Machine Learning II: ML Fundamentals and Supervised Learning
About This Course
By the end of this course, the participants will learn the knowledge and practical skills on:
Revision on ML Background
• Motivation for ML
• Examples of ML Applications
• Individual Project Introduction
Primer on Math for ML
• Linear Algebra
• Statistics
• Calculus
• Project Conceptualization
Primer on Python Programming for ML
• Basic Programming Constructs
• Data Structures and Algorithms
• Project Coding Environment Setup
Regression
• Concept of Predicting Continuous Values
• Linear Regression
• Gradient Descent
Classification I
• Concept of Predicting Discrete Labels
• Naive Bayes
• Support Vector Machines (SVMs)
• K-nearest Neighbours (k-NN)
• Project Implementation
Classification II
• Decision Trees
• Artificial Neural Networks (ANNs)
• Deep Learning
• Continuation of Project Implementation
Putting It All Together
• Finalizing Project Implementation
• Project Sharing and Discussion.
What You'll Learn
It also introduces supervised learning techniques, a class of ML techniques based on learning by examples This course is recommended to be taken as a continuation from Machine Learning I or as a standalone for participants with the recommended foundation.
By the end of the course, you'll have a better understanding of the mathematics that underlies ML and the essential Python programming knowledge needed to implement ML algorithms. You will also be able to apply supervised learning techniques to solve sample problems using Python and implement a basic ML application using Python as part of an individual project.
To aid companies in transforming their capabilities through their human capital, and support Singapore’s drive towards becoming a Smart Nation, this course is mapped to the Singapore Economic Development Board’s Singapore Smart Industry Readiness Index:
Dimension 10 - Shop Floor Intelligence
Dimension 11 - Enterprise Intelligence
Dimension 12 - Facility intelligence
Entry Requirements
Diploma holders who possess basic knowledge of the Python programming language or ML fundamentals.