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Machine Learning and its Applications

Machine Learning and its Applications

本课程是 Applied AI for Engineers and Scientists: Foundations 专项课程的一部分

位教师：Bo Liu

包含在中

了解更多

5个模块

深入了解一个主题并学习基础知识。

49 条评论

初级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

5个模块

深入了解一个主题并学习基础知识。

49 条评论

初级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Explain core machine learning concepts for classification and regression.
Prepare engineering data for machine learning workflows.
Apply SVMs, neural networks, and MATLAB apps to practical prediction tasks.

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

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作业

5 项作业

授课语言：英语（English）

91% of learners achieved a positive career outcome

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积累特定领域的专业知识

本课程是 Applied AI for Engineers and Scientists: Foundations 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有5个模块

This course provides a practical introduction to machine learning techniques for data analysis in MATLAB, focusing on widely used methods for real-world technical applications.

You will begin by exploring the core concepts behind machine learning, including model workflows, data preparation, and the factors that affect model performance. The course then focuses on two popular techniques—support vector machines and artificial neural networks—as well as MATLAB apps that make model building and evaluation more accessible. Using practical examples, you will prepare data, build machine learning workflows, and apply classification and regression methods to science and engineering problems. By the end of the course, you will be able to use MATLAB to develop, test, and evaluate predictive models for real-world applications. In partnership with MathWorks, enrolled learners receive access to MATLAB for the duration of the course.

单元详情

One of the most important applications of AI in science and engineering is classification and regression using machine learning. This module introduces essential concepts and principles in machine learning using two simple but useful machine learning techniques. After learning this module, students will be able to:

涵盖的内容

10个视频12篇阅读材料1个作业2个应用程序项目

10个视频总计65分钟

Welcome to the specialization - Applied AI for Engineers and Scientists: Foundations6分钟
Introduction to Module 7's Study2分钟
Machine Learning Fundamentals: What is Machine Learning6分钟
Machine Learning Fundamentals: Fundamental Concepts in Machine Learning (1)6分钟
Machine Learning Fundamentals: Fundamental Concepts in Machine Learning (2)9分钟
Mapping Inputs to Outputs: Data Representation11分钟
Mapping Inputs to Outputs: Parametric ML Model8分钟
Mapping Inputs to Outputs: Non-Parametric ML Model8分钟
Mapping Inputs to Outputs: Evaluate Output3分钟
MATLAB Implementation: Simple Linear Regression and KNN7分钟

12篇阅读材料总计81分钟

Specialization and Course Structure10分钟
Specialization Sample Certificate10分钟
How to Access MATLAB Online5分钟
Machine Learning in Engineering Practice: Tool, Replacement, or Decision Aid?5分钟
Materials for Machine Learning Fundamentals10分钟
From Explicit Rules to Learned Models: What Actually Changes?5分钟
Materials for Mapping Inputs to Outputs10分钟
How Data Representation and Model Choice Shape What a Model Can Learn5分钟
Materials about MATLAB Implementation10分钟
From Concept to Code: What the Model Is Actually Doing5分钟
CSV File for Module 7 Assignment 1分钟
Module 7 Recap5分钟

1个作业总计30分钟

Module 7 Quiz30分钟

2个应用程序项目总计60分钟

Module 7 Assignment 130分钟
Module 7 Assignment 230分钟

Continuing the last module, this module still introduces essential concepts and principles in machine learning with a focus on model training and evaluation. After learning this module, students will be able to:

涵盖的内容

7个视频9篇阅读材料1个作业2个应用程序项目

7个视频总计50分钟

Introduction to Module 8's Study2分钟
ML Model Training Fundamentals: Parameters, Hyperparameters, and Loss Functions9分钟
ML Model Training Fundamentals: Loss Functions and Gradient Descent6分钟
ML Model Training Fundamentals: Gradient Descent10分钟
ML Model Evaluation Fundamentals: Fundamental Concepts in ML Model Evaluation14分钟
ML Model Evaluation Fundamentals: Fundamental Concepts in ML Model Evaluation5分钟
Summary of the ML Process5分钟

9篇阅读材料总计56分钟

What Does “Good” Mean in Machine Learning Models?5分钟
Materials for ML model training10分钟
What Does It Actually Mean to “Train” a Machine Learning Model?5分钟
Materials on ML Model Evaluation Fundamentals10分钟
When a Model Fails: Is It Bias, Variance, or Something Else?5分钟
Materials for ML Process10分钟
From Data to Decisions: Reconstructing the Machine Learning Pipeline5分钟
CSV file for Module 8 Assignment1分钟
Module 8 Recap5分钟

1个作业总计30分钟

Module 8 Quiz30分钟

2个应用程序项目总计60分钟

Module 8 Assignment 130分钟
Module 8 Assignment 230分钟

This module introduces fundamental data preparation concepts and techniques to improve data quality in order to promote machine learning models providing good outcomes in real-world science and engineering practice. After learning this module, students will be able to:

涵盖的内容

8个视频13篇阅读材料1个作业3个应用程序项目

8个视频总计38分钟

Introduction to Module 9's Study2分钟
Basic Data Cleaning Review 1分钟
Distributions, Outliers and Their Removal: Gaussian Distribution, Skewness and Outliers7分钟
Distributions, Outliers and Their Removal: The Z-Score and IQR Method for Outliers Removal6分钟
Data Transform: Normalization, Standardization, Power Transform8分钟
Construct Training and Test Sets for Model Evaluation: Methods, Implementation, and Stratified Sampling6分钟
Construct Training and Test Sets for Model Evaluation: Cross-Validation6分钟
Data Preparation Overview4分钟

13篇阅读材料总计86分钟

Why Data Preparation Is Not Optional in Machine Learning5分钟
Materials on Basic Data Cleaning 10分钟
Cleaning Data Is a Decision, Not a Checklist5分钟
Materials on Distributions, Outliers and Their Removal10分钟
Outliers, Assumptions, and When “Cleaning” Becomes Damage5分钟
Materials on Data Transform10分钟
When Scaling Helps — and When It Quietly Breaks Your Model5分钟
Materials on Training/Test Sets Generation10分钟
Evaluation Starts with the Split: When Performance Numbers Lie5分钟
Materials on Data Preparation10分钟
From Raw Data to Reliable Learning: What Actually Matters?5分钟
XLS file for Module 9 Assignment1分钟
Module 9 Recap5分钟

1个作业总计30分钟

Module 9 Quiz30分钟

3个应用程序项目总计90分钟

Module 9 Assignment 130分钟
Module 9 Assignment 230分钟
Module 9 Assignment 330分钟

This module introduces support vector machines (SVMs), which is one of the most effective and popular methods for classification. After learning this module, students will be able to:

涵盖的内容

12个视频6篇阅读材料1个作业2个应用程序项目

12个视频总计80分钟

Introduction to Module 10's Study2分钟
Support Vector Machine Fundamentals: Concepts12分钟
Support Vector Machine Fundamentals: Types of SVM4分钟
Support Vector Machines: Linear SVM of Hard Margin Classifier7分钟
Support Vector Machines: Linear SVM of Soft Margin Classifier11分钟
Support Vector Machines: Non-Linear SVM13分钟
Support Vector Machines: Multi-Class SVM8分钟
Support Vector Machine Implementation: MATLAB Implementation of SVM5分钟
Support Vector Machine Implementation: Iris Flower Example6分钟
Support Vector Machine Implementation: 2D Point Classification Example1分钟
Case Study 1: Banknote Classification (Linear SVM)4分钟
Case Study 2: Raisin Classification (Non-Linear SVM)8分钟

6篇阅读材料总计41分钟

Why Support Vector Machines?5分钟
Materials on Support Vector Machine Fundamentals10分钟
Materials on Support Vector Machines10分钟
Materials on Support Vector Machine Implementation and Case Studies10分钟
CSV file for Module 10 Assignment1分钟
Module 10 Recap5分钟

1个作业总计30分钟

Module 10 Quiz30分钟

2个应用程序项目总计60分钟

Module 10 Assignment 130分钟
Module 10 Assignment 230分钟

This module introduces artificial neural networks (ANNs), which is one of the most effective and popular methods for regression and classification. After learning this module, students will be able to:

涵盖的内容

14个视频6篇阅读材料1个作业1个应用程序项目

14个视频总计84分钟

Introduction to Module 11's Study1分钟
Artificial Neural Network Introduction: Introduction3分钟
Artificial Neural Network Introduction: ANN Structure5分钟
Neural Network Training: Forward Propagation6分钟
Neural Network Training: Backward Propagation13分钟
Neural Network Training: More on Neural Network Training8分钟
Underfitting and Overfitting: Concepts8分钟
Underfitting and Overfitting: Methods to Improve Overfitting9分钟
ANN Implementation in MATLAB and Case Studies: Build an ANN Using MATLAB6分钟
ANN Implementation in MATLAB and Case Studies: A Case Study on Diabetes Diagnosis1分钟
Tutorial: MATLAB Classification and Regression Learner Apps7分钟
Tutorial: Classification Learner11分钟
Tutorial: Export Model3分钟
Tutorial: Regression Learner4分钟