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Foundations course

Machine Learning

Start from zero. Every lesson begins with a small real example you can work out by hand, then shows the same idea in scikit-learn, with a narrated video for the trickiest step.

10 parts and 48 lessons planned. 48 lessons are ready now, each with code and a quiz.

  1. 1What Is Machine Learning?Video9 min
  2. 2AI vs ML vs DL vs Data ScienceVideo7 min
  3. 3Supervised and Unsupervised LearningVideo10 min
  4. 4Train and Test SplitVideo8 min
  1. 5Simple Linear RegressionVideo12 min
  2. 6Cost FunctionVideo10 min
  3. 7Gradient DescentVideo13 min
  4. 8R Squared and Adjusted R SquaredVideo9 min
  5. 9MSE, MAE and RMSE7 min
  6. 10Multiple Linear Regression7 min
  7. 11Polynomial Regression7 min
  8. 12Cross-ValidationVideo6 min
  1. 13Overfitting and UnderfittingVideo6 min
  2. 14Bias and Variance6 min
  3. 15Ridge Regression6 min
  4. 16Lasso Regression6 min
  5. 17ElasticNet Regression6 min
  6. 18Hyperparameter Tuning With GridSearchCV6 min
  7. 19Linear Regression Assumptions7 min
  1. 20Logistic RegressionVideo7 min
  2. 21Log Loss6 min
  3. 22Confusion Matrix6 min
  4. 23Precision, Recall and F-Beta6 min
  5. 24Logistic Regression in scikit-learn6 min
  6. 25One-vs-Rest Logistic Regression6 min
  1. 26Naive Bayes7 min
  2. 27Naive Bayes Worked Example7 min
  3. 28K Nearest Neighbours (KNN)6 min
  1. 29Decision Tree ClassifierVideo6 min
  2. 30Entropy and Gini Impurity6 min
  3. 31Information Gain6 min
  4. 32Decision Tree Regression and Pruning7 min
  5. 33Decision Tree in scikit-learn6 min
  1. 34Bagging and Boosting6 min
  2. 35Random Forest7 min
  3. 36AdaBoost6 min
  4. 37Gradient Boosting7 min
  5. 38XGBoost6 min
  1. 39Support Vector Machines (SVM)Video6 min
  2. 40Support Vector Regression (SVR)6 min
  3. 41SVM Kernels7 min
  1. 42K-Means ClusteringVideo7 min
  2. 43Hierarchical Clustering6 min
  3. 44Silhouette Score6 min
  4. 45DBSCAN7 min
  5. 46Curse of Dimensionality6 min
  6. 47Principal Component Analysis (PCA)7 min
  1. 48Machine Learning Workflow8 min