Skip to content

SamplePreview build: teacher profiles, ratings, reviews and lesson counts are sample data.

Log in Find a tutor

Find a tutor

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

Filters

Sort by

Price

Any price Free Paid

Level

Any level Beginner Intermediate Advanced

Topic

All topics

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

Clear

149 tutors

All Machine learning Clear all

Monitoring models and catching drift

Monitoring models and catching drift

Notice when a live model starts getting worse, even before the true answers arriveIntermediateMLOps and deployment4.7(3)58 lessonsSampleMalik Brennan$9 Expected Value and Variance, Gently

Expected Value and Variance, Gently

Understand averages of uncertain outcomes and how much they spread, with dice, games and decisionsBeginnerMath for AI4.3(3)58 lessonsSampleKenta Arai$4 Experiment tracking you will actually use

Experiment tracking you will actually use

Log runs so you can compare, reproduce and explain results months later, with any toolBeginnerMLOps and deployment4.3(3)58 lessonsSampleMalik Brennan$4 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 Batching and caching for model inference

Batching and caching for model inference

Serve more requests on the same hardware by batching smartly and caching what can be reusedIntermediateMLOps and deployment4.7(3)57 lessonsSampleMagnus Eriksen$9 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 Optimisers: SGD, momentum and Adam

Optimisers: SGD, momentum and Adam

Choose and tune optimisers and learning rate schedules with understanding instead of guessworkIntermediateDeep learning4.7(3)56 lessonsSampleMira Okafor$8 Question answering, from extractive to generative

Question answering, from extractive to generative

Understand how QA systems find, read and generate answers, and how to tell when they should abstainIntermediateNLP4.7(3)56 lessonsSampleMateo Rojas$7 Convolutional networks from the pixel up

Convolutional networks from the pixel up

See how convolutions turn pixels into features, and calculate shapes and parameters yourselfBeginnerComputer vision4.7(3)56 lessonsSampleNikolai Sorin$5 Tokenisation: how text becomes numbers

Tokenisation: how text becomes numbers

See how models split text into tokens, why it matters for cost and context, and where it trips upBeginnerHow language models work4.7(3)56 lessonsSampleNadia HaddadFree GPUs and AI hardware for beginners

GPUs and AI hardware for beginners

Understand what GPUs do for AI and estimate whether a model will fit on your hardwareBeginnerMLOps and deployment4.7(3)55 lessonsSampleMagnus EriksenFree Reading Maths Notation in AI Papers

Reading Maths Notation in AI Papers

Translate the symbols in machine learning papers into plain words and small examplesAll levelsMath for AI4.7(3)55 lessonsSampleKatarzyna Wolska$6