Скачать или смотреть Lecture 8 : Lasso Regression | L1 Regularization | Complete | In Hindi

Lecture 8 : Lasso Regression | L1 Regularization | Complete | In Hindi

Lecture Work:
Code : https://colab.research.google.com/dri...

Used Datasets : https://www.kaggle.com/datasets/japon...
https://www.kaggle.com/datasets/nikhi...

Notes : https://miro.com/app/board/uXjVJ2dQQz...

Machine Learning Playlist :    • Machine Learning Using Python  

Welcome to Lecture 8 of the Machine Learning Playlist!
In this video, we’ll cover Lasso Regression (L1 Regularization) — an important concept in Machine Learning that not only helps reduce overfitting but also performs feature selection automatically.

We’ll begin by understanding what Lasso Regression is, how it differs from Ridge Regression, and why L1 Regularization leads to sparse models.
You’ll also see how to implement Lasso Regression in Python using Scikit-learn, tune the alpha parameter, and visualize the impact on coefficients.

🧠 In This Lecture, You’ll Learn:
What is Lasso Regression (L1 Regularization)
Why Regularization is important in ML
Difference between Ridge and Lasso Regression
Mathematical Formulation of Lasso Regression
How L1 penalty helps in Feature Selection
Practical Example of Lasso Regression in Python (Scikit-learn)
Finding best alpha using cross-validation
Visualization of coefficient shrinkage and sparsity
Comparison of Lasso and Ridge Regression models

More Playlist:
Python Playlist :    • Python in 45 Days Seminars  
Pandas :    • Pandas  
Pandas Data Analysis Process :    • Pandas Data Analysis Process  
Django Playlist :    • Django  
CSS Playlist :    • CSS  
HTML Playlist :    • HTML in 5 Lectures  
Bootstrap :    • Bootstrap  


This lecture is part of the Machine Learning Series that covers everything from Simple Linear Regression to Regularization Techniques.
In this lecture, we discuss Lasso Regression, a powerful method that introduces an L1 penalty to the cost function. This not only controls overfitting but also eliminates unnecessary features, making your model simpler and more efficient.

You’ll learn the mathematical foundation, intuition, and Python implementation of Lasso Regression step by step.
By the end, you’ll clearly understand how to choose between Ridge, Lasso, or ElasticNet Regression in real-world ML problems.


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