JavaScript机器学习入门：回归（数学 + 源代码） (英文)

JavaScript机器学习入门：回归（数学 + 源代码）

发布于 2024年5月 创建者：Eincode by Filip Jerga, Filip Jerga 格式：MP4 | 视频: h264, 1280×720 | 音频: AAC, 44.1 KHz, 2 声道 类型：电子学习 | 语言：英语 | 时长：88讲（13小时13分钟） | 大小：6.25 GB

探索使用JavaScript和React JS进行实际编码、数据分析和可视化，并掌握数学背景知识。

学习内容

• 理解并应用线性和多元回归技术
• 使用Node.js和React.js构建并使用回归模型
• 掌握回归算法背后的关键数学概念
• 创建用于实时数据绘图和回归分析的React应用程序

要求

任何编程语言的基础知识

描述

通过《JavaScript机器学习：回归任务（数学+源代码）》深入学习机器学习的世界。本课程专注于线性回归，将理论知识与实际编码相结合，教你如何使用JavaScript构建和应用线性回归模型。

学习内容：

• 线性回归核心原理：从线性回归的基础开始，扩展到多元回归技术。了解这些模型如何根据过去的数据预测未来的结果。
• 动手编码：直接参与实际编码示例，使用JavaScript进行计算，使用Node.js进行计算部分，并使用React.js进行动态数据可视化。
• 简化数学：使模型背后的必要数学知识易于理解，重点是让你能够有效地理解和实施算法。
• 项目驱动学习：从零开始构建一个React应用程序，不仅绘制数据，还计算回归参数并实时可视化这些计算。通过实际开发经验巩固你的学习。
• 现实应用：学习使用你构建的模型预测现实世界的结果。了解残差的重要性以及如何用统计指标如R平方、平均绝对误差（MAE）和均方误差（MSE）量化模型的准确性。
• 深入的高级主题：超越基础回归，学习通过多元回归分析、矩阵运算和模型选择技术处理复杂数据类型。

课程结构：

本课程包含80多个详细的视频讲座，逐步引导你学习JavaScript的机器学习：

• 介绍与设置：从必要工具和配置的概述开始。了解回归中的基础术语和概念。
• 互动练习：每个新概念都配有实际编码练习，通过实践将理论付诸实践。
• 深入项目：在广泛的现实项目中应用所学知识。通过复杂的回归模型预测薪资范围或估算驾驶汽车价格。

为什么选择本课程？

• 针对性学习：我们专注于线性回归，提供对最常见机器学习技术的深入理解。
• 实用JavaScript使用：通过使用许多开发者熟悉的JavaScript，本课程将机器学习集成到Web应用程序和后端服务中变得简单易懂。
• 项目驱动的方法：项目设计反映了真实的行业问题，为你的职业生涯中的技术挑战做好准备。

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

Published 5/2024
Created by Eincode by Filip Jerga,Filip Jerga
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 88 Lectures ( 13h 13m ) | Size: 6.25 GB

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.

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