Machine Learning for Beginners-Regression Analysis in Python

What you’ll learn -:


Learn how to solve real life problem using the Linear Regression technique
Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression
Predict future outcomes basis past data by implementing Simplest Machine Learning algorithm
Understand how to interpret the result of Linear Regression model and translate them into actionable insight
Understanding of basics of statistics and concepts of Machine Learning
Indepth knowledge of data collection and data preprocessing for Machine Learning Linear Regression problem
Learn advanced variations of OLS method of Linear Regression
Course contains a end-to-end DIY project to implement your learnings from the lectures
How to convert business problem into a Machine learning Linear Regression problem
Basic statistics using Numpy library in Python

Description -:
You’re looking for a complete Linear Regression course that teaches you everything you need to create a Linear Regression model in Python, right?


You’ve found the right Linear Regression course!


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


A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.


How this course will help you?


If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular technique of machine learning, which is Linear Regression


Why should you choose this course?


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.


What makes us qualified to teach you?


The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course


We are also the creators of some of the most popular online courses – with over 150,000 enrollments and thousands of 5-star reviews like these ones:


This is very good, i love the fact the all explanation given can be understood by a layman – Joshua


Thank you Author for this wonderful course. You are the best and this course is worth any price. – Daisy

What is covered in this course?


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.

• People pursuing a career in data science
• Working Professionals beginning their Data journey
• Statisticians needing more practical experience
• Anyone curious to master Linear Regression from beginner to Advanced in short span of time