•Linear Models
•The Course Overview
•Introduction to Linear Regression
•Various concepts around Linear Regression
•Using Linear Regression for prediction
•Advantages and Limitations of Linear Regression
•Case Study Linear Regression
•Introduction to Logistic Regression
•Various Concepts around Logistic Regression
•How Logistic Regression Can Be Used for MultiClass Classification
•Advantages and Limitations of Logistic Regression
•Case Study Logistic Regression
•Homework Assignment Linear Models
•Decision Tree Algorithm
•Introduction to Decision Tree
•Concepts Various Decision Tree Algorithms
•Various Components of Decision Tree
•Advantages and Disadvantages of Decision Tree Algorithm
•Case Study IBMs HR Attrition Data
•Homework Assignment Decision Tree Algorithm
•Random Forest Algorithm
•Introduction to Random Forest Algorithm
•Concepts of Random Forest Algorithm
•Various components of Random Forest Algorithm
•Advantages and Disadvantages of Random Forest Algorithm
•Homework Assignment Random Forest Algorithm
•KMeans Clustering Algorithm
•Introduction to KMeans Clustering
•Concepts of KMeans Clustering Algorithm
•Different Clustering Methods
•Advantages and Disadvantages of KMeans Clustering Algorithm
•Case Study Iris Dataset
•Homework Assignment KMeans Clustering Algorithm
•KNearest Neighbors Algorithm
•Introduction to KNN Algorithm
•Concepts of KNN Algorithm
•Advantages and Limitations of KNN Algorithm
•Case Study Income Census Dataset
•Homework Assignment KNN Algorithm
•Naïve Bayes Algorithm
•Introduction to Naïve Bayes Algorithm
•Concepts of Naïve Bayes Algorithm
•Advantages and Limitations of Naïve Bayes Algorithm
•Case Study Bank Marketing Dataset
•Homework Assignment Naïve Bayes Algorithm
•Time Series Analysis
•Introduction to Time Series Analysis
•Various Concepts around Time Series Model
•Full overview of ARIMA SARIMA Model
•Forecast Accuracy Measure Time Series Analysis
•Case Study CPI Inflation Dataset
•Homework Assignment Time Series Analysis
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