Master the basics of Machine Learning in Python and use

**Regression Models to solve business problems**This is very good, i love the fact the all explanation given can be understood by a layman

Joshua

Good introductory course to Machine Learning. It is true that a lot of the time is spent in setting up the data. This course covers how to do that too. Well done

Hetal Patel

Concepts are clearly taught. Shared resources also make it easy to follow

Abhishek

This course covers all the steps that one should take while solving a business problem through linear regression.

Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.

After completing this course **you will be able to**:

- Identify the business problem which can be solved using linear regression technique of Machine Learning.
- Create a linear regression model in Python and analyze its result.
- Confidently practice, discuss and understand Machine Learning concepts

This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems.

Below are the course contents of this course on Linear Regression:

**Section 1 - Basics of Statistics**

This section is divided into five different lectures starting from types of data then types of statistics then graphical representations to describe the data and then a lecture on measures of center like mean, median and mode and lastly measures of dispersion like range and standard deviation

**Section 2 - Python basic**

This section gets you started with Python.

This section will help you set up the python and Jupyter environment on your system and it'll teach you 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 Learning**

In 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 Preprocessing**

In this section you will learn what actions you need to take a step by step to get the data and then prepare 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 uni-variate analysis and bi-variate analysis then we cover topics like** outlier treatment, missing value imputation, variable transformation and correlation.**

**Section 5 - Regression Model**

This 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 you understand where the concept is coming from and how it is important. But even if you don't understand it, 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.

By the end of this course, your confidence in creating a regression model in Python will soar. You'll have a thorough understanding of how to use regression modelling to create predictive models and solve business problems.

53 videos
2 files

Introduction

Basics of Statistics

Setting up Python and Jupyter Notebook

Introduction to Machine Learning

Data Preprocessing

Linear Regression

Other Linear Models