Linear Regression and Logistic Regression in Python
4.1 (100 ratings)
20,048 students enrolled

# 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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4.1 (100 ratings)
20,048 students enrolled
Last updated 6/2020
English
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Current price: \$139.99 Original price: \$199.99 Discount: 30% off
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This course includes
• 8.5 hours on-demand video
• 1 article
• Access on mobile and TV
• Certificate of Completion
Training 5 or more people?

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
Course content
Expand all 65 lectures 08:15:41
+ Setting up Python and Python Crash Course
10 lectures 01:38:01
Course Resources
00:03
Preview 09:06
Introduction to Jupyter
13:26
Arithmetic operators in Python: Python Basics
04:28
Strings in Python: Python Basics
19:07
Lists, Tuples and Directories: Python Basics
18:41
Working with Numpy Library of Python
11:54
Working with Pandas Library of Python
09:15
Working with Seaborn Library of Python
08:57
+ Basics of Statistics
5 lectures 30:08
Preview 04:04
Types of Statistics
02:45
Describing data Graphically
11:37
Measures of Centers
07:05
Measures of Dispersion
04:37
+ Data Preprocessing before building Linear Regression Model
18 lectures 02:04:52
03:26
Data Exploration
03:19
The Dataset and the Data Dictionary
07:31
Importing Data in Python
06:04
Univariate analysis and EDD
03:34
EDD in Python
12:11
Outlier Treatment
04:15
Outlier Treatment in Python
14:18
Missing Value Imputation
03:36
Missing Value Imputation in Python
04:57
Seasonality in Data
03:35
Bi-variate analysis and Variable transformation
16:14
Variable transformation and deletion in Python
09:21
Non-usable variables
04:44
Dummy variable creation: Handling qualitative data
04:50
Dummy variable creation in Python
05:45
Correlation Analysis
10:05
Correlation Analysis in Python
07:07
+ Building the Linear Regression Model
12 lectures 01:44:12
Preview 01:25
Basic Equations and Ordinary Least Squares (OLS) method
08:13
Assessing accuracy of predicted coefficients
14:40
Assessing Model Accuracy: RSE and R squared
07:19
Simple Linear Regression in Python
14:07
Multiple Linear Regression
04:57
The F - statistic
08:22
Interpreting results of Categorical variables
05:04
Multiple Linear Regression in Python
14:13
Test-train split
09:32
06:01
Test train split in Python
10:19
+ Logistic Regression: Data Preprocessing
7 lectures 56:33
The Dataset and the Data Dictionary
08:14
Data Import in Python
04:56
EDD in Python
18:01
Outlier Treatment in Python
09:53
Missing Value Imputation in Python
04:49
Variable transformation and Deletion in Python
04:55
Dummy variable creation in Python
05:45
+ Building a Logistic Regression Model
10 lectures 01:02:13
Why can't we use Linear Regression?
04:32
Logistic Regression
07:54
Training a Simple Logistic Model in Python
12:25
Result of Simple Logistic Regression
05:11
Logistic with multiple predictors
02:22
Training multiple predictor Logistic model in Python
06:05
Confusion Matrix
03:47
Creating Confusion Matrix in Python
09:55
Evaluating performance of model
07:40
Evaluating model performance in Python
02:22
+ Test-Train Split
2 lectures 16:16
Test-Train Split
09:30
Test-Train Split in Python
06:46
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

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.

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