Regression Models for Data AnalystsPublished 9/2026
Created by Anishabrata Ghosh
MP4 |
Video: h264, 1920x1080 |
Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner |
Genre: eLearning |
Language: English |
Duration: 91 Lectures ( 3h 42m ) |
Size: 760.5 MB
⚡ Understand linear, multiple, ridge, lasso, and polynomial regression and know when to use each for prediction.
⚡ Build and evaluate regression models in Python using MSE, R², and appropriate model evaluation techniques.
⚡ Identify overfitting and apply Ridge, Lasso, feature scaling, and regularization techniques to improve model performance.
⚡ Apply feature scaling and standardization correctly using methods such as StandardScaler, MinMax, Robust, and L1/L2 normalization.
Requirements❗ Basic knowledge of data analysis is helpful, but no prior Machine Learning experience is required. Familiarity with basic Python and mathematics such as algebra will be beneficial.
DescriptionRegression is one of the most useful techniques for analyzing relationships between variables and making predictions from data. This course is designed to help beginners and aspiring Data Analysts understand regression models through clear explanations, practical examples, and Python demonstrations.
You'll begin with the fundamentals of
Linear Regression, learning how to understand the equation of a line, work with datasets, fit a model, and interpret how different factors influence predictions. You'll then progress to
Multiple Linear Regression, where you'll work with multiple variables and learn how to evaluate model performance using metrics such as
MSE and R².
The course then introduces
Ridge and Lasso Regression, helping you understand overfitting, regularization, the role of lambda, and how these techniques can improve models. You'll also learn how Lasso can support feature selection.
You'll explore
model evaluation, including train-validation-test splits, confusion matrices, classification metrics, regression metrics, ranking metrics, and model diagnostics.
Finally, you'll learn
Polynomial Regression for situations where straight-line relationships are not sufficient, along with the bias-variance trade-off and practical use cases. The course also covers
feature scaling and standardization, including Z-score standardization, MinMax scaling, Robust scaling, and L1/L2 normalization.
Throughout the course, Python mini-demos and quizzes reinforce the concepts and connect theory to practical data analysis.
Who this course is for⭐ Beginners in Data Analysis, aspiring Data Analysts, students, and professionals who want to learn regression models, make predictions, evaluate model performance, and apply regression techniques using Python.
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