Machine learning
How models learn from data: training, validation, overfitting and the classic methods.
35tutors
12teachers
3free to start
$3 to $13per paid lesson
Machine learning tutors
35 tutors
Naive Bayes Classifiers
Naive Bayes Classifiers
Build a fast text classifier from counts and Bayes rule, and know its blind spots58 lessonsSampleKavya Raman$3Data Science Interview PracticeData Science Interview Practice
Practise statistics, ML, SQL and case questions with structured feedback on your answers57 lessonsSampleKojo Amankwah$8Random Forests and BaggingRandom Forests and Bagging
Understand why averaging many trees works and how to tune a forest sensibly54 lessonsSampleKavya Raman$6Anomaly Detection in PracticeAnomaly Detection in Practice
Find unusual events in data without drowning your team in false alarms51 lessonsSampleKenta Arai$10Overfitting, regularisation and dropoutOverfitting, regularisation and dropout
Recognise overfitting from your curves and pick the right fix, from more data to dropout50 lessonsSampleMira Okafor$5Fairness Checks for ML ModelsFairness Checks for ML Models
Measure how a model treats different groups and understand the trade offs between fairness metrics50 lessonsSampleKojo Amankwah$7Loss Functions and What They RewardLoss Functions and What They Reward
Choose a loss that matches what you actually care about, and know what each one optimises50 lessonsSampleLeandro Ferraz$7Train, Validation and Test SplitsTrain, Validation and Test Splits
Split your data so your model's score means something outside your laptop48 lessonsSampleLukas BrennerFreeDimensionality Reduction in PracticeDimensionality Reduction in Practice
Use PCA, t-SNE and UMAP well, and avoid reading too much into pretty plots45 lessonsSampleKatarzyna Wolska$7Support Vector Machines ExplainedSupport Vector Machines Explained
See margins, support vectors and kernels clearly, then tune C and gamma with confidence45 lessonsSampleKavya Raman$7Data Leakage DetectiveData Leakage Detective
Find the hidden leaks that make models look brilliant in testing and fail in production43 lessonsSampleLukas Brenner$10Reinforcement learning basicsReinforcement learning basics
Understand agents, rewards and policies, and compute a Q learning update yourself41 lessonsSampleNikolai SorinFreeTeachers 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