Machine learning
How models learn from data: training, validation, overfitting and the classic methods.
35tutors
12teachers
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$3 to $13per paid lesson
Machine learning tutors
35 tutors
Eigenvalues, Eigenvectors and PCA
Eigenvalues, Eigenvectors and PCA
Derive principal component analysis from eigenvectors and use it with full understanding39 lessonsSampleKatarzyna Wolska$11Cross Validation Without Fooling YourselfCross Validation Without Fooling Yourself
Use k fold, grouped, time series and nested cross validation correctly34 lessonsSampleLukas Brenner$6Clustering Without GuessworkClustering Without Guesswork
Group data with k-means, hierarchical clustering and DBSCAN, and check the groups are useful33 lessonsSampleKatarzyna Wolska$4ML Engineering Interview PreparationML Engineering Interview Preparation
Practise ML fundamentals, coding, ML system design and behavioural rounds with realistic mock questions30 lessonsSampleYohannes Tesfaye$13Decision Trees You Can DrawDecision Trees You Can Draw
Build a decision tree by hand and understand every split it makes22 lessonsSampleKavya Raman$4Machine learning from zeroMachine learning from zero
The core ideas of machine learning, explained with small, concrete examples.Daniel Reyes$7Recommender Systems ExplainedRecommender Systems Explained
Understand how recommendations are made, evaluated and kept from narrowing what people seeKatarzyna Wolska$8Hyperparameter Tuning on a BudgetHyperparameter Tuning on a Budget
Search smarter, spend less compute and avoid overfitting your validation setLukas Brenner$11Overfitting and RegularisationOverfitting and Regularisation
Diagnose overfitting with learning curves and fix it with the right kind of regularisationLukas Brenner$6k Nearest Neighbours, Built by Handk Nearest Neighbours, Built by Hand
Predict by similarity and learn why distance, scaling and k decide everythingKavya Raman$3Logistic Regression for ClassificationLogistic Regression for Classification
Model yes or no outcomes, read odds ratios and choose thresholds with intentKavya Raman$6Teachers 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
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- 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
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- 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