Complete Linear Regression Analysis in Python

Linear regression is a basic statistical and machine learning technique. There’s a good chance you’ll need it if you want to do statistics, machine learning, or scientific computing. It is best to start with a solid foundation and then progress to more complex methods.
You’re looking for a comprehensive Linear Regression course that will teach you everything you need to know to create a Linear Regression model in Python, correct? You’ve found the best Linear Regression training!

KEY FEATURE

Linear regression is a basic statistical and machine learning technique. There’s a good chance you’ll need it if you want to do statistics, machine learning, or scientific computing.

The dependent characteristics are referred to as dependent variables, outputs, or responses. The independent characteristics are also known as independent variables.

You could observe several employees of a company and try to figure out how their salaries are affected by factors such as experience, education level, role, city of employment, and so on.

Regression is used in a wide range of disciplines. Its significance grows by the day, as more data becomes available, and people become more aware of the practical value of data.

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Key Topics

    Following completion of this course, you will be able to:

    • Determine the business problem that can be solved using the Machine Learning linear regression technique.
    • In Python, create a linear regression model and analyse the results.
    • Practice, discuss, and comprehend Machine Learning concepts with confidence.
    All students who complete this Machine learning basics course receive a verifiable Certificate of Completion.

    How Will This Course Benefit You?

    You live in an era characterised by massive amounts of data, powerful computers, and artificial intelligence. This is only the start. Data science and machine learning are driving image recognition, autonomous vehicle development, financial and energy sector decisions, medical advances, the rise of social networks, and more. Linear regression is a critical component of this.
    If you are a business manager, executive, or student who wants to learn and apply machine learning to real-world business problems, this course will provide you with a solid foundation by teaching you the most popular machine learning technique, Linear Regression.

    Why Should You Take This Class?

    This course covers all of the steps involved in solving a business problem using linear regression.
    Most courses only teach 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, you must have the correct data and perform some pre-processing on it. And, after running the analysis, you should be able to judge how good your model is and interpret the results in order to actually help your business.

    What Qualifies Us To Teach You?

    We have assisted businesses in solving business problems through the use of machine learning techniques, and we have used our experience to include practical aspects of data analysis in this course. We also developed some of the most popular online courses, with over 1500 enrollments and thousands of 5-star reviews. Our job is to teach our students, and we are dedicated to it. If you have any questions about the course content, practise sheet, or anything else, please post them in the course or send us a direct message. There are notes attached to each assignment so you can follow along. You can also take quizzes to test your comprehension of concepts. Each section includes a practise assignment to help you put your knowledge into practise.

    The Important Course Content Covered By Our Experts

    The following are the course materials for this Linear Regression course:

    Basics of Statistics

    • This section is divided into five lectures, beginning with types of data and progressing to types of statistics.
    • then graphical representations of the data, and finally a lecture on centre measures such as mean
    • Finally, measures of dispersion such as range and standard deviation are used.

    Python Basic

    • This section introduces you to Python.
    • This section will teach you how to set up the Python and Jupyter environments on your system.
    • you how to perform some basic Python operations We will learn about the significance of various libraries such as Numpy, Pandas, and Seaborn.

    Machine Learning Overview

    • In this section, we’ll look at what Machine Learning entails.
    • What are the various meanings or terms associated with machine learning?
    • You will see some examples to better understand what machine learning is.
    It also includes the steps involved in developing any machine learning model, not just linear models

    Data Pre-processing

    • This section will teach you what actions you must take step by step to obtain the data and then prepare it for analysis. These steps are critical.
    • We will begin by discussing the significance of business knowledge before moving on to data exploration.

    Regression Model

    • This section begins with basic linear regression and progresses to multiple linear regression.
    • We covered the basic theory behind each concept without getting too mathematical, so you understand where the concept comes from and why it is important.
    • Even if you don’t understand it, it’s fine as long as you learn how to run it and interpret the results as demonstrated in the practical lectures.
    Your confidence in creating a regression model in Python will skyrocket by the end of this course. You’ll have a solid grasp on how to use regression modelling to build predictive models and solve business problems.

    Key Topics Studied Under Complete Linear Regression Analysis In Python

    Setting Up Python

    • Opening Jupyter Notebook
    • Introduction to Jupyter Notebook – Part 1 and Part 2

    Python Crash Course - Working With Different Data Types

    • Arithmetic operators in Python
    • Strings in Python – Part 1 and Part 2
    • Lists
    • Tuples and Directories

    Important Types Of Python

    • Working with Numpy Library of Python
    • Working with Pandas Library of Python
    • Working with Seaborn Library of Python

    Basics Of Statistics

    • Types of Data
    • Types of statistics
    • Describing data graphically
    • Measures of Centers
    • Measures of Dispersion

    Introduction to Machine Learning

    • Building a Machine Learning Model
    • Introduction to Machine learning quiz

    Data Preprocessing

    • Gathering Business Knowledge
    • Data Exploration
    • The Dataset and the Data Dictionary
    • Importing Data in Python
    • Univariate analysis and EDD
    • EDD in Python
    • Outlier Treatment in Python
    • Missing Value Imputation in Python
    • Seasonality in Data
    • Bi-variate analysis and Variable transformation
    • Variable transformation and deletion in Python
    • Non-usable variables
    • Handling qualitative data by using dummy variables
    • Dummy variable creation in Python
    • Correlation Analysis in Python

    Linear Regression

    • Basic Equations and Ordinary Least Squares (OLS) method
    • Assessing accuracy of predicted coefficients
    • Assessing Model Accuracy: RSE and R squared
    • Simple Linear Regression in Python
    • Multiple Linear Regression
    • The F – statistic
    • Interpreting results of Categorical variables
    • Multiple Linear Regression in Python
    • Test-train split
    • Bias Variance trade-off
    • Test train split in Python
    • Linear models other than OLS
    • Shrinkage methods: Ridge and Lasso
    • Ridge regression and Lasso in Python
    • Heteroscedasticity

    Why Should You Learn The Linear Regression Machine Learning Technique?

    There are four reasons to learn the Linear Regression Machine Learning technique:

    1. The most widely used machine learning technique is linear regression.
    2. Linear Regression has a reasonable prediction accuracy.
    3. Linear regression is simple to implement and understand.
    4. It provides you with a solid foundation for learning more advanced Machine Learning techniques.

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