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

Confidence Intervals, Clearly

Confidence Intervals, Clearly

Build, read and explain confidence intervals without the usual misunderstandingsIntermediateData science and statistics4.3(3)66 lessonsSampleLina Khoury$6 Evaluating a model after fine tuning

Evaluating a model after fine tuning

Prove a tuned model is actually better, on your task and everywhere else it mattersIntermediateEvaluation and testing4.7(3)65 lessonsSampleNeha Varadan$8 Planning the cost of a training run

Planning the cost of a training run

Estimate compute, time, memory and budget for a training or fine tuning run before you spendAll levelsFine tuning and training4.3(3)65 lessonsSampleMagnus Eriksen$7 Survey Data: Design to Analysis

Survey Data: Design to Analysis

Write better questions, weight responses sensibly and report survey results honestlyAll levelsData science and statistics3.7(3)65 lessonsSampleLina Khoury$6 Descriptive Statistics, First Steps

Descriptive Statistics, First Steps

Summarise any set of numbers honestly with averages, spread and shapeBeginnerData science and statistics4.7(3)65 lessonsSampleLina KhouryFree Your first fine tune, start to finish

Your first fine tune, start to finish

Run one small, honest fine tuning project end to end and decide whether it was worth doingBeginnerFine tuning and training4.7(3)64 lessonsSampleNeha Varadan$6 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 Designing Experiments: Power, Units and Blocking

Designing Experiments: Power, Units and Blocking

Plan experiments that can actually detect the effects you care aboutIntermediateData science and statistics4.7(3)64 lessonsSampleKojo Amankwah$7 Image classification, first principles

Image classification, first principles

Plan, train and honestly evaluate an image classifier, from defining classes to studying its mistakesBeginnerComputer vision4.7(3)64 lessonsSampleNoor SiddiquiFree Maximum Likelihood, Step by Step

Maximum Likelihood, Step by Step

Derive estimates by maximising likelihood and see why common losses are likelihoods in disguiseIntermediateMath for AI4.7(3)63 lessonsSampleKenta Arai$7 MLOps for a team of one

MLOps for a team of one

Put a model into use responsibly with the few practices that matter when you work aloneBeginnerMLOps and deployment4.7(3)62 lessonsSampleMalik BrennanFree 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