Practical Machine Learning

Introduction Introduction Introduction to Python, Numpy, Pandas Test 1 [solution]
Data Exploration Feature Engineering Extract Data to DataFrame, Scaling, Transformation, Selection, Introduction to Sklearn Test 2 [solution]
Regression Linear Regression Overfitting & Regularization (L1, L2), Split Data, Evaluate (RMSE and R2) Test 3 [solution]
Classification Logistic Regression Nearest Neighbour, Evaluation (Accuracy, Confusion Matrix)
Classification SVM SVM (Linear vs Nonlinear), Decision Trees, Split data (k-Fold Cross Validation) Model Selection Test 4 [solution]
Clustering Clustering K-Means, Nearest Neighbor Test 5 [solution]
Dimensionality Reduction Dimensionality Reduction PCA, SVD Test 6 [solution]
Advanced Experiments Advanced Experiments Imbalanced Data, Undersampling, Oversampling Evaluation, F1 (Recall & Precision), ROCGridSearch (Hyperparameters) Test 7 [solution]
Ensemble Learning Ensemble Learning Bagging, Voting, Stacking, RandomForest Boosting, Xgboost Test 8 [solution]
Natural Language Processing Natural Language Processing Feature Extraction, Bag of Words, N-grams, Features Transformation, Hashing, TF-IDF, Text classification
Natural Language Processing Natural Language Processing Word Embedding Similarity Measures, Text classification Test 9 [solution]
Anomaly Detection Anomaly Detection OneClass SVM, Isolation Forest Test 10 [solution]