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Students - How to solve problems using the linear regression technique
- How to implement preliminary analysis of data using Univariate and Bivariate analysis before linear regression
- How to make predictions based on data
- In-depth knowledge about data collection and data preprocessing
- How to implement linear regression using Scikit Learn and Statsmodel libraries of Python

7h 21m

09:42

1:38:02

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

11:55

Working with Pandas Library of Python

09:15

Working with Seaborn Library of Python

08:58

30:11

Types of Data

04:05

Types of Statistics

02:46

Describing data Graphically

11:37

Measures of Centers

07:05

Measures of Dispersion

04:38

24:46

Introduction to Machine Learning

16:03

Building a Machine Learning Model

08:43

2:04:55

Gathering Business Knowledge

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

04:16

Outlier Treatment in Python

14:18

Missing Value Imputation

03:37

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

05:46

Correlation Analysis

10:05

Correlation Analysis in Python

07:07

2:33:47

The Problem Statement

01:26

Basic Equations and Ordinary Least Squares (OLS) method

08:13

Assessing accuracy of predicted coefficients

14:40

Assessing Model Accuracy RSE and R squared

07:20

Simple Linear Regression in Python

14:07

Multiple Linear Regression

04:58

The F - statistic

08:23

Interpreting results of Categorical variables

05:04

Multiple Linear Regression in Python

14:13

Test-train split

09:33

Bias Variance trade-off

06:02

Test train split in Python

10:19

Linear models other than OLS

04:19

Subset selection techniques

11:34

Shrinkage methods Ridge and Lasso

07:14

Ridge regression and Lasso in Python

23:51

Heteroscedasticity

02:31

Linear regression in Python is a technique vital to the machine learning procedures and predictive analysis. By definition, linear regression is a learning algorithm that reveals the relationship between several variables.

For instance, you could put the number of customers that have bought your product on the y-axis. Then, you can set the price of the product on the x-axis. From this regression analysis in Python, you would determine how the price influences the number of customers you have. Of course, you can analyze any problem you want, and this is one of the simplest examples. However, it represents the fact that linear regression helps to solve real-life issues.

As I have already explained, Python regression analysis helps you determine how one thing influences the other. In other words, it reveals the relationship between two variables, how the tendencies shift when one changes. Therefore, you will need to use linear regression to prove that one factor influences the other.

In this course, we will be making all of the steps necessary for implementing it in Python. First of all, you will need to indicate the problem you want to solve. In other words, you can call it the problem statement. As an example, you can aim to study whether years of experience determine one’s salary.

For instance, if you take one predictor variable X, the quantitative response Y will be estimated. This technique is also referred to as the simple linear regression. In some cases, you can use multiple linear regression. It means that you choose more than one predictor variable to use for predicting the changes in the response variable.

In this course on linear regression in Python, I will start from some Python basics: arithmetic operations, strings, list tuples, and directories. I will also cover the installation processes for Python and Anaconda, opening Jupyter Notebook, and working with Numpy, Pandas, and Seaborn libraries.

Additionally, before starting the regression analysis in Python, I will go over the types of data and statistics, describing data graphically, measures of centers, and measures of dispersion. Later on, I will provide an introduction to machine learning and the way its models are built. If you are already familiar with these basics, you can skip this part and jump to the lectures about how to do linear regression in Python.

However, I recommend that you refresh your memory beforehand. I try not to be too mathematical when it comes to explaining how to do linear regression in Python. This technique is one of the simplest ways to start pursuing machine learning, but linear regression offers a decent prediction ability.

While this course covers many basic principles and procedures leading up to the Python regression analysis, you should be aware of the Python basics and its use. Additionally, one of the requirements is to have Anaconda. In case you do not have this software, please follow the instructions in one of the first lectures of this course.

- Installing Python and Anaconda (explained in the course as well)
- Basics of Python

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Working as a Project manager in an Analytics consulting firm, Pukhraj has multiple years of experience working on analytics tools and software. He is competent in MS office suites, Cloud computing, SQL, Tableau, SAS, Google analytics, and Python.

Abhishek worked as an Acquisition Process owner in a leading telecom company before moving on to learning and teaching technologies like Machine Learning and Artificial Intelligence.

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