Machine Learning Module Contents
- Machine Detailed implementLearning Project Flow
- Regularization Techniques in MAchine learning
- Outlier Detection methods in detail
- Feature Scaling Techniques in detail
- Techniques for Encoding Categorical Data
- Handling Multi-Collinearity in Data
- Feature Extraction methods
- Feature Selection Methods
- More on Classification & Regression metrics
- Deep-dive into SVM & Decision Trees
- Hyper-parameter Tuning
- Clustering Algorithms in detail
- Dimensionality Reduction: PCA vs LDA
- Ensemble methods: Boosting & Bagging implementation
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