Ultimate ML Bootcamp #2: Linear Regression

Name: Ultimate ML Bootcamp #2: Linear Regression
Rating: 4.3 (16 reviews)

Master the Fundamentals of Linear Regression

Created byMiuul Data Science & Deep Learning

Last updated 8/2024

English

English [Auto],

What you'll learn

Understand the principles and applications of linear regression in predictive modeling and data analysis.
Calculate and interpret the weights (coefficients) in a linear regression model to understand relationships between variables.
Evaluate the performance of linear regression models using key metrics such as R-squared and Mean Squared Error (MSE).
Implement and optimize linear regression models using techniques like gradient descent for both simple and multiple regression scenarios.

Course content

1 section • 13 lectures • 1h 45m total length

Course Materials0:03
What is Linear Regression?5:33
Learn how linear regression models the linear relationship between dependent and independent variables using weights and a bias, enabling predictions like house prices from multiple features.
Calculating the Weights9:28
We estimate the constant and weight by minimizing the mean squared error to fit a linear regression model that predicts house prices from square footage.
Model Evaluation in Linear Regression7:45
Explore how regression models are evaluated using mean squared error, RMSE, and MAE, and learn how to optimize constants and weights to reduce prediction errors.
Estimation of Parameters12:28
Explore parameter estimation in linear regression by comparing normal equations and gradient descent, defining weights, coefficients, and estimators, and understanding MSE optimization and matrix considerations.
Gradient Descent for Linear Regression16:16
Apply gradient descent to linear regression by iteratively updating theta0 and theta1 to minimize the mean squared error, using partial derivatives, the update rule, and a learning rate alpha.
Simple Linear Regression Model6:19
Develop a simple linear regression model to predict sales from TV advertising spend, introducing the intercept and coefficient, and preparing data with Pandas and scikit-learn.
Prediction in Simple Linear Regression4:46
Explore prediction in simple linear regression with a two-variable model linking TV ad spend to sales, using equation sales equals 7.03 plus 0.05 times TV spend, and visualize with Seaborn.
Model Evaluation in Simple Linear Regression7:13
Evaluate simple linear regression predictions using mean squared error, RMSE, MAE, and r squared, by predicting sales from TV expenditure with a regression model and interpreting errors.
Multiple Linear Regression Model4:49
Apply multiple linear regression with newspaper, television, and radio to predict sales, using an 80/20 train-test split and interpreting the intercept (2.90) and three coefficients.
Prediction in Multiple Linear Regression4:13
Compute predictions in a multiple linear regression using the intercept 290 and coefficients for television, radio, and newspaper; write the model equation and predict sales for new data.
Model Evaluation in Multiple Linear Regression7:16
Evaluate regression models using training and test sets, explore cross-validation, compare training and test errors, and interpret R-squared and RMSE for univariate and multivariate regression.
Linear Regression with Gradient Descent19:19
Explore gradient descent for linear regression by coding from scratch: implement the mean squared error cost function and update weights with a learning rate in batch gradient steps.

Requirements

Basic understanding of Python programming is recommended, as the course involves coding exercises to implement linear regression models.

Description

Welcome to the second chapter of Miuul’s Ultimate ML Bootcamp—a comprehensive series designed to take you from beginner to expert in the world of machine learning and artificial intelligence. This course, Ultimate ML Bootcamp #2: Linear Regression, builds on the foundation you've established in the first chapter and dives deep into one of the most fundamental techniques in machine learning—linear regression.

In this chapter, you'll explore the principles and applications of linear regression, a critical tool for predictive modeling and data analysis. We’ll start by defining what linear regression is and why it’s so essential in both machine learning and statistical modeling. You’ll then learn how to calculate the weights (coefficients) that define your regression model, and how to evaluate its performance using key metrics.

As you progress, you’ll delve into more advanced topics such as parameter estimation, gradient descent optimization, and the differences between simple and multiple linear regression models. We’ll cover both theoretical concepts and practical applications, ensuring that you can confidently apply linear regression to real-world datasets.

This chapter is designed with a hands-on approach, featuring practical exercises and real-life examples to reinforce your learning. You’ll gain experience not only in building and evaluating models but also in understanding the mathematical foundations that underlie these techniques. Whether you're looking to enhance your predictive modeling skills, prepare for more complex machine learning tasks, or simply deepen your understanding of linear regression, this chapter will provide the knowledge and tools you need.

By the end of this chapter, you’ll have a solid grasp of linear regression, equipped with the skills to build, evaluate, and optimize both simple and multiple linear regression models. You’ll also be well-prepared to tackle more advanced techniques in the subsequent chapters of Miuul’s Ultimate ML Bootcamp. We’re excited to continue this journey with you, and we’re confident that with dedication and practice, you’ll master the art of linear regression and beyond. Let’s dive in!

Who this course is for:

Ideal for beginners in machine learning and data science who want to gain a strong understanding of linear regression
Suitable for professionals looking to enhance their predictive modeling skills