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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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Loss functions: what your model is minimising

Loss functions: what your model is minimising

Understand MSE, cross entropy and friends well enough to choose, read and debug themBeginnerDeep learning4.0(3)53 lessonsSample
Mira Okafor$5
Incident response for ML systems

Incident response for ML systems

Detect, contain and learn from ML failures, from silent quality drops to harmful outputsAll levelsMLOps and deployment4.7(3)51 lessonsSample
Malik Brennan$7
OCR and document understanding

OCR and document understanding

Understand how machines read scans, forms and tables, and how to check that they read correctlyBeginnerComputer vision4.3(3)51 lessonsSample
Noor Siddiqui$5
Transfer learning with pretrained models

Transfer learning with pretrained models

Get strong results from small datasets by starting with a model that has already learnedBeginnerComputer vision4.7(3)50 lessonsSample
Mateo Rojas$5
Activation functions explained

Activation functions explained

Learn what ReLU, sigmoid, tanh, GELU and softmax do, and choose the right one for each layerBeginnerDeep learning4.5(2)50 lessonsSample
Mira Okafor$4
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 lessonsSample
Mira Okafor$5
From Spreadsheets to Data Analysis

From Spreadsheets to Data Analysis

Bring your spreadsheet skills into structured, repeatable data analysisBeginnerData science and statistics4.3(3)50 lessonsSample
Lin Zhao$3
Matrix Multiplication Intuition

Matrix Multiplication Intuition

See matrix multiplication as many dot products and as a transformation of spaceBeginnerMath for AI4.7(3)50 lessonsSample
Katarzyna Wolska$5
Data Cleaning Fundamentals

Data Cleaning Fundamentals

Turn a messy table into data you can trust, with every change written downBeginnerData science and statistics4.7(3)49 lessonsSample
Lin Zhao$4
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 lessonsSample
Lukas BrennerFree
Versioning models and data

Versioning models and data

Know exactly which data and code produced every model, and recover or delete them when neededBeginnerMLOps and deployment4.7(3)48 lessonsSample
Malik Brennan$5
Neurons and layers, built up by hand

Neurons and layers, built up by hand

Compute a small neural network on paper so every layer, weight and shape makes senseBeginnerDeep learning4.7(3)46 lessonsSample
Mira Okafor$4