Fundamentals of Regression Analysis

IntermediateLevel
10928+Students Enrolled
1 Hr 30 MinsDuration
4.9Average Rating

About
Learning Outcomes
Who Should Enroll
Course Curriculum
Instructors

About this Course

Learn the fundamentals, types, applications, key assumptions, advantages, and limitations of regression analysis.
Covers Linear, Logistic, Ridge, Lasso, Polynomial, Stepwise, and other advanced regression techniques.
Hands-on coding in Python with real-world use cases and model selection strategies.

Learning Outcomes

Understaning Regression

Learn the basics, types, and applications of regression analysis.

Regression Techniques

Gain expertise in Linear, Logistic, Ridge, and Lasso regression.

Develop Practical Skills

Implement regression models using Python for real-world scenarios.

Selection & Evaluation

Learn to choose the right regression model and handle challenges.

Who Should Enroll

Data scientists and analysts can use regression to predict outcomes and better understand work trends.
Students and researchers can learn how to analyze data and spot trends with easy, clear methods.
Individual wanting clear,practical skills can learn easy ways to use data for making smart choices every day.

Course Curriculum

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

1. Introduction to the Course

1. What is Regression Analysis?
2. Why do we use Regression?
3. AI&ML Blackbelt Plus Program

1. How many types of regression techniques do we have?

1. Introduction to Linear Models
2. Understanding Cost function.
3. Understanding Gradient descent
4. Maths behind gradient descent
5. Convexity of cost function
6. Assumptions of Linear Regression
7. Implementing Linear Regression
8. Generalized Linear Models

1. Introduction to Logistic Regression
2. Odds Ratio
3. Implementing Logistic Regression
4. Multiclass using Logistic Regression
5. Challenges with Linear Regression

1. What is Ridge Regression?
2. Hands-on Practise

1. What is Lasso Regression?

1. How to select the right regression model?

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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 30 Mins
Duration
Kunal Jain
Instructor
Intermediate
Level

Certificate of completion

Earn a professional certificate upon course completion

Industry-Recognized Credential
Career Advancement Credential
Shareable Achievement

Frequently Asked Questions

Looking for answers to other questions?

Regression analysis is a statistical technique used to identify relationships between a dependent variable and one or more independent variables.

Common types include Linear Regression, Logistic Regression, Ridge Regression, Lasso Regression, and Polynomial Regression.

Ridge adds a squared penalty to the loss function, while Lasso adds an absolute penalty, which can shrink some coefficients to zero.

Regression can be implemented in Python, R, Excel, Minitab, and KNIME, but Python is widely used in data science.

Yes, you will receive a certificate of completion after successfully finishing the course and assessments.

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