100% Discount || Linear Regression and Logistic Regression in Python

Requirements
This course starts from basics and you do not even need coding background to build these models in Python
Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same
Description
You’re looking for a complete Linear Regression and Logistic Regression course that teaches you everything you need to create a Linear or Logistic 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 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

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

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

Who this course is for:
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