09-20-2024, 12:03 AM
![[Image: a7c56cdcdf45ddc07cadb5b642d3e2d3.jpg]](https://i124.fastpic.org/big/2024/0919/d3/a7c56cdcdf45ddc07cadb5b642d3e2d3.jpg)
Machine Learning Primer with JS: Regression (Math + Code)
Published 5/2024
Duration: 13h13m | .MP4 1280x720, 30 fps® | AAC, 44100 Hz, 2ch | 6.25 GB
Genre: eLearning | Language: English
Explore practical coding, data analysis, and visualization with JavaScript and React JS, plus get Math background.
What you'll learn
Understand and apply linear and multiple regression techniques.
Build and use regression models with Node js and React js
Grasp the key mathematical concepts behind regression algorithms.
Create a React app for real-time data plotting and regression analysis.
Requirements
Base knowledge of any programming language
Description
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.
![[Image: 3uTr4RTH_o.jpg]](https://images2.imgbox.com/b6/c1/3uTr4RTH_o.jpg)
What you'll learn
Understand and apply linear and multiple regression techniques.
Build and use regression models with Node js and React js
Grasp the key mathematical concepts behind regression algorithms.
Create a React app for real-time data plotting and regression analysis.
Requirements
Base knowledge of any programming language
Description
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.
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