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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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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 Quantisation: smaller, faster models

Quantisation: smaller, faster models

Shrink models with lower precision numbers and measure exactly what quality you trade awayIntermediateFine tuning and training4.3(3)49 lessonsSampleMagnus Eriksen$9 Summarisation systems and their failure modes

Summarisation systems and their failure modes

Build and judge summaries that stay faithful to the source, from short notes to long reportsAll levelsNLP4.7(3)46 lessonsSampleMateo Rojas$6 Deploying an open weights model

Deploying an open weights model

Choose, size, secure and run an open weights model in production, and compare its real costIntermediateMLOps and deployment4.7(3)46 lessonsSampleMagnus Eriksen$9 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 Hypothesis Tests and P Values Done Right

Hypothesis Tests and P Values Done Right

Run tests, read p values correctly and report effects that actually matterIntermediateData science and statistics4.7(3)45 lessonsSampleLina Khoury$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 How to read a deep learning paper

How to read a deep learning paper

Read papers in passes, find the real claim and judge the evidence behind itAll levelsAI for research and study4.7(3)41 lessonsSampleNikolai Sorin$6 Image segmentation: semantic, instance, panoptic

Image segmentation: semantic, instance, panoptic

Label every pixel correctly: understand the three kinds of segmentation, their models and metricsIntermediateComputer vision4.3(3)40 lessonsSampleNoor Siddiqui$8 Which Maths Do You Need for ML?

Which Maths Do You Need for ML?

Build a realistic maths study plan matched to the kind of AI work you want to doAll levelsMath for AI4.3(3)36 lessonsSampleLeandro Ferraz$5 Cross Validation Without Fooling Yourself

Cross Validation Without Fooling Yourself

Use k fold, grouped, time series and nested cross validation correctlyIntermediateMachine learning4.5(2)34 lessonsSampleLukas Brenner$6