COURSE AUTHOR –
1. Learn how to solve real life problem using the Linear and Logistic Regression technique
2. Preliminary analysis of data using Univariate and Bivariate analysis before running regression analysis
3. Understand how to interpret the result of Linear and Logistic Regression model and translate them into actionable insight
4. Indepth knowledge of data collection and data preprocessing for Linear and Logistic Regression problem
5. Basic statistics using Numpy library in Python
6. Data representation using Seaborn library in Python
7. Linear Regression technique of Machine Learning using Scikit Learn and Statsmodel libraries of Python
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