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 sub-areas. 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 real-world 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 R-Squared, 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 T-Test, Z-Test and Chi-Squared Test in python

Duration: 6.5 hours
Rating: 4.9 out of 5

Enroll here: udemy.com/course/linear-regression-in-python-statistics-and-coding

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 Cetinkaya-Rundel 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 student-centered 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, 5-7 hours/week

Rating: 4.7/5.0

Enroll here: coursera.org/learn/linear-regression-model

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.

• 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 5

Enroll here: udemy.com/course/linear-regression-and-logistic-regression-in-python-starttech

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 full-stack 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 multi-dimensional linear regression.
• Get lifetime access to the course after one-time 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: 4.6/5

Enroll here: udemy.com/course/data-science-linear-regression-in-python

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, 1-2 hours/week
Rating: 4.5/5

Enroll here: edx.org/course/data-science-linear-regression

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: 4.5/5

Enroll here: datacamp.com/courses/correlation-and-regression

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 pre-processing 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 end-to-end DIY project to implement your learnings from the lectures

Duration: 2.5 hours
Rating: 4.4/5

Enroll here: udemy.com/course/predictive-regression-modelling-in-microsoft-excel

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 outcome-oriented 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 black-box
• 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 in-depth analysis of various forms of Linear and Non-Linear 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: 4.3/5

Enroll here: udemy.com/course/python-for-advanced-linear-regression-masterclass

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 real-world 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 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. 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: 4.3/5

Enroll here: udemy.com/course/linear-regression-and-logistic-regression-r-studio-starttech