Intro to Machine Learning & Transparency
1. Intro to Machine Learning & Transparency¶
Objectives
Understand what machine learning is.
Understand machine learning systems and processes.
Understand machine learning transparency.
Understand and use a basic \(K\)-NN classifier.
Expected time to complete: 4 hours
In this chapter, we start with “what is machine learning?”. We then discuss the system and process of machine learning. Next, we discuss the transparency of machine learning and go through a simple \(K\)-nearest neighbour (\(K\)-NN) classifier. Finally, we list the real-world datasets to be used and machine learning models to be covered in this course, and introduce the organisation of the rest of the book.
Note
If you do not have prior knowledge/experience with linear algebra, Python programming, and probability and statistics, please go through Prerequisites before starting this course.