Machine Learning Primer with JS: Regression (Math + Code)

Explore practical coding, data analysis, and visualization with JavaScript and React JS, plus get Math background.

Dive into the world of machine learning with Machine Learning with JS: Regression Tasks (Math + Code). This course offers a focused look at linear regression, blending theoretical knowledge with hands-on coding to teach you how to build and apply linear regression models using JavaScript.

What You Will Learn:

Core Principles of Linear Regression: Begin with the fundamentals of linear regression and expand into multiple regression techniques. Discover how these models can predict future outcomes based on past data.

Hands-On Coding: Engage directly with practical coding examples, utilizing JavaScript. You'll use Node.js for the computational aspects and React.js for dynamic data visualization.

Simplified Mathematics: We make the essential math behind the models accessible, focusing on concepts that allow you to understand and implement the algorithms effectively.

Project-Based Learning: Build a React application from scratch that not only plots data but also computes regression parameters and visualizes these computations in real-time. This hands-on approach will help solidify your learning through actual development experience.

Real-World Applications: Learn to forecast real-world outcomes using the models you build. Understand the importance of residuals and how to quantify model accuracy with statistical measures such as R-squared, Mean Absolute Error (MAE), and Mean Squared Error (MSE).

Advanced Topics in Depth: Go beyond basic regression with sessions on handling complex data types through multiple regression analysis, matrix operations, and model selection techniques.

Course Structure:

This course includes over 80 detailed video lectures that guide you through every step of learning machine learning with JavaScript:

Introduction and Setup: Start with an overview of the necessary tools and configurations. Understand the foundational terms and concepts in regression.

Interactive Exercises: Each new concept is paired with practical coding exercises that reinforce the material by putting theory into practice.

In-Depth Projects: Apply what you've learned in extensive, real-world projects. Predict salary ranges based on job data or estimate car prices with sophisticated regression models.

Why Choose This Course?

Targeted Learning: We focus on linear regression to provide a thorough understanding of one of the most common machine learning techniques.

Practical JavaScript Use: By using JavaScript, a language familiar to many developers, this course demystifies the process of integrating machine learning into web applications and backend services.

Project-Driven Approach: The projects are designed to reflect real industry problems, preparing you for technical challenges in your career.

Who this course is for:

Beginners curious about the field of machine learning.
Software developers interested in adding machine learning capabilities to their skillset.
Students and professionals who prefer a hands-on, practical approach to learning data analysis and statistical modeling.