MyPhatshala

Machine Learning

Teach computers to learn patterns from data, with scikit-learn.

Intermediate8 modules33 lessonsFree · no sign-up
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Syllabus

01 IntroductionWhat ML is. 4 lessons
  1. 1.1What is machine learning?Coming soon
  2. 1.2Types: supervised, unsupervised, reinforcementComing soon
  3. 1.3The ML workflowComing soon
  4. 1.4Setting up scikit-learnComing soon
02 Data PreparationGetting data model-ready. 4 lessons
  1. 2.1Train-test splitComing soon
  2. 2.2Feature scalingComing soon
  3. 2.3Feature selectionComing soon
  4. 2.4Handling imbalanced dataComing soon
03 RegressionPredicting numbers. 4 lessons
  1. 3.1Linear regressionComing soon
  2. 3.2Multiple and polynomial regressionComing soon
  3. 3.3Regularisation: Ridge and LassoComing soon
  4. 3.4Regression metrics: MAE, MSE, R²Coming soon
04 ClassificationPredicting categories. 6 lessons
  1. 4.1Logistic regressionComing soon
  2. 4.2K-nearest neighboursComing soon
  3. 4.3Naive BayesComing soon
  4. 4.4Support vector machinesComing soon
  5. 4.5Decision treesComing soon
  6. 4.6Classification metrics: confusion matrix, precision, recall, F1, ROCComing soon
05 Ensemble MethodsMany models are better than one. 3 lessons
  1. 5.1Bagging and random forestComing soon
  2. 5.2Boosting: AdaBoost and gradient boostingComing soon
  3. 5.3XGBoost and LightGBMComing soon
06 Unsupervised LearningFinding hidden structure. 5 lessons
  1. 6.1K-means clusteringComing soon
  2. 6.2Hierarchical clusteringComing soon
  3. 6.3DBSCANComing soon
  4. 6.4PCA (dimensionality reduction)Coming soon
  5. 6.5Association rulesComing soon
07 Model TuningGetting the best model. 4 lessons
  1. 7.1Bias-variance trade-offComing soon
  2. 7.2Cross-validationComing soon
  3. 7.3Hyperparameter tuning: grid and random searchComing soon
  4. 7.4Pipelines in scikit-learnComing soon
08 ProjectsEnd-to-end ML. 3 lessons
  1. 8.1House price predictionComing soon
  2. 8.2Customer churn predictionComing soon
  3. 8.3Saving and deploying a modelComing soon