Supervised Machine Learning – Regression

This learning track provides hands-on application of Statistical Analysis, Hypothesis Testing, Data Handling, Data Transformations, and Data Visualizations for variety of data. The track also provides the understanding of the basic concepts of Time Series Analysis, Network Flow, Optimization Problems and Operation Research.

  • icons final-02 6 Courses
  • icons final-03 7 Projects & Case Studies
Supervised Machine learning - Regression (1)
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    Difficulty: Intermediate

    Foundational knowledge or experience is recommended

  • Asset 1
    Duration: Approximately 5 Weeks

    Suggested learning pace is 5hr/week

Course Overview

  • Learn the fundamental concepts of regression analysis, and assumptions of the OLS method and understand the theory and implementation of both simple and linear regression models in Python.
  • Learn how to handle the challenge of overfitting and underfitting in regression models through regularization techniques using Python.
  • Learn how to perform detailed model evaluation and validation of multiple models using different evaluations metrics and techniques and choose the best model.
  • Learn how to apply the concepts learned on live data across industries to generate insights.

What’s included


Shareable Certificate

Earn a sharable certificate upon completion


Lifetime Access

Access this case study for life once completed


Flexible Scheduling

Start learning online immediately, at your own pace


Desktop Only

We recommend completing this learning track on a desktop

Skills you will learn

Simple Linear Regression

Multiple Linear Regression

Machine Learning

Model Validation

Model Evaluation

Model Selection

Regression Model Building



  • Machine Learning – Linear Regression
  • Simple Linear Regression using Python
  • Multiple Linear Regression using Python
  • Regularized Linear Models
  • Model Evaluation Techniques – Regression Models
  • Model Selection Techniques

  • Applying Linear Regression to predict used car prices
  • Automobile Price Estimation – Using ML to price automobiles based on key features
  • Predict Holiday Sales for A Retail Client – Application of Linear Regression
  • Assumptions in OLS Regression Models (Ordinary Least Squares)Predicting failure of Power Transformers in a Manufacturing plant using Regression & Performing Root Cause Analysis
  • Loan Amount Prediction for different Applicants using Regression Techniques
  • Analyzing body composition to predict body fat percentage by using Supervised learning methods

How it Works

Learn new skills that will boost your career by enrolling in courses across data analytics, data science, ML and AI. These courses will utilize readings, videos, quizzes, data cases, and even coding exercises to teach you skills and concepts in a way that will solidify your new knowledge for hands-on application.

With our hands-on projects, you will take your newly learned skills along with our 750+ low-code/no-code functions and embedded coding console to complete milestone-based projects. Once completed, you will have effectively applied new skills and concepts to real-world data cases that can be translated directly into your career.

Complete assessments and track your progress in real-time to benchmark your proficiency in relation to key functional areas. As you progress through your courses, our patented platform will utilize ML and AI to record and analyze your inputs and output to provide active feedback and recommendations that will help you learn more effectively than the standard Letter Grade system used today.

Learner Outcomes

Complete learning tracks to earn sharable certificates and badges. These awarded items will be look great in your portfolio as you showcase your skills and project experience to employers and colleagues.

  • Develop a working understanding of the fundamental concepts of regression analysis, assumptions of the OLS method, theory and implementation of Simple and Multiple Linear Regression Model in Python.
  • Ability to resolve overfitting and underfitting of a model through the application of different regularization techniques in Python.
  • Evaluate and validate multiple regression models and identify the best model with respect to the relevant metrics.
  • Interpretation of the outcome of the applied techniques.

“Rolai provides contextual upskilling opportunities … on one single platform.”

Sundar Ramamoorthy
Managing Director of Solutions.AI, Global Products & Delivery Lead at Accenture

“An excellent tool for anyone who wants to quickly learn the ropes.”

Sanket Kawde
Head Data and Analytics at CitiBank India

“Rolai is the best program available for someone looking to enhance their skills”

Connor McEachron
Planning & Analytics @ Brooks Brothers

“Great way to learn data analytics and data science”

Balaji Reddy
Manager – Applications Development

“The courses were excellent and covered topics that I didn’t expect”

Aadarsha G
Student At Ohio Wesleyan University
All the Most Frequently Asked Questions

What People Are Asking About Data Education

Rolai’s patented process provides a personalized learning process for each user. Rolai goes deeper than simply learning concepts and testing your skills. At Rolai, learners can apply their skills to actual industry use cases and projects.

Our courses include readings, videos, quizzes, and hands-on data cases that are completed using our virtual lab; give learners an applied learning experience.

No additional tools are needed to begin learning with Rolai. Our virtual lab contains the necessary data workspace and an embedded coding console.

  • We have internal SMEs across industries and domains that we work with to develop relevant content and assure quality datasets and problem statements.
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