Machine Learning Primer with JS: Regression (Math + Code)
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.
Basic knowledge of any programming language
Setup
FREE PREVIEWLinear regression 101
FREE PREVIEWSimple line
Equation parameters
Draw 3 equations
Linear regression definition
Equation format + regression terms
Init React App - Start of Exercise 1
FREE PREVIEWPlot the data
Average X
Average Y
Mean values in code
Slope numerator
Numerator in code
Compute Denominator + Slope
Compute slope in the code
Compute the y-intercept
Plot regression line
FREE PREVIEWSet regression params and input
Predict score
Compute predicted values from input data
Residuals
Compute residuals in the code
R squared computation
Compute r2 in code
Mae computation
Compute MAE in code
MSE computation
Compute MSE in code
Create separate component for prediction
Model selection
Finish model selection
Formula with residual
Multiple regression start
Multiple regression in App
Matrices explanation
Organize matrices in code
Matrix multiplication
Matrix multiplication in code
Another Multiplication
Calculate Determinant
Adjugate
Compute B coefficients
Compute coefficients in code
Store coefficients
Get coefficients on frontend
Display regression plane
Data preparation
Parse data from CSV
Split data
Data seeding
Compute regression data
Explain stats
Store coefficients
Prepare data for r2
Compute r2
Store all data in JSON
Display data on the graph
Display regression plane on salaries
Predict salaries
Prepare car prediction
Format data to dictionary
Simplify car name
Fix typos in car names
Create category map
Process data to array
Debugging
One hot encode
Text to number parsing
Row categories
Data splitting
Train the model
Compute r2 for car prices
Compute correlation array
Get correlated categories
Compute model with correlations
Include car names in model
Car prediction init in React
Export data
Display all graphs
Improve model performance
Create inputs
Create selection for car names
Set car name value
Default values for inputs
Course end - Compute prediction
Filip Jerga
Every video contains a discussion where you can create a post describing an issue. The instructor usually responds within 1 business day.
Within 30 days of the purchase, you can ask for a full refund. No questions asked. Your happiness is our priority.
Of course not! Every lecture is committed (explained in the introduction section). You can start watching at any lecture. Just download the correct version of the project attached to lecture resources.
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