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

Instructor: Bo Liu

Included with

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5 modules

Gain insight into a topic and learn the fundamentals.

47 reviews

Beginner level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

47 reviews

Beginner level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

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

Classification Algorithms

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

5 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the Applied AI for Engineers and Scientists: Foundations Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

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 videos7 readings1 assignment2 app items1 discussion prompt

10 videosTotal 65 minutes

Welcome to the specialization - Applied AI for Engineers and Scientists: Foundations6 minutes
Module 7: Introduction to Week 7's Study2 minutes
Machine Learning Fundamentals: What is Machine Learning6 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 Representation11 minutes
Mapping Inputs to Outputs: Parametric ML Model8 minutes
Mapping Inputs to Outputs: Non-Parametric ML Model8 minutes
Mapping Inputs to Outputs: Evaluate Output3 minutes
MATLAB Implementation: Simple Linear Regression and KNN7 minutes

7 readingsTotal 56 minutes

Specialization and Course Structure10 minutes
Specialization Sample Certificate10 minutes
How to Access MATLAB Online5 minutes
Materials for Machine Learning Fundamentals10 minutes
Materials for Mapping Inputs to Outputs10 minutes
Materials about MATLAB Implementation10 minutes
CSV File for Module 7 Assignment 1 minute

1 assignmentTotal 30 minutes

Module 7 Quiz30 minutes

2 app itemsTotal 60 minutes

Module 7 Assignment 130 minutes
Module 7 Assignment 230 minutes

1 discussion promptTotal 10 minutes

Module 7 Discussion on Machine Learning10 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 videos4 readings1 assignment2 app items1 discussion prompt

7 videosTotal 50 minutes

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

4 readingsTotal 31 minutes

Materials for ML model training10 minutes
Materials on ML Model Evaluation Fundamentals10 minutes
Materials for ML Process10 minutes
CSV file for Module 8 Assignment1 minute

1 assignmentTotal 30 minutes

Module 8 Quiz30 minutes

2 app itemsTotal 60 minutes

Module 8 Assignment 130 minutes
Module 8 Assignment 230 minutes

1 discussion promptTotal 10 minutes

Module 8 Discussion on ML Model Quality10 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 videos6 readings1 assignment3 app items1 discussion prompt

8 videosTotal 38 minutes

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

6 readingsTotal 51 minutes

Materials on Basic Data Cleaning 10 minutes
Materials on Distributions, Outliers and Their Removal10 minutes
Materials on Data Transform10 minutes
Materials on Training/Test Sets Generation10 minutes
Materials on Data Preparation10 minutes
XLS file for Module 9 Assignment1 minute

1 assignmentTotal 30 minutes

Module 9 Quiz30 minutes

3 app itemsTotal 90 minutes

Module 9 Assignment 130 minutes
Module 9 Assignment 230 minutes
Module 9 Assignment 330 minutes

1 discussion promptTotal 10 minutes

Module 9 Discussion on Data Preparation10 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 videos4 readings1 assignment2 app items

12 videosTotal 80 minutes

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

4 readingsTotal 31 minutes

Materials on Support Vector Machine Fundamentals10 minutes
Materials on Support Vector Machines10 minutes
Materials on Support Vector Machine Implementation and Case Studies10 minutes
CSV file for Module 10 Assignment1 minute

1 assignmentTotal 30 minutes

Module 10 Quiz30 minutes

2 app itemsTotal 60 minutes

Module 10 Assignment 130 minutes
Module 10 Assignment 230 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 videos5 readings1 assignment1 app item

14 videosTotal 84 minutes

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

5 readingsTotal 41 minutes

Materials on ANN fundamentals10 minutes
Materials on Neural Network Training10 minutes
Materials on Underfitting and Overfitting10 minutes
Materials on ANN Implementation10 minutes
MAT files for Module 11 Assignment1 minute

1 assignmentTotal 30 minutes

Module 11 Quiz30 minutes

1 app itemTotal 30 minutes

Module 11 Assignment30 minutes

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Instructor

Bo Liu

University of Glasgow

3 Courses3,656 learners

Offered by

University of Glasgow

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Reviewed on Nov 23, 2025

The support from teaching staff is timely and helpful.

Reviewed on Nov 23, 2025

Finished feeling confident to put “ML skills” on my CV.

Reviewed on Nov 22, 2025

The pacing is perfect: conceptual overview first, then data prep, then deep dives—no cognitive overload at any point.

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