Understanding Linear Regression

  • BeginnerLevel

  • 502+Students Enrolled

  • 1 Hr 20 MinsDuration

  • 4.7Average Rating

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About this Course

  • Learn Linear Regression through a real-world case study, understanding its predictive power in business scenarios.
  • Explore the fundamentals of regression algorithms and their use in predicting continuous outcomes.
  • Apply Linear Regression techniques to real-world problems and evaluate model predictions effectively.

Learning Outcomes

Basics:Linear Regression

Explore the fundamentals of regression algorithms and their use.

Case Study

Learn Linear Regression through a real-world case study,

Practical Application

Apply Linear Regression techniques to real-world problems

Who Should Enroll

  • Ideal for professionals looking to strengthen their data analysis skills and apply regression techniques.
  • Perfect for those starting their journey in ML aiming to build a strong foundation in regression analysis.
  • Business analyst intrested in decision-making through data-driven predictions and insights using Linear Regression.

Course Curriculum

Explore a comprehensive curriculum covering Python, machine learning models, deep learning techniques, and AI applications.

tools

  1. 1. Introduction to the Problem Statement

  2. 2. Resources for this Course

  3. 3. Introduction to Linear Regression

  4. 4. Significance of Slope and Intercept in the linear regression

  5. 5. How Model Decides The Best-Fit Line

  6. 6. Let’s Build a Simple Linear Regression Model

  7. 7. Model Understanding Using Descriptive Approach

  8. 8. Model Understanding Using Descriptive Approach - II

  9. 9. Model Building Using Predictive Approach

  10. 10. Quiz: Linear regression

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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Kunal Jain

Founder & CEO, Analytics Vidhya

Kunal has 15+ years of experience in the field of Data Science and is the founder and CEO of Analytics Vidhya- the world's 2nd largest Data Science community.

Get this Course Now

With this course you’ll get

  • 1 Hour 20 Mins

    Duration

  • Kunal Jain

    Instructor

  • Beginner

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Globally recognized certificate
  • Verifiable online credential
  • Enhances professional credibility
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Frequently Asked Questions

Looking for answers to other questions?

Linear Regression is a fundamental algorithm in machine learning that models the relationship between a dependent variable and one or more independent variables to make predictions for regression problems.

Linear Regression works with numerical data, but categorical variables can be included using techniques like one-hot encoding to convert them into numerical form.

Linear Regression is used in fields like finance, marketing, healthcare, and operations to forecast trends, predict customer behavior, optimize pricing strategies, and more.

You can evaluate the model’s performance using metrics like Mean Squared Error (MSE), R-squared, and residual analysis, which help assess how well the model predicts outcomes.

Linear Regression assumes linearity, independence of errors, homoscedasticity (constant variance), and normally distributed residuals. Understanding these assumptions is crucial for building accurate models.

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