Udemy – Machine Learning Primer with JS: Regression (Math + Code) Filip Jerga
English | Tutorial | Size: 6.26 GB
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.
RAPIDGATOR:
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TURBOBIT:
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