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Module 03
Core Machine Learning
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Submodule 01Data Preparation & Feature Engineering0/21
- 1Random Shuffle of DatasetEasy
- 2Batch Iterator for DatasetEasy
- 3Generate Random Subsets of a DatasetMed.
- 4One-Hot Encoding of Nominal ValuesEasy
- 5Feature Scaling ImplementationEasy
- 6Generate Sorted Polynomial FeaturesMed.
- 7Phi Transformation for Polynomial FeaturesEasy
- 8TF-IDF (Term Frequency-Inverse Document Frequency)Med.
- 9Handle Missing Data with ImputationMed.
- 10Stratified Train-Test SplitMed.
- 11Data Quality Scoring for ML PipelinesMed.
- 12Label Encoding for Ordinal VariablesEasy
- 13Min-Max Scaling of Feature ValuesEasy
- 14Handle Imbalanced Data with SMOTEMed.
- 15Outlier Detection and Removal Using IQR MethodconceptMed.
- 16Focal Loss for Imbalanced ClassificationMed.
- 17Dollar Bars SamplingHard
- 18Tick Bars SamplingMed.
- 19Volume Bars SamplingconceptMed.
- 20Feature Density and Approximation QualityMed.
- 21Shift and Scale Array to Target RangeconceptEasy
Submodule 02Linear Models0/11
- 1Linear Regression Using Normal EquationEasy
- 2Linear Regression Using Gradient DescentEasy
- 3Ridge Regression Loss FunctionEasy
- 4Lasso Regression using ISTAHard
- 5Elastic Net Regression via Gradient DescentMed.
- 6Binary Classification with Logistic RegressionEasy
- 7Train Logistic Regression with Gradient DescentHard
- 8R-squared for Regression AnalysisconceptEasy
- 9Gaussian Process for RegressionconceptHard
- 10Train Softmax Regression with Gradient DescentHard
- 11Linear Regression - Power Grid OptimizationMed.
Submodule 03Tree Models & Ensembles0/15
- 1Decision Tree LearningHard
- 2The Best Gini-Based Split for a Binary Decision TreeMed.
- 3Gini Impurity Calculation for a Set of ClassesconceptMed.
- 4Divide Dataset Based on Feature ThresholdMed.
- 5AdaBoost Fit MethodconceptMed.
- 6Decision Tree Pruning with Cost-ComplexityHard
- 7Decision Tree for RegressionMed.
- 8Entropy-based Split SelectionMed.
- 9Random Forest Feature ImportanceMed.
- 10Bagging Classifier from ScratchMed.
- 11Hard Voting ClassifierMed.
- 12Soft Voting ClassifierMed.
- 13Stacking ClassifierMed.
- 14XGBoost Objective Function CalculationconceptMed.
- 15Out-of-Bag Score CalculationconceptMed.
Submodule 04Instance-Based, Kernel & Probabilistic Methods0/10
- 1K-Nearest NeighborsMed.
- 2Gaussian Naive Bayes ClassifierMed.
- 3Linear Kernel FunctionEasy
- 4Pegasos Kernel SVM ImplementationHard
- 5SVM Margin WidthMed.
- 6Hinge Loss for SVMMed.
- 7Polynomial Kernel FunctionMed.
- 8RBF (Gaussian) Kernel FunctionMed.
- 9Apriori Frequent Itemset MiningMed.
- 10Optimal String Alignment DistanceconceptMed.