Machine Learning in Python

Detailed implementation of the Machine Learning Algorithms, and tuning them


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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Module 1: Machine Learning Module Contents

  • Machine learning 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
  • XGBoost and CatBoost


Cinque Terre
Prashant Sahu
Freelance Corporate Trainer for Data Analytics | AI & Machine Learning | Python | PhD @ IIT-Bombay

prashant.sahu@iitb.ac.in, prashant9501@gmail.com
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