Linear Regression Analysis in Python-Machine Learning Basics

Linear Regression in Python| Simple Regression, Multiple Regression, Ridge Regression, Lasso and subset selection

  • ( 4.0 ) (1 Reviews) , 1 eingeschriebene Studenten

Kursübersicht

After completing this course you will be able to:

·         Identify the business problem which can be solved using a 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

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 be able

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 than types of statistics then graphical representations to describe the data and then a lecture on measures of centre 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 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 univariate analysis and bivariate 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.

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, 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 a 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 depends on you. The method we adopted to help you learn Linear regression starts from the basics and takes you to an advanced level within hours. You can follow the same, but remember you cannot learn anything without practising 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. The second section of the course covers this part.

Understanding of Machine learning - The 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 stand out to be the leaders in recent days. The 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. The fifth and sixth section covers Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture where we 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 like 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.

 

Was sind die Anforderungen?

  • Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same

Was bekomme ich von diesem Kurs?

  • Learn how to solve real life problem using the Linear Regression technique
  • Predict future outcomes basis past data by implementing Simplest Machine Learning algorithm
  • Understanding of basics of statistics and concepts of Machine Learning
  • Learn advanced variations of OLS method of Linear Regression
  • How to convert business problem into a Machine learning Linear Regression problem
  • Data representation using Seaborn library in Python
  • Preliminary analysis of data using Univariate and Bivariate analysis before running Linear regression
  • Understand how to interpret the result of Linear Regression model and translate them into actionable insight
  • Indepth knowledge of data collection and data preprocessing for Machine Learning Linear Regression problem
  • Basic statistics using Numpy library in Python
  • Linear Regression technique of Machine Learning using Scikit Learn and Statsmodel libraries of Python

Was ist das Zielpublikum?

  • 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

Über den Autor

Lehrplan

Introduction Video
1 Video Lectures | 00:02:29

  • Welcome to the course!
    02:29
     

Course contents
1 Video Lectures | 00:07:04

  • Course contents
    07:04
     

Setting up Python and Jupyter Notebook
9 Video Lectures | 01:38:02

  • Installing Python and Anaconda
    03:05
     
  • Opening Jupyter Notebook
    09:06
     
  • Introduction to Jupyter
    13:27
     
  • Arithmetic operators in Python Basics
    04:28
     
  • Strings in 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
     

Basics of Statistics
5 Video Lectures | 00:30:11

  • 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
     

Introduction to Machine Learning
2 Video Lectures | 00:24:46

  • Introduction to Machine Learning
    16:03
     
  • Building a Machine Learning Model Learning
    08:43
     

Data Preprocessing
18 Video Lectures | 02:04:55

  • 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
     

Linear Regression
17 Video Lectures | 02:33:46

  • 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
    02:30
     

Bewertungen

  • Laxmi Jh
    Jyothi