$Udemy – Machine Learning Primer with JS: Regression (Math + Code) 2024-5$

Udemy – Machine Learning Primer with JS: Regression (Math + Code) 2024-5

Published on: 2024-09-19 14:01:01

Categories: 28

Description

Machine Learning Primer with JS: Regression (Math + Code) is a training course on the principles of machine learning using JavaScript, published by Udemy Online Academy. Machine Learning Primer with JS: Regression (Math + Code) is a beginner’s course that introduces you to the basics of machine learning using JavaScript with a focus on regression analysis. This course combines the mathematical foundations of regression with hands-on coding exercises, making it perfect for aspiring data scientists, developers, and anyone interested in understanding how regression models work. You will learn key concepts such as linear and polynomial regression, model evaluation and optimization techniques.

By implementing these models in JavaScript, you’ll gain hands-on experience coding algorithms that can predict outcomes based on data, enhancing your skills in both machine learning and JavaScript development. This course covers the fundamentals of machine learning with a focus on regression, mathematical synthesis, and coding in JavaScript. Key topics include linear and polynomial regression, model evaluation, and optimization techniques. It provides hands-on experience coding regression algorithms, making it ideal for beginners in data science and development.

What you will learn in Machine Learning Primer with JS: Regression (Math + Code):

Linear and multiple regression techniques
Building and using regression models with Node js and React js
Key mathematical concepts behind regression algorithms
React program for graphing and regression analysis
and…

Course specifications

Publisher: Udemy
Instructors: Eincode by Filip Jerga and Filip Jerga
Language: English
Level: Introductory to Advanced
Number of Lessons: 88
Duration: 13 hours and 13 minutes