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What you’ll learn
Learn how to solve real life problem using the Linear and Logistic Regression technique
Preliminary analysis of data using Univariate and Bivariate analysis before running regression analysis
Understand how to interpret the result of Linear and Logistic Regression model and translate them into actionable insight
Indepth knowledge of data collection and data preprocessing for Linear and Logistic Regression problem
Basic statistics using Numpy library in Python
Data representation using Seaborn library in Python
Linear Regression technique of Machine Learning using Scikit Learn and Statsmodel libraries of Python

Identify the business problem which can be solved using linear and logistic regression technique of Machine Learning.

Create a linear regression and logistic regression model in Python and analyze its result.

Confidently model and solve regression and classification problems

Section 1 – Basics of StatisticsThis section is divided into five different lectures starting from types of data then types of statisticsthen graphical representations to describe the data and then a lecture on measures of center like meanmedian and mode and lastly measures of dispersion like range and standard deviation

Section 2 – Python basicThis section gets you started with Python.This section will help you set up the python and Jupyter environment on your system and it’ll teachyou how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.

Section 3 – Introduction to Machine LearningIn this section we will learn – What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.

Section 4 – Data PreprocessingIn this section you will learn what actions you need to take a step by step to get the data and thenprepare it for the analysis these steps are very important.We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do univariate analysis and bivariate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation.

Section 5 – Regression ModelThis section starts with simple linear regression and then covers multiple linear regression.We have covered the basic theory behind each concept without getting too mathematical about it so that youunderstand where the concept is coming from and how it is important. But even if you don’t understandit, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem.
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 People pursuing a career in data science
 Working Professionals beginning their Data journey
 Statisticians needing more practical experience
 Anyone curious to master Linear and Logistic Regression from beginner to advanced level in a short span of time
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