Machine Learning and its Applications

Machine Learning and its Applications

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

Instructor: Bo Liu

Access provided by KBTG

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

Learn essential concepts and working principles of machine learning algorithms and their application in science and engineering.

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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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

One of the most important applications of AI in engineering is classification and regression using machine learning. After taking this course, students will have a clear understanding of essential concepts in machine learning, and be able to fluently use popular machine learning techniques in science and engineering problems via MATLAB. Among the many machine learning methods, only those with the best performance and are widely used in science and engineering are carefully selected and taught. To avoid students getting lost in details, in contrast to teaching machine learning methods one by one, the first two lectures display the global picture of machine learning, making students clearly understand essential concepts and the working principle of machine learning. Data preparation is then introduced, followed by two popular machine learning methods, support vector machines and artificial neural networks. Practical cases in science and engineering are provided, making sure students have the ability to apply what they have learned in real practice. In addition, MATLAB classification and regression apps, which allow easy access to many machine learning methods, are introduced.

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 videos Total 65 minutes

Welcome to the specialization - Applied AI for Engineers and Scientists: Foundations 6 minutes
Module 7: Introduction to Week 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

7 readings Total 56 minutes

Specialization and Course Structure 10 minutes
Specialization Sample Certificate 10 minutes
How to Access MATLAB Online 5 minutes
Materials for Machine Learning Fundamentals 10 minutes
Materials for Mapping Inputs to Outputs 10 minutes
Materials about MATLAB Implementation 10 minutes
CSV File for Module 7 Assignment 1 minute

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

1 discussion prompt Total 10 minutes

Module 7 Discussion on Machine Learning 10 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 videos Total 50 minutes

Module 8: Introduction to Week 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

4 readings Total 31 minutes

Materials for ML model training 10 minutes
Materials on ML Model Evaluation Fundamentals 10 minutes
Materials for ML Process 10 minutes
CSV file for Module 8 Assignment 1 minute

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

1 discussion prompt Total 10 minutes

Module 8 Discussion on ML Model Quality 10 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 videos Total 38 minutes

Module 9: Introduction to Week 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

6 readings Total 51 minutes

Materials on Basic Data Cleaning 10 minutes
Materials on Distributions, Outliers and Their Removal 10 minutes
Materials on Data Transform 10 minutes
Materials on Training/Test Sets Generation 10 minutes
Materials on Data Preparation 10 minutes
XLS file for Module 9 Assignment 1 minute

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

1 discussion prompt Total 10 minutes

Module 9 Discussion on Data Preparation 10 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 videos Total 80 minutes

Module 10: Introduction to Week 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

4 readings Total 31 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

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 videos5 readings1 assignment1 app item

14 videos Total 84 minutes

Module 11: Introduction to Week 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