Top 9 Best Online Linear Regression Courses
The topic of Linear Regression is more important than ever in the fields of Business, Machine Learning, Probability, Statistics, Data Science and Deep ... read more...Learning. Only the best course to learn Linear Regression will help you get there. This is the top 10 best online Linear Regression course that you cannot miss.

Deep Learning Foundation: Linear Regression and Statistics (Udemy) ranks first in the list of best online Linear Regression course. Linear regression is a good place to start in data science, and this course will help you build a solid foundation for deep learning and machine learning algorithms. This course covered hypothesis testing, unbiased estimators, statistical tests, and gradient descent. By the end of the course, you will be able to write your own regression algorithm from the ground up.
This course will help you gain a solid understanding of the fundamental topics that form the foundation of artificial intelligence and its subareas. Begin with introductory terminology, then move on to a statistical test, gradient descent, and logistic fitting. After completing this project, you will be able to design and implement regression algorithms. Take a look at their list of the Best Linear Algebra Courses.
Highlights:
 Develop a model from scratch with realworld examples
 Learn about cost optimization and adaptive learning rate
 Discuss sample estimator, distributions, and hypothesis testing
 Look into additional mathematical and Python programming concepts in the bonus section
 Touch upon important data science interview questions
 44 Lectures + 3 Downloadable resources + Full lifetime access
What you will learn:
 Mathematics behind RSquared, Linear Regression,VIF and more!
 Deep understating of Gradient descent and Optimization
 Program your own version of a linear regression model in Python
 Derive and solve a linear regression model, and implement it appropriately to data science problems
 Statistical background of Linear regression and Assumptions
 Assumptions of linear regression hypothesis testing
 Writing codes for TTest, ZTest and ChiSquared Test in python
Duration: 6.5 hours
Rating: 4.9 out of 5Enroll here: udemy.com/course/linearregressioninpythonstatisticsandcoding

Linear Regression and Modeling from Duke University (Coursera) ranks 2nd in the list of best online Linear Regression course. This course introduces linear regression models, both simple and multiple. You can use these models to examine the relationship between variables in a data set and a continuous response variable. This course will teach you the fundamental theory of linear regression and how to fit, examine, and use regression models to investigate relationships between multiple variables using the free statistical software R and RStudio.
Mine CetinkayaRundel is one of the best Assistant Professors of practice at Duke University's Department of Statistical Science. She has a Ph.D. in statistics and is interested in developing studentcentered learning tools for introductory statistics courses. By the end of this course, you will have a basic understanding of Linear Regression and its models. You might also be interested in their collection of Best Data Science Courses and machine learning tutorials.
Highlights:
 One of the simplest and easy to understand Linear Regression course available online for beginners.
 Learn from one of the top instructors of Duke University
 Learn about Linear Regression and its models that can be used to predict a linear relationship between two numerical variables
 Explore multiple regression that allows you to model numerical response variables with the help of multiple predictors.
 You’ll also learn inference for multiple linear regression, model selection, and model diagnostics.
 Work on data analysis assignments to test your knowledge of linear regression.
 Get shareable certificates after completing the course and peer review assignment.
Duration: 4 weeks, 57 hours/week
Rating: 4.7/5.0
Enroll here: coursera.org/learn/linearregressionmodel

Linear Regression and Logistic Regression in Python (Udemy) ranks 3rd on the list of best online Linear Regression course. Check out this resource if you want to learn about the intricacies of creating regression models. The lessons begin with an introduction to Python and statistics. The mentor then goes over machine learning and techniques for preparing datasets for analysis. Finally, you can put all the components together as your model comes to life. Following completion of this course, you will be able to:
 Identify the business problem which can be solved using linear and logistic regression technique of Machine Learning.
 Create a linear regression and logistic regression model in Python and analyze its result.
 Confidently model and solve regression and classification problems
A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course. 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.
What is covered in this course? Section 1  Basics of Statistics
 Section 2  Python basic
 Section 3  Introduction to Machine Learning
 Section 4  Data Preprocessing
 Section 5  Regression Model
Highlights:
 Set up your system by following the provided guidelines
 Explore and import data from multiple sources after various treatments
 Work with libraries like NumPy, Statsmodel, and Scikit Learn
 Gain actionable insights from the result of your algorithms
 Train and evaluate your model and identify an improvement scope
 69 Lectures + 3 Downloadable resources + Full lifetime access
What you will learn:
 Learn how to solve real life problem using the Linear and Logistic Regression technique
 Preliminary analysis of data using Univariate and Bivariate analysis before running regression analysis
 Understand how to interpret the result of Linear and Logistic Regression model and translate them into actionable insight
 Indepth knowledge of data collection and data preprocessing for Linear and Logistic Regression problem
 Basic statistics using Numpy library in Python
 Data representation using Seaborn library in Python
 Linear Regression technique of Machine Learning using Scikit Learn and Statsmodel libraries of Python
Duration: 8.5 hours
Rating: 4.7 out of 5Enroll here: udemy.com/course/linearregressionandlogisticregressioninpythonstarttech

Statistics: Linear Regression in Python (Udemy) ranks 4th in the list of best online Linear Regression course. Individuals who want to pursue a career in data science, statistics, machine learning, or artificial intelligence will benefit from Udemy's linear regression course. Developers who want to improve their coding skills can also benefit greatly from this course. This course will teach you the most widely used technique in machine learning, data science, and statistics: linear regression. We believe that enrolling in this course is a step toward success; let us explain why.
Lazy Programmer Inc.'s instructor is a data scientist and fullstack software engineer. He has trained over 200,000 students in data science, specifically linear regression. After completing this course, you’ll be able to understand the basic concepts of ML, Data Science, and Statistics: Linear Regression in python. You may also want to have a look at our compilation of Best AI Courses.
Highlights:
 Learn how to develop your own working program in Python for data analysis.
 Solve linear regression models to apply it to data science problems.
 Learn how to predict a patient’s systolic blood pressure with their age and weight by applying multidimensional linear regression.
 Get lifetime access to the course after onetime subscription
 Access lecturers on any device
 Get certified in Linear Regression after completing the course
What you will learn:
 Derive and solve a linear regression model, and apply it appropriately to data science problems
 Program your own version of a linear regression model in Python
Duration: 6 Hours
Rating: 4.6/5Enroll here: udemy.com/course/datasciencelinearregressioninpython

Data Science: Linear Regression from Harvard University (edX) ranks 5th on the list of best online Linear Regression course. Linear regression is typically used to quantify the relationship between two or more variables. This Linear Regression course from Harvard University will teach you how to use R to implement linear regression and adjust for confounding. This is an excellent course, according to our team, for those who want to learn the most common statistical modeling approaches in data science.
Rafael Irizarry, the instructor, is a top Biostatistics professor at Harvard University. He has over 15 years of experience teaching data analysis and applied statistics to students. You will be able to examine confounding and where extraneous variables affect the relationship between two or more other variables after completing this course.
Highlights:
 A basic level course to understand how to use R to implement linear regression.
 Learn from the best instructor of Data Science from Harvard University.
 Know about how Galton originally developed linear regression.
 Get information regarding when to use linear regression and how to implement it.
 Free to learn without any charges. However, you can upgrade the course for 49$ to access graded assignments and certification on passing the exam.
What you'll learn
 Skip What you'll learn
 How linear regression was originally developed by Galton
 What is confounding and how to detect it
 How to examine the relationships between variables by implementing linear regression in R
Duration: 8 weeks, 12 hours/week
Rating: 4.5/5Enroll here: edx.org/course/datasciencelinearregression

Data Science: Correlation and Regression (DataCamp) ranks 6th on the list of best online Linear Regression course. DataCamp is known for offering some of the best online courses to individuals, and one of those courses is correlation and regression. Finally, data analysis is about understanding the relationships between variables. Exploring data with multiple variables necessitates new, more complex tools, but it allows for a more diverse set of comparisons. You will learn how to describe relationships between two numerical quantities in this course. You will depict these relationships graphically, in summary statistics, and using simple linear regression models.
The course will provide you with a thorough understanding of the relationships between various variables. You will be provided with a platform to explore data with multiple variables using new and more complex tools. This course will also teach you how to determine relationships between two numerical quantities. Ben Baumer is an assistant professor in Smith College's Statistical & Data Science Program. He is an American Statistical Association Accredited Professional Statistician who believes in educating anyone who is interested in Linear Regression. You will have a firm grasp on correlation and linear regression after completing this course.
What is covered in this course?
 Visualizing two variables
 Correlation
 Simple linear regression
 Interpreting regression models
 Model Fit
Highlights:
 A quick and straightforward course to understand correlation and regression.
 Get lessons from one of the top professors of Smith College.
 Learn how to characterize relationships graphically between two numerical quantities.
 Understand the techniques to explore bivariate relationships.
 Know the basic concepts of correlation to quantify bivariate relationships.
 Explore and learn the basic concepts of simple linear regression models.
 Get knowledge about how to interpret the coefficients in a regression model.
Duration: 4 Hours, 18 Videos
Rating: 4.5/5Enroll here: datacamp.com/courses/correlationandregression

Excel has long been one of the most popular data storage and manipulation applications. In this program, you will learn how to use software features to achieve regression analytics. Investigate the practical aspects of dataset evaluation and learn how and where to apply the methods discussed. Most courses only teach how to run the analysis, but they believe that what happens before and after running the analysis is even more important, i.e. before running the analysis, you must have the correct data and perform some preprocessing on it.
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. 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.
Highlights:
 No prior experience of the subject is needed
 Observe and recognize potential problems that can be solved with this process
 Strengthen your grip on ML topics and discuss ideas confidently with peers
 Test the efficiency level of the model
 29 Lectures + 2 Articles + 1 Downloadable resource + Full lifetime access
What you will learn:
 Learn how to solve real life problem using the Linear Regression technique
 Preliminary analysis of data using Univariate 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
 Indepth knowledge of data collection and data preprocessing for Machine Learning Linear Regression problem
 Course contains a endtoend DIY project to implement your learnings from the lectures
Duration: 2.5 hours
Rating: 4.4/5Enroll here: udemy.com/course/predictiveregressionmodellinginmicrosoftexcel

This course will teach you how to analyze Linear Regression in depth. For a better understanding, let's go over the theory and coding together. You will learn how to conduct a thorough analysis of machine learning models. They will demonstrate outcomeoriented techniques for improving the accuracy of your machine learning models. This course will teach you how to build an accurate Linear Regression model in Python.
Developing a solution that checks all the boxes necessitates several stages and the integration of numerous libraries. This curriculum ensures that you have a thorough understanding of all aspects of computational modeling. You will also learn how to share your insights with the team in order to make critical corporate decisions. You should have an introductory knowledge of Python before enrolling in this course otherwise please do not enroll in this course.
After completing this course you will be able to:
 Interpret and Explain machine learning models which are treated as a blackbox
 Create an accurate Linear Regression model in python and visually analyze it
 Select the best features for a business problem
 Remove outliers and variable transformations for better performance
 Confidently solve and explain regression problems
What is covered in this course?
 Section 1 Introduction
 Section 2 Python Crash Course
 Section 3 Numpy Introduction
 Section 4 Pandas IntroductionSection 5 Matplotlib Introduction
 Section 6 Linear Regression Introduction
 Section 7 Data Preprocessing for Linear Regression
 Section 8 Machine Learning Models Interpretability and Explainer
 Section 9 Linear Regression Model Optimization
 Section 10 Feature Selection for Linear Regression
 Section 11 Ridge & Lasso Regression, ElasticNet, and Nonlinear Regression.
Highlights
 Installation guidelines for Python and Anaconda are available
 Brush up the fundamental syntax in the crash course section
 Explore the functions of libraries such as NumPy, Pandas, and Matplotlib
 Cleanse, preprocess and transform data and variables
 Interpret, and plot your observations with professional accuracy
 Interact with predictions using SHAP, YellowBrick, and LIME
 Learn about optimization techniques and feature selection
 138 Lectures + 2 Articles + 1 Downloadable resource + Full lifetime access
What you will learn:
 Analyse and visualize data using Linear Regression
 Plot the graph of results of Linear Regression to visually analyze the results
 Learn how to interpret and explain machine learning models
 Do indepth analysis of various forms of Linear and NonLinear Regression
 Use YellowBrick, SHAP, and LIME to interact with predictions of machine learning models
 Do feature selection and transformations to fine tune machine learning models
 Course contains result oriented algorithms and data explorations techniques
Duration: 14.5 hours
Rating: 4.3/5Enroll here: udemy.com/course/pythonforadvancedlinearregressionmasterclass

This course is one of the top contenders available online for developing data analysis skills using the popular programming language R. Develop your intuition to identify business problems that can be solved using these analyses. Before beginning the training and testing process, structure the data and eliminate errors. Finally, estimate the model's efficiency by measuring its accuracy rate. Following completion of this course, you will be able to:
 Identify the business problem which can be solved using linear and logistic regression technique of Machine Learning.
 Create a linear regression and logistic regression model in R Studio and analyze its result.
 Confidently practice, discuss and understand Machine Learning concepts
If you are a business manager, executive, or student who wants to learn and apply machine learning in realworld business problems, this course will provide you with a solid foundation by teaching you the most popular machine learning technique, Linear Regression. 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 preprocessing 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. 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.
What is covered in this course?
 Section 1  Basics of Statistics
 Section 2  Python basic
 Section 3  Introduction to Machine Learning
 Section 4  Data Preprocessing
 Section 5  Regression Model
Highlights:
 Set up Jupyter environment and Python for your computer
 Perform preliminary checks with univariate and bivariate analysis
 Create dummy variables and transform them
 Attempt quizzes and identify your weak areas
 Showcase your findings graphically and gain insights
 67 Lectures + 3 Articles + Full lifetime access
What you will learn:
 Learn how to solve real life problem using the Linear and Logistic Regression technique
 Preliminary analysis of data using Univariate and Bivariate analysis before running regression analysis
 Graphically representing data in R before and after analysis
 How to do basic statistical operations in R
 Understand how to interpret the result of Linear and Logistic Regression model and translate them into actionable insight
 Indepth knowledge of data collection and data preprocessing for Linear and Logistic Regression problem
Duration: 7 hours
Rating: 4.3/5Enroll here: udemy.com/course/linearregressionandlogisticregressionrstudiostarttech