Machine Learning for Beginners-Regression Analysis in Python
4.3 (532 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
61,369 students enrolled

Machine Learning for Beginners-Regression Analysis in Python

Linear Regression in Python| Simple Regression & Multiple Regression are essential for Machine Learning & Econometrics
4.3 (532 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
61,369 students enrolled
Last updated 5/2020
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Current price: $17.99 Original price: $29.99 Discount: 40% off
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This course includes
  • 7.5 hours on-demand video
  • 16 articles
  • 39 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
  • 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 69 lectures 07:24:13
+ Introduction
3 lectures 09:37

This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Preview 02:28

In this lecture we will learn about the content of this course.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Preview 07:04
Course resources
00:05
+ Setting up Python and Jupyter Notebook
9 lectures 01:37:58

In this lecture we will learn how to install python and anaconda on your system.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Preview 03:04

There are different ways to run Python. In this lecture we will learn how to open Jupyter notebook.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Opening Jupyter Notebook
09:06

Jupyter notebook comes with very handy features to enhance your coding experience. In this lecture we will learn about the Jupyter interface.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Introduction to Jupyter
13:26

It's time to start our Python Journey. In this lecture we will learn about the basic arithmetic operators in Python.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Arithmetic operators in Python: Python Basics
04:28

Next, we will move on to string related functions and operator in Python.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Strings in Python: Python Basics
19:07

In this lecture we will learn about Lists, Tuples and Directories in Python


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Lists, Tuples and Directories: Python Basics
18:41

In this lecture we will learn about Numpy Library in Python


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Working with Numpy Library of Python
11:54

In this lecture we will learn about Pandas library in Python


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Working with Pandas Library of Python
09:15

Graphical representation of data helps us to see underlying patterns in our data. In this lecture we will learn how to represent our data graphically using Seaborn Library in Python.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Working with Seaborn Library of Python
08:57
+ Basics of Statistics
7 lectures 30:31

In this lecture we will learn about the different types of data.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Preview 04:04

In this lecture we will learn about the types of Statistics.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Preview 02:45

Graphical representation of data helps us to see underlying patterns in our data. In this lecture we will learn how to represent our data graphically.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Preview 11:37

You must have heard about Mean, Median, Modes etc. In this lecture we will learn about different measures of centers.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Measures of Centers
07:05

Test your knowledge by answering the questions in this practice exercise


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Practice Exercise 1
00:12

You must have heard about Standard deviation, variance, range etc. If you have not, don't worry, in this lecture we will learn about different measures of dispersion.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Measures of Dispersion
04:37

Test your knowledge by answering this practice exercise.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Practice Exercise 2
00:11
+ Introduction to Machine Learning
2 lectures 24:45

We all struggle with the exact definition and meaning of Machine Learning. In this lecture we will cover a brief introduction of Machine learning


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Introduction to Machine Learning
16:03

Not sure where to start your Machine learning modelling? In this lecture we will learn different steps of Machine Learning and their importance in building a perfect model.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Building a Machine Learning Model
08:42
Introduction to Machine learning quiz
4 questions
+ Data Preprocessing
25 lectures 02:06:04

First step in Machine learning is to have a good business understanding of the problem you are going to solve. In this lecture we will discuss the same.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Gathering Business Knowledge
03:26

Next step is to gather the data. In this lecture we will learn how to gather more data for your Machine learning/ Linear Regression model.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Data Exploration
03:19

Understanding the gathered data is the next step. In this lecture we will learn about the importance of Data dictionary in Machine Learning.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

The Dataset and the Data Dictionary
07:31

In this lecture we will learn how to import our data in Python.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Importing Data in Python
06:04

We have also provide additional project for you to practice. Project exercises are spread throughout this course.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Project exercise 1
00:16

The next step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. In this lecture we will learn about the EDD and Univariate analysis.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Univariate analysis and EDD
03:34

The next step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. In this lecture we will learn how to run EDD and Univariate analysis in Python.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

EDD in Python
12:11

We have also provide additional project for you to practice. Project exercises are spread throughout this course.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Project Exercise 2
00:07

Data preprocessing is the most important step of building a Linear Regression model. In this lecture we will learn how to treat outliers in our data.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Outlier Treatment
04:15

Data preprocessing is the most important step of building a Linear Regression model. In this lecture we will learn how to treat outliers using Python.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Outlier Treatment in Python
14:18

We have also provide additional project for you to practice. Project exercises are spread throughout this course.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Project Exercise 3
00:09

Data preprocessing is the most important step of building a Linear Regression model. In this lecture we will learn about the Missing Value Imputation.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Missing Value Imputation
03:36

Data preprocessing is the most important step of building a Linear Regression model. In this lecture we will learn about how to impute Missing Values using python.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Missing Value Imputation in Python
04:57

We have also provide additional project for you to practice. Project exercises are spread throughout this course.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Project Exercise 4
00:09

Sometimes, the business is seasonal in nature for example travel industry, winter wear manufacturing etc. In this lecture we will learn about the impact of seasonality and how to treat it.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Seasonality in Data
03:35

The next step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. In this lecture we will learn how to use Bivariate analysis.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Bi-variate analysis and Variable transformation
16:14

Sometimes, transforming variables by taking log, exponential etc is necessary to remove outlier or improve the fit. In this lecture we will learn how to transform and delete useless variables in python


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Variable transformation and deletion in Python
09:21

We have also provide additional project for you to practice. Project exercises are spread throughout this course.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Project Exercise 5
00:10

In this lecture we will learn how to identify Non-usable variables


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Non-usable variables
04:44

We cannot use categorical variables in Linear Regression. In this lecture we will learn the important concept of creating dummy numeric variables from our categorical data.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Dummy variable creation: Handling qualitative data
04:50

We cannot use categorical variables in Linear Regression. In this lecture we will learn how to create dummy numeric variables from our categorical data in Python.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Dummy variable creation in Python
05:45

We have also provide additional project for you to practice. Project exercises are spread throughout this course.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Project Exercise 6
00:07

The last step before running Linear Regression model is to lookout for potential multi collinearity issue. In this lecture we will learn in detail about the correlation.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Correlation Analysis
10:05

The last step before running Linear Regression model is to lookout for potential multi collinearity issue. In this lecture we will learn how to run correlation analysis in Python.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Correlation Analysis in Python
07:07

We have also provide additional project for you to practice. Project exercises are spread throughout this course.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Project Exercise 7
00:11
+ Linear Regression
22 lectures 02:34:55

In this lecture we will learn in about the solutions we are seeking from our House price data.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

The Problem Statement
01:25

Most basic Linear Regression model is simple Linear Regression model. In this lecture we will learn in detail about the theory behind simple Linear Regression model.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Basic Equations and Ordinary Least Squares (OLS) method
08:13

Final step is to interpret the result of Linear Regression model. In this video we learn about the various model statistics and how these statistics help us in assessing the accuracy of our model.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Assessing accuracy of predicted coefficients
14:40

Final step is to interpret the result of Linear Regression model. In this video we learn about the various model statistics and how these statistics help us in assessing the accuracy of our model.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Assessing Model Accuracy: RSE and R squared
07:19

Most basic Linear Regression model is simple Linear Regression model. In this lecture we will learn how to run  simple Linear Regression model in Python.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Simple Linear Regression in Python
14:07

We have also provide additional project for you to practice. Project exercises are spread throughout this course.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Project Exercise 8
00:13

This time we will take into consideration all our independent variable for building Linear Regression model. In this video we learn about the multiple linear regression model.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Multiple Linear Regression
04:57

In this lecture we learn about statistics to assess the accuracy of our multiple linear regression model.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

The F - statistic
08:22
Quiz
1 question

In this lecture you will learn how to interpret the result of categorical in multiple linear regression model.

This course 'Machine Learning Basics: Building Regression Model in R' will help you to solve real life problem with Linear Regression technique of Machine Learning using R. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in R. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in R' will walk you through all these steps of Linear Regression technique of Machine Learning with R.

Interpreting results of Categorical variables
05:04

This time we will take into consideration all our independent variable for building Linear Regression model in Python.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Multiple Linear Regression in Python
14:13
Quiz
1 question

We have also provide additional project for you to practice. Project exercises are spread throughout this course.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Project Exercise 9
00:12

In this video you will learn how to split your data into Train and Test set.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Test-train split
09:32

In this video you will learn about two important topics i.e. Bias and Variance.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Bias Variance trade-off
06:01

In this video you will learn how to split your data into Train and Test set in Python.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Test train split in Python
10:19
Quiz
1 question

In this video you will learn other Linear Regression techniques.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Linear models other than OLS
04:18

In this video you will learn about the subset selection techniques of Linear Regression.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Subset selection techniques
11:34

In this video you will learn about the Shrinkage Techniques such as Ridge and Lasso.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Shrinkage methods: Ridge and Lasso
07:14

In this video you will learn about how to run the Shrinkage Techniques such as Ridge and Lasso in Python.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Ridge regression and Lasso in Python
23:50
Heteroscedasticity
02:30

We have also provide additional project for you to practice. Project exercises are spread throughout this course.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Project Exercise 10
00:18

We have also provide additional project for you to practice. Project exercises are spread throughout this course.


This course 'Machine Learning Basics: Building Regression Model in Python' will help you to solve real life problem with Linear Regression technique of Machine Learning using Python. First step in any Machine learning technique is to have a good business knowledge and data understanding. The Second step of Machine Learning is to get a deeper understanding of your data using Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression. Next step is an iterative process in which you try different variations of linear regression such as Multiple Linear Regression, Ridge Linear Regression, Lasso Linear Regression and Subset selection techniques of Linear Regression in Python. Final step is to interpret the result of Linear Regression model and translate them into actionable insight. This course 'Machine Learning Basics: Building Regression Model in Python' will walk you through all these steps of Linear Regression technique of Machine Learning in Python

Final Project Exercise
00:12
Course Conclusion
00:20
Requirements
  • 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 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

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.

Download Practice files, take Quizzes, and complete Assignments

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.

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.


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


Cheers

Start-Tech Academy


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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 Regression modelling - Having a good knowledge of Linear 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.

What is the difference between Data Mining, Machine Learning, and Deep Learning?

Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.

Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.

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 Regression from beginner to Advanced in short span of time