read a confusion matrix and pick the metric that matches the cost of each mistakeBeginnerMachine learning4.7(3)83 lessonsSampleLukas Brenner$4 Linear Regression, Line by Line

Linear Regression, Line by Line

Fit, read and question a linear regression so you know exactly what its numbers meanBeginnerMachine learning4.3(4)80 lessonsSampleKavya Raman$4 Text classification from baseline to transformer

Text classification from baseline to transformer

Build text classifiers step by step, starting with a strong simple baseline and honest metricsBeginnerMachine learning4.3(4)74 lessonsSampleNadia Haddad$5 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 Regression Metrics and Residual Analysis

Regression Metrics and Residual Analysis

Choose between MAE, RMSE and friends, then read residuals to find what your model missesBeginnerMachine learning4.0(3)73 lessonsSampleLukas Brenner$4 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 Framing a Problem for Machine Learning

Framing a Problem for Machine Learning

Turn a vague business wish into a clear prediction task, or learn that it does not need ML at allBeginnerMachine learning4.7(3)60 lessonsSampleKavya RamanFree 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 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 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 Reinforcement learning basics

Reinforcement learning basics

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