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

Udemy
Deal Score+2
Deal Score+2
Machine Learning Primer with JS: Regression (Math + Code)
Machine Learning Primer with JS Regression

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

Product Brand: Udemy

Editor's Rating:
5

Udemy Coupon Code for Machine Learning Primer with JS: Regression (Math + Code) Course. Explore practical coding, data analysis, and visualization with JavaScript and React JS, plus get Math background.

Created by Filip Jerga, Eincode by Filip Jerga | 13 hours on-demand video course

Machine Learning Primer with JS Course Overview

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Online Course: Machine Learning Primer with JS: Regression (Math + Code) from Udemy

Description for Machine Learning Primer with JS Course

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.

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What You Will Learn

This course is an excellent way to learn Machine Learning
  • 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.
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Course Structure

Machine Learning Primer with JS

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.
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Who this course is for

This course is designed for everyone who wants to
  • 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.

Taught by Filip Jerga

Josh Smith
Josh Smith

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