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# Linear Regression and Logistic Regression in Python

## Build predictive ML models with no coding or maths background. Linear Regression and Logistic Regression for beginners

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## What you will 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

## 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

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.

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 techniques of machine learning, which is Linear Regression and Logistic Regregression

Why should you choose this course?

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

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.

Go ahead and click the enroll button, and I'll see you in lesson 1!

Cheers

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Below is a list of popular FAQs of students who want to start their Machine learning journey-

What is Machine Learning?

Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

What is the Linear regression technique of Machine learning?

Linear Regression is a simple machine learning model for regression problems, i.e., when the target variable is a real value.

Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x).

When there is a single input variable (x), the method is referred to as simple linear regression.

When there are multiple input variables, the method is known as multiple linear regression.

Why learn Linear regression technique of Machine learning?

There are four reasons to learn Linear regression technique of Machine learning:

1. Linear Regression is the most popular machine learning technique

2. Linear Regression has fairly good prediction accuracy

3. Linear Regression is simple to implement and easy to interpret

4. It gives you a firm base to start learning other advanced techniques of Machine Learning

How much time does it take to learn Linear regression technique of machine learning?

Linear Regression is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn Linear regression starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of Linear regression.

What are the steps I should follow to be able to build a Machine Learning model?

You can divide your learning process into 4 parts:

Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.

Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning model

Programming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in Python

Understanding of Linear and Logistic Regression modelling - Having a good knowledge of Linear and Logistic Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.

Why use Python for data Machine Learning?

Understanding Python is one of the valuable skills needed for a career in Machine Learning.

Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:

In 2016, it overtook R on Kaggle, the premier platform for data science competitions.

In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.

In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.

Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.

## Content

Introduction

Introduction

Setting up Python and Python Crash Course

Installing Python and Anaconda

Course Resources

Opening Jupyter Notebook

Introduction to Jupyter

Arithmetic operators in Python: Python Basics

Strings in Python: Python Basics

Lists, Tuples and Directories: Python Basics

Working with Numpy Library of Python

Working with Pandas Library of Python

Working with Seaborn Library of Python

Basics of Statistics

Types of Data

Types of Statistics

Describing data Graphically

Measures of Centers

Measures of Dispersion

Data Preprocessing before building Linear Regression Model

Data Exploration

The Dataset and the Data Dictionary

Importing Data in Python

Univariate analysis and EDD

EDD in Python

Outlier Treatment

Outlier Treatment in Python

Missing Value Imputation

Missing Value Imputation in Python

Seasonality in Data

Bi-variate analysis and Variable transformation

Variable transformation and deletion in Python

Non-usable variables

Dummy variable creation: Handling qualitative data

Dummy variable creation in Python

Correlation Analysis

Correlation Analysis in Python

Building the Linear Regression Model

The Problem Statement

Basic Equations and Ordinary Least Squares (OLS) method

Assessing accuracy of predicted coefficients

Assessing Model Accuracy: RSE and R squared

Simple Linear Regression in Python

Multiple Linear Regression

The F - statistic

Interpreting results of Categorical variables

Multiple Linear Regression in Python

Test-train split

Test train split in Python

Logistic Regression: Data Preprocessing

The Dataset and the Data Dictionary

Data Import in Python

EDD in Python

Outlier Treatment in Python

Missing Value Imputation in Python

Variable transformation and Deletion in Python

Dummy variable creation in Python

Building a Logistic Regression Model

Why can't we use Linear Regression?

Logistic Regression

Training a Simple Logistic Model in Python

Result of Simple Logistic Regression

Logistic with multiple predictors

Training multiple predictor Logistic model in Python

Confusion Matrix

Creating Confusion Matrix in Python

Evaluating performance of model

Evaluating model performance in Python

Test-Train Split

Test-Train Split

Test-Train Split in Python

## Reviews

A
Anurag5 August 2020

Vague on concepts and clarity of how things work. Since it says no coding or maths background needed, it is not the case please. The course doesn't explain many things so definitely coding and stats background is needed to understand the concepts else this course is of no use. The curators of the course could maybe give a gist of the concepts if not details is what I felt for those who really have no coding/stats background.

A

It was a great course explaining the Linear Regression and Logistic Regression and its implementation in practical scenario

S
Syed6 July 2020

This course clarified some of my doubts. This course is good for beginners of machine learners. They can learn how and when to use linear or logistic regression. The course is well-designed and suited well for data science learners.

D
Divyashree24 June 2020

It gives more detailing for analysing the data . The theory explanation of the topics are too good. I would highly recommend this course for beginners.

J
Jitender23 June 2020

Pathetic accent and pronunciation!! I really doubt these guys went to IIT and IIM /FMS as they claim! Sound like fraudsters!

## Coupons

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Udemy ID

## 6/4/2020

Course created date

## 6/22/2020

Course Indexed date
Lee Jia Cheng
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