Excel Analytics: Linear Regression Analysis in MS Excel

Excel Analytics: Linear Regression Analysis in MS Excel

Language: English
Created by: Start-Tech Academy
Rate: 4.7 / 709 ratings
Enroll: 86,935 students

What you’ll 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

Requirements

  • You will need a PC with any version of Excel installed in it
  • Basic understanding of Excel operations like opening, closing and saving a file

Description

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?

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

Who this course is for:

  • Working Professionals beginning their Data journey
  • Anyone curious to master Linear Regression in short span of time
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