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1. Logistic Regression: https://github.com/amitmse/in_Python_/tree/master/Logistic%20Regression#readme

        - Statistical Derivation of Logistic Regression
        - Logistic Regression Algorithm Coded in Python
        - Brief of Gradient Descent
        - Computation of Metrics
   

2. Decision Tree: https://github.com/amitmse/in_Python_/tree/master/Decision%20Tree#readme

        - Explain Decision Tree Algorithms
        - Exmaple: Test Run 
   

3. Random Forest: https://github.com/amitmse/in_Python_/tree/master/Random_Forest#readme

        - Explain Random Forest Algorithms
        - Exmaple: Test Run 
   

4. Naive Bayes: https://github.com/amitmse/in_Python_/tree/master/Naive%20Bayes#readme

        - Explain Naive Bayes Algorithms
   

5. Boosting (XGboost, ADA & GBM): https://github.com/amitmse/in_Python_/tree/master/Boosting#readme

        - Explain Boosting Algorithms
        - Exmaple: Test Run 
   

6. Neural Network: https://github.com/amitmse/in_Python_/tree/master/Neural%20Network#readme

        - Explain Neural Network Algorithms
        - Computation of Neural Network in excel
   

7. Cluster Analysis: https://github.com/amitmse/in_Python_/tree/master/Cluster%20Analysis#readme

        - Explain Cluster Analysis Algorithms
        - K means
        - Hierarchical Clustering      
   

8. Data Preparation: https://github.com/amitmse/in_Python_/tree/master/Data%20Prep#readme

       - Basic SAS functions coded in python using Pandas
       - Distributed computing: Dask vs Spark (Pyspark)
       - Exploratory Data Analysis
       - Computation of Information Value
       - Charts in Python
   

9. Basic statistics: https://github.com/amitmse/in_Python_/blob/master/Others/README.md

       - Probability Distribution
       - Assumptions of Ordinary Least Squares
       - Computation of Model Metrics
       - Explain Gradient Descent    
   

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