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You're looking for a complete Linear Regression course that teaches you everything you need to create a Linear Regression model in Excel, right?

You've found the right Linear Regression course.

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You're looking for a complete Linear Regression course that teaches you everything you need to create a Linear Regression model in Excel, 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 Excel and analyze its result.

· Confidently practice, discuss and understand Machine Learning concepts

How this course will help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.

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 - 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 3 - 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 R 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

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 R 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 R

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 in R where we actually run each query with you.

Enroll now

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Teaches linear regression, which is a fundamental technique of machine learning
Covers both simple and multiple linear regression, giving learners a comprehensive understanding of the technique
Strong emphasis on practical implementation, with hands-on practice and assignments
Suitable for beginners with little to no prior knowledge of machine learning or statistics
Taught by experienced professionals in the field, providing learners with up-to-date knowledge and industry insights
Covers a range of real-world business applications, demonstrating the practical value of linear regression

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Excel Analytics: Linear Regression Analysis in MS Excel with these activities:
Linear Regression: Theory and Applications
Use this book as a solid reference as you progress through the course, reviewing concepts and using extra examples to reinforce your learning.
Show steps
  • Read the introduction and chapter 1.
  • Read chapters 2 and 3.
  • Complete the practice problems at the end of each chapter.
  • Create a summary of the key concepts covered in the book thus far.
Linear Regression Tutorial - Step by Step
This interactive tutorial will enhance your conceptual understanding, allowing you to follow along with practical demonstrations and reinforce your knowledge.
Browse courses on Linear Regression
Show steps
  • Go through the tutorial and complete the interactive exercises.
  • Take notes on the key concepts and formulas used.
  • Apply the concepts to solve a sample problem.
Linear Regression Practice Problems
Solve a series of practice problems, checking your answers against provided solutions to solidify your understanding and identify areas for improvement.
Browse courses on Linear Regression
Show steps
  • Attempt to solve the practice problems on your own.
  • Check your answers against the provided solutions.
  • Review the concepts related to the problems you struggled with.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Create a Linear Regression Model for a Business Problem
Work on a project that challenges you to apply linear regression to solve a real-world business problem, demonstrating your comprehension and practical skills.
Browse courses on Linear Regression
Show steps
  • Identify a suitable business problem.
  • Collect and prepare the necessary data.
  • Build a linear regression model using the data.
  • Evaluate the performance of the model.
  • Write a report summarizing your findings.
Mentor a Peer in Linear Regression
Guide a peer through the concepts of linear regression, providing support and encouragement, which will reinforce your own understanding of the material.
Browse courses on Linear Regression
Show steps
  • Review practice problems and provide feedback.
  • Find a peer who needs assistance with linear regression.
  • Schedule regular sessions to discuss concepts and provide guidance.
  • Offer encouragement and support throughout the learning process.
Develop a Linear Regression App
Create an app that utilizes linear regression to address a specific problem, showcasing your ability to apply the technique in a practical setting.
Browse courses on Linear Regression
Show steps
  • Define the problem and scope of the app.
  • Design the user interface and functionality.
  • Implement the linear regression algorithm in the app.
  • Test and debug the app.
  • Deploy and maintain the app.
Participate in a Linear Regression Hackathon
Collaborate on a team to solve a complex linear regression problem within a limited time frame, fostering your problem-solving and teamwork skills.
Browse courses on Linear Regression
Show steps
  • Form a team with complementary skills.
  • Identify a problem statement.
  • Develop a solution using linear regression.
  • Present your solution to a panel of judges.
Attend a Linear Regression Meetup
Connect with professionals in the field, exchange knowledge, and explore potential collaboration opportunities that could enhance your learning journey.
Browse courses on Linear Regression
Show steps
  • Search for upcoming linear regression meetups in your area.
  • Register for the event and attend.
  • Introduce yourself to others and engage in discussions.
  • Follow up with interesting connections you make.

Career center

Learners who complete Excel Analytics: Linear Regression Analysis in MS Excel will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts use their knowledge of statistics and programming to gather, clean, and analyze data. This course can help you develop the skills needed to succeed as a Data Analyst by teaching you how to use Excel to create linear regression models. Linear regression is a powerful tool that can be used to predict future outcomes, identify trends, and make informed decisions. By taking this course, you will gain the skills and knowledge needed to become a successful Data Analyst.
Business Analyst
Business Analysts use data to identify and solve business problems. This course can help you develop the skills needed to succeed as a Business Analyst by teaching you how to use Excel to create linear regression models. Linear regression is a powerful tool that can be used to predict future outcomes, identify trends, and make informed decisions. By taking this course, you will gain the skills and knowledge needed to become a successful Business Analyst.
Financial Analyst
Financial Analysts use data to make investment recommendations. This course can help you develop the skills needed to succeed as a Financial Analyst by teaching you how to use Excel to create linear regression models. Linear regression is a powerful tool that can be used to predict future outcomes, identify trends, and make informed decisions. By taking this course, you will gain the skills and knowledge needed to become a successful Financial Analyst.
Statistician
Statisticians use data to collect, analyze, and interpret data. This course can help you develop the skills needed to succeed as a Statistician by teaching you how to use Excel to create linear regression models. Linear regression is a powerful tool that can be used to predict future outcomes, identify trends, and make informed decisions. By taking this course, you will gain the skills and knowledge needed to become a successful Statistician.
Data Scientist
Data Scientists use data to solve business problems. This course can help you develop the skills needed to succeed as a Data Scientist by teaching you how to use Excel to create linear regression models. Linear regression is a powerful tool that can be used to predict future outcomes, identify trends, and make informed decisions. By taking this course, you will gain the skills and knowledge needed to become a successful Data Scientist.
Machine Learning Engineer
Machine Learning Engineers use data to build and train machine learning models. This course can help you develop the skills needed to succeed as a Machine Learning Engineer by teaching you how to use Excel to create linear regression models. Linear regression is a powerful tool that can be used to predict future outcomes, identify trends, and make informed decisions. By taking this course, you will gain the skills and knowledge needed to become a successful Machine Learning Engineer.
Risk Analyst
Risk Analysts use data to identify and manage risks. This course can help you develop the skills needed to succeed as a Risk Analyst by teaching you how to use Excel to create linear regression models. Linear regression is a powerful tool that can be used to predict future outcomes, identify trends, and make informed decisions. By taking this course, you will gain the skills and knowledge needed to become a successful Risk Analyst.
Quantitative Analyst
Quantitative Analysts use data to make investment decisions. This course can help you develop the skills needed to succeed as a Quantitative Analyst by teaching you how to use Excel to create linear regression models. Linear regression is a powerful tool that can be used to predict future outcomes, identify trends, and make informed decisions. By taking this course, you will gain the skills and knowledge needed to become a successful Quantitative Analyst.
Market Researcher
Market Researchers use data to understand consumer behavior. This course can help you develop the skills needed to succeed as a Market Researcher by teaching you how to use Excel to create linear regression models. Linear regression is a powerful tool that can be used to predict future outcomes, identify trends, and make informed decisions. By taking this course, you will gain the skills and knowledge needed to become a successful Market Researcher.
Operations Research Analyst
Operations Research Analysts use data to improve business processes. This course can help you develop the skills needed to succeed as an Operations Research Analyst by teaching you how to use Excel to create linear regression models. Linear regression is a powerful tool that can be used to predict future outcomes, identify trends, and make informed decisions. By taking this course, you will gain the skills and knowledge needed to become a successful Operations Research Analyst.
Computer Programmer
Computer Programmers write and maintain computer programs. This course may be useful for Computer Programmers who want to learn how to use Excel to create linear regression models. Linear regression is a powerful tool that can be used to predict future outcomes, identify trends, and make informed decisions. By taking this course, Computer Programmers can gain the skills and knowledge needed to use Excel to solve business problems.
Software Engineer
Software Engineers design, develop, and maintain software applications. This course may be useful for Software Engineers who want to learn how to use Excel to create linear regression models. Linear regression is a powerful tool that can be used to predict future outcomes, identify trends, and make informed decisions. By taking this course, Software Engineers can gain the skills and knowledge needed to use Excel to solve business problems.
Database Administrator
Database Administrators design and maintain databases. This course may be useful for Database Administrators who want to learn how to use Excel to create linear regression models. Linear regression is a powerful tool that can be used to predict future outcomes, identify trends, and make informed decisions. By taking this course, Database Administrators can gain the skills and knowledge needed to use Excel to solve business problems.
Data Engineer
Data Engineers design and build data pipelines. This course may be useful for Data Engineers who want to learn how to use Excel to create linear regression models. Linear regression is a powerful tool that can be used to predict future outcomes, identify trends, and make informed decisions. By taking this course, Data Engineers can gain the skills and knowledge needed to use Excel to solve business problems.
Systems Analyst
Systems Analysts design and implement computer systems. This course may be useful for Systems Analysts who want to learn how to use Excel to create linear regression models. Linear regression is a powerful tool that can be used to predict future outcomes, identify trends, and make informed decisions. By taking this course, Systems Analysts can gain the skills and knowledge needed to use Excel to solve business problems.

Reading list

We've selected 11 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Excel Analytics: Linear Regression Analysis in MS Excel.
This classic textbook provides a comprehensive overview of reinforcement learning, which subfield of machine learning that uses artificial intelligence to learn how to make decisions in an environment. It valuable resource for anyone who wants to learn more about how reinforcement learning models are used to solve real-world problems.
This advanced textbook provides a comprehensive overview of statistical learning, including linear regression. It valuable resource for anyone who wants to learn more about the理論ical foundations of machine learning.
This comprehensive textbook provides a comprehensive overview of deep learning, which subfield of machine learning that uses artificial neural networks to learn from data. It valuable resource for anyone who wants to learn more about how deep learning models are used to solve real-world problems.
This comprehensive textbook provides a modern introduction to statistical learning, including linear regression. It valuable resource for anyone who wants to learn more about machine learning.
Provides a comprehensive overview of causal inference, which is the study of how to learn about cause-and-effect relationships from data. It valuable resource for anyone who wants to learn more about how to use regression models to make causal claims.
Provides a Bayesian perspective on statistical modeling, including linear regression. It valuable resource for anyone who wants to learn more about Bayesian statistics.
This classic textbook provides a comprehensive overview of linear regression models, from basic concepts to advanced topics such as generalized linear models and nonlinear regression. It valuable reference for anyone interested in learning more about linear regression.
This classic textbook provides a comprehensive overview of generalized linear models, which are a generalization of linear regression models. It valuable resource for anyone who wants to learn more about how regression models are used to model non-linear relationships.
This practical guide to machine learning provides step-by-step instructions on how to build and interpret machine learning models using Python. It is an excellent resource for anyone who wants to use machine learning to solve real-world problems.
This practical guide to linear regression provides step-by-step instructions on how to build and interpret regression models. It is an excellent resource for anyone who wants to use linear regression to solve real-world problems.
This online textbook provides a gentle introduction to machine learning, including linear regression. It valuable resource for anyone who wants to learn more about machine learning without having to read a textbook.

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