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

Machine Learning Primer with JS: Regression (Math + Code), available at $54.99, has an average rating of 4.7, with 88 lectures, based on 5 reviews, and has 204 subscribers.

You will learn about 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. This course is ideal for individuals who are Beginners curious about the field of machine learning. or Software developers interested in adding machine learning capabilities to their skillset. or Students and professionals who prefer a hands-on, practical approach to learning data analysis and statistical modeling. It is particularly useful for Beginners curious about the field of machine learning. or Software developers interested in adding machine learning capabilities to their skillset. or Students and professionals who prefer a hands-on, practical approach to learning data analysis and statistical modeling.

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

Summary

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

Price: $54.99

Average Rating: 4.7

Number of Lectures: 88

Number of Published Lectures: 88

Number of Curriculum Items: 88

Number of Published Curriculum Objects: 88

Original Price: $119.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

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.

Target Audiences

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.

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: How to watch the lectures

Chapter 2: Linear Regression 101

Lecture 1: Setup

Lecture 2: Linear regression 101

Lecture 3: Simple line

Lecture 4: Equation parameters

Lecture 5: Draw 3 equations

Lecture 6: Linear regression definition

Lecture 7: Equation format + regression terms

Chapter 3: Linear Regression Basics

Lecture 1: Init React App + Start of Exercise 1

Lecture 2: Plot the data

Lecture 3: Average X

Lecture 4: Average Y

Lecture 5: Mean values in code

Lecture 6: Slope numerator

Lecture 7: Numerator in code

Lecture 8: Compute Denominator + Slope

Lecture 9: Compute slope in the code

Lecture 10: Compute the y-intercept

Chapter 4: Score Prediction

Lecture 1: Plot regression line

Lecture 2: Set regression params and input

Lecture 3: Predict score

Lecture 4: Compute predicted values from input data

Chapter 5: Model Evaluation

Lecture 1: Residuals

Lecture 2: Compute residuals in the code

Lecture 3: R squared computation

Lecture 4: Compute r2 in code

Lecture 5: Mae computation

Lecture 6: Compute MAE in code

Lecture 7: MSE computation

Lecture 8: Compute MSE in code

Chapter 6: Prepare React JS Components

Lecture 1: Create separate component for prediction

Lecture 2: Model selection

Lecture 3: Finish model selection

Lecture 4: Formula with residual

Chapter 7: Multiple Regression Basics

Lecture 1: Multiple regression start

Lecture 2: Multiple regression in App

Lecture 3: Matrices explanation

Lecture 4: Organize matrices in code

Lecture 5: Matrix multiplication

Lecture 6: Matrix multiplication in code

Lecture 7: Another multiplication

Chapter 8: Multiple Regression Advanced

Lecture 1: Calculate Determinant

Lecture 2: Adjugate

Lecture 3: Compute B coefficients

Lecture 4: Compute coefficients in code

Lecture 5: Store coefficients

Lecture 6: Get coefficients on frontend

Lecture 7: Display regression plane

Chapter 9: Salaries Prediction Task

Lecture 1: Data preparation

Lecture 2: Parse data from CSV

Lecture 3: Split data

Lecture 4: Data seeding

Lecture 5: Compute regression data

Lecture 6: Explain stats

Lecture 7: Store coefficients

Lecture 8: Prepare data for r2

Lecture 9: Compute r2

Lecture 10: Store all data in JSON

Lecture 11: Display data on the graph

Lecture 12: Display regression plane on salaries

Lecture 13: Predict salaries

Chapter 10: Car Prices Prediction Task

Lecture 1: Prepare car prediction

Lecture 2: Format data to dictionary

Lecture 3: Simplify car name

Lecture 4: Fix typos in car names

Lecture 5: Create category map

Lecture 6: Process data to array

Lecture 7: Debugging

Lecture 8: One hot encode

Lecture 9: Text to number parsing

Lecture 10: Row categories

Lecture 11: Data splitting

Chapter 11: Model training & Evaluation

Lecture 1: Train the model

Lecture 2: Compute r2 for car prices

Lecture 3: Compute correlation array

Lecture 4: Get correlated categories

Lecture 5: Compute model with correlations

Lecture 6: Include car names in model

Lecture 7: Car prediction init in React

Lecture 8: Export data

Chapter 12: Data visualization

Lecture 1: Display all graphs

Lecture 2: Improve model performance

Lecture 3: Create inputs

Lecture 4: Create selection for car names

Lecture 5: Set car name value

Lecture 6: Default values for inputs

Lecture 7: Course end – Compute prediction

Instructors

Eincode by Filip Jerga
Online Education

Filip Jerga
Software Engineer

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3 stars: 1 votes

4 stars: 0 votes

5 stars: 4 votes

Frequently Asked Questions

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