Machine Learning and its Applications
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Machine Learning and its Applications
This course is part of Applied AI for Engineers and Scientists: Foundations Specialization
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What you'll learn
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.
Skills you'll gain
Tools you'll learn
Details to know
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There are 5 modules in this course
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.
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:
What's included
10 videos12 readings1 assignment2 app items
10 videos•Total 65 minutes
- Welcome to the specialization - Applied AI for Engineers and Scientists: Foundations•6 minutes
- Introduction to Module 7's Study•2 minutes
- Machine Learning Fundamentals: What is Machine Learning•6 minutes
- Machine Learning Fundamentals: Fundamental Concepts in Machine Learning (1)•6 minutes
- Machine Learning Fundamentals: Fundamental Concepts in Machine Learning (2)•9 minutes
- Mapping Inputs to Outputs: Data Representation•11 minutes
- Mapping Inputs to Outputs: Parametric ML Model•8 minutes
- Mapping Inputs to Outputs: Non-Parametric ML Model•8 minutes
- Mapping Inputs to Outputs: Evaluate Output•3 minutes
- MATLAB Implementation: Simple Linear Regression and KNN•7 minutes
12 readings•Total 81 minutes
- Specialization and Course Structure•10 minutes
- Specialization Sample Certificate•10 minutes
- How to Access MATLAB Online•5 minutes
- Machine Learning in Engineering Practice: Tool, Replacement, or Decision Aid?•5 minutes
- Materials for Machine Learning Fundamentals•10 minutes
- From Explicit Rules to Learned Models: What Actually Changes?•5 minutes
- Materials for Mapping Inputs to Outputs•10 minutes
- How Data Representation and Model Choice Shape What a Model Can Learn•5 minutes
- Materials about MATLAB Implementation•10 minutes
- From Concept to Code: What the Model Is Actually Doing•5 minutes
- CSV File for Module 7 Assignment •1 minute
- Module 7 Recap•5 minutes
1 assignment•Total 30 minutes
- Module 7 Quiz•30 minutes
2 app items•Total 60 minutes
- Module 7 Assignment 1•30 minutes
- Module 7 Assignment 2•30 minutes
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:
What's included
7 videos9 readings1 assignment2 app items
7 videos•Total 50 minutes
- Introduction to Module 8's Study•2 minutes
- ML Model Training Fundamentals: Parameters, Hyperparameters, and Loss Functions•9 minutes
- ML Model Training Fundamentals: Loss Functions and Gradient Descent•6 minutes
- ML Model Training Fundamentals: Gradient Descent•10 minutes
- ML Model Evaluation Fundamentals: Fundamental Concepts in ML Model Evaluation•14 minutes
- ML Model Evaluation Fundamentals: Fundamental Concepts in ML Model Evaluation•5 minutes
- Summary of the ML Process•5 minutes
9 readings•Total 56 minutes
- What Does “Good” Mean in Machine Learning Models?•5 minutes
- Materials for ML model training•10 minutes
- What Does It Actually Mean to “Train” a Machine Learning Model?•5 minutes
- Materials on ML Model Evaluation Fundamentals•10 minutes
- When a Model Fails: Is It Bias, Variance, or Something Else?•5 minutes
- Materials for ML Process•10 minutes
- From Data to Decisions: Reconstructing the Machine Learning Pipeline•5 minutes
- CSV file for Module 8 Assignment•1 minute
- Module 8 Recap•5 minutes
1 assignment•Total 30 minutes
- Module 8 Quiz•30 minutes
2 app items•Total 60 minutes
- Module 8 Assignment 1•30 minutes
- Module 8 Assignment 2•30 minutes
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:
What's included
8 videos13 readings1 assignment3 app items
8 videos•Total 38 minutes
- Introduction to Module 9's Study•2 minutes
- Basic Data Cleaning Review •1 minute
- Distributions, Outliers and Their Removal: Gaussian Distribution, Skewness and Outliers•7 minutes
- Distributions, Outliers and Their Removal: The Z-Score and IQR Method for Outliers Removal•6 minutes
- Data Transform: Normalization, Standardization, Power Transform•8 minutes
- Construct Training and Test Sets for Model Evaluation: Methods, Implementation, and Stratified Sampling•6 minutes
- Construct Training and Test Sets for Model Evaluation: Cross-Validation•6 minutes
- Data Preparation Overview•4 minutes
13 readings•Total 86 minutes
- Why Data Preparation Is Not Optional in Machine Learning•5 minutes
- Materials on Basic Data Cleaning •10 minutes
- Cleaning Data Is a Decision, Not a Checklist•5 minutes
- Materials on Distributions, Outliers and Their Removal•10 minutes
- Outliers, Assumptions, and When “Cleaning” Becomes Damage•5 minutes
- Materials on Data Transform•10 minutes
- When Scaling Helps — and When It Quietly Breaks Your Model•5 minutes
- Materials on Training/Test Sets Generation•10 minutes
- Evaluation Starts with the Split: When Performance Numbers Lie•5 minutes
- Materials on Data Preparation•10 minutes
- From Raw Data to Reliable Learning: What Actually Matters?•5 minutes
- XLS file for Module 9 Assignment•1 minute
- Module 9 Recap•5 minutes
1 assignment•Total 30 minutes
- Module 9 Quiz•30 minutes
3 app items•Total 90 minutes
- Module 9 Assignment 1•30 minutes
- Module 9 Assignment 2•30 minutes
- Module 9 Assignment 3•30 minutes
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:
What's included
12 videos6 readings1 assignment2 app items
12 videos•Total 80 minutes
- Introduction to Module 10's Study•2 minutes
- Support Vector Machine Fundamentals: Concepts•12 minutes
- Support Vector Machine Fundamentals: Types of SVM•4 minutes
- Support Vector Machines: Linear SVM of Hard Margin Classifier•7 minutes
- Support Vector Machines: Linear SVM of Soft Margin Classifier•11 minutes
- Support Vector Machines: Non-Linear SVM•13 minutes
- Support Vector Machines: Multi-Class SVM•8 minutes
- Support Vector Machine Implementation: MATLAB Implementation of SVM•5 minutes
- Support Vector Machine Implementation: Iris Flower Example•6 minutes
- Support Vector Machine Implementation: 2D Point Classification Example•1 minute
- Case Study 1: Banknote Classification (Linear SVM)•4 minutes
- Case Study 2: Raisin Classification (Non-Linear SVM)•8 minutes
6 readings•Total 41 minutes
- Why Support Vector Machines?•5 minutes
- Materials on Support Vector Machine Fundamentals•10 minutes
- Materials on Support Vector Machines•10 minutes
- Materials on Support Vector Machine Implementation and Case Studies•10 minutes
- CSV file for Module 10 Assignment•1 minute
- Module 10 Recap•5 minutes
1 assignment•Total 30 minutes
- Module 10 Quiz•30 minutes
2 app items•Total 60 minutes
- Module 10 Assignment 1•30 minutes
- Module 10 Assignment 2•30 minutes
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:
What's included
14 videos6 readings1 assignment1 app item
14 videos•Total 84 minutes
- Introduction to Module 11's Study•1 minute
- Artificial Neural Network Introduction: Introduction•3 minutes
- Artificial Neural Network Introduction: ANN Structure•5 minutes
- Neural Network Training: Forward Propagation•6 minutes
- Neural Network Training: Backward Propagation•13 minutes
- Neural Network Training: More on Neural Network Training•8 minutes
- Underfitting and Overfitting: Concepts•8 minutes
- Underfitting and Overfitting: Methods to Improve Overfitting•9 minutes
- ANN Implementation in MATLAB and Case Studies: Build an ANN Using MATLAB•6 minutes
- ANN Implementation in MATLAB and Case Studies: A Case Study on Diabetes Diagnosis•1 minute
- Tutorial: MATLAB Classification and Regression Learner Apps•7 minutes
- Tutorial: Classification Learner•11 minutes
- Tutorial: Export Model•3 minutes
- Tutorial: Regression Learner•4 minutes
6 readings•Total 46 minutes
- Materials on ANN fundamentals•10 minutes
- Materials on Neural Network Training•10 minutes
- Materials on Underfitting and Overfitting•10 minutes
- Materials on ANN Implementation•10 minutes
- MAT files for Module 11 Assignment•1 minute
- Module 11 Recap•5 minutes
1 assignment•Total 30 minutes
- Module 11 Quiz•30 minutes
1 app item•Total 30 minutes
- Module 11 Assignment•30 minutes
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Reviewed on Nov 22, 2025
The support from teaching staff is timely and helpful.
Reviewed on Nov 22, 2025
Finished feeling confident to put “ML skills” on my CV.
Reviewed on Nov 21, 2025
The pacing is perfect: conceptual overview first, then data prep, then deep dives—no cognitive overload at any point.
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