Machine learning
How models learn from data: training, validation, overfitting and the classic methods.
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Machine learning tutors
35 tutors
Imbalanced Classes, Handled Carefully
Imbalanced Classes, Handled Carefully
Model rare events like fraud or failures without tricks that quietly backfire86 lessonsSampleLukas Brenner$7Classification Metrics Beyond AccuracyClassification Metrics Beyond Accuracy
Read a confusion matrix and pick the metric that matches the cost of each mistake83 lessonsSampleLukas Brenner$4Linear Regression, Line by LineLinear Regression, Line by Line
Fit, read and question a linear regression so you know exactly what its numbers mean80 lessonsSampleKavya Raman$4Text classification from baseline to transformerText classification from baseline to transformer
Build text classifiers step by step, starting with a strong simple baseline and honest metrics74 lessonsSampleNadia Haddad$5Which Model When: Choosing an AlgorithmWhich Model When: Choosing an Algorithm
Pick a sensible model family for your data, constraints and goals, then test it fairly74 lessonsSampleKenta Arai$7Regression Metrics and Residual AnalysisRegression Metrics and Residual Analysis
Choose between MAE, RMSE and friends, then read residuals to find what your model misses73 lessonsSampleLukas Brenner$4End to End Tabular ML ProjectEnd to End Tabular ML Project
Take one tabular dataset from question to tested model to clear write up72 lessonsSampleLukas Brenner$9Feature Engineering for Tabular DataFeature Engineering for Tabular Data
Create features that help models learn, without leaking the answer72 lessonsSampleLin Zhao$7Model Interpretability and ExplanationsModel Interpretability and Explanations
Explain what drives a model's predictions, globally and case by case, without overclaiming64 lessonsSampleKojo Amankwah$7Time Series Forecasting BasicsTime Series Forecasting Basics
Forecast demand, traffic or sales with honest baselines, proper backtests and useful intervals62 lessonsSampleKenta Arai$8Framing a Problem for Machine LearningFraming a Problem for Machine Learning
Turn a vague business wish into a clear prediction task, or learn that it does not need ML at all60 lessonsSampleKavya RamanFreeGradient Boosting for Tabular DataGradient Boosting for Tabular Data
Understand, tune and debug boosted trees, the workhorse of structured data problems60 lessonsSampleKavya Raman$12Teachers who teach Machine learning
They wrote the lesson plans these tutors follow.
Nadia Haddad
Practical NLP: from tokens and embeddings to classification, translation and speechtokenisation, embeddings, text classification9 tutorsSampleLukas Brenner
Model evaluation you can trust: splits, metrics, leakage and tuningtrain validation test splits, cross validation, overfitting9 tutorsSampleKojo Amankwah
Experiments, causal questions and responsible models, explained for decision makersA/B testing, experiment design, statistical power9 tutorsSampleLin Zhao
Data cleaning, SQL, exploratory analysis and honest chartsdata cleaning, exploratory data analysis, SQL9 tutorsSampleMira Okafor
I teach how neural networks learn, one small worked example at a timeneural network fundamentals, activation and loss functions, backpropagation9 tutorsSampleKavya Raman
Classical machine learning models, worked through on paper before any codelinear and logistic regression, decision trees, random forests9 tutorsSampleMore in Machine learning
- Data science and statistics Clean data, explore it and draw conclusions you can defend. 30 tutors
- Math for AI The linear algebra, calculus and probability behind modern models, one idea at a time. 23 tutors
- Deep learning Neural networks from single neurons to full training loops, with worked examples. 28 tutors
- NLP Work with text: tokens, embeddings, classification, translation and speech. 14 tutors
- Computer vision Models that see: classification, detection, segmentation and their limits. 14 tutors
- Fine tuning and training Adapt a model to your task with good data and careful training runs. 12 tutors
- MLOps and deployment Ship models to production and keep them healthy: serving, monitoring, updates. 18 tutors