Understanding Machine Learning

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Overview

Understanding Machine Learning is a foundational course designed to introduce learners to the core principles, algorithms, and real-world applications of machine learning. Ideal for beginners and professionals looking to build a strong conceptual base, the course demystifies how machines learn from data and make intelligent decisions without being explicitly programmed. The course begins with key theoretical concepts, including supervised and unsupervised learning, model training, testing, and evaluation. Learners will explore essential algorithms such as linear regression, logistic regression, decision trees, k-means...

Syllabus

What is Machine Learning?

In this chapter, we'll define machine learning and its relation to data science and artificial intelligence. Then, we'll unpack important machine learning jargon and end with the machine learning workflow for building models.

Machine Learning Models

Now that you know the basics of machine learning, let's dive a little bit deeper. At the end of this chapter, you will know the different types of machine learning, as well as how to evaluate and improve your models.

Deep Learning

In this chapter, we'll unpack deep learning beginning with neural networks. Next, we'll take a closer look at two common use-cases for deep learning: computer vision and natural language processing. We'll wrap up the course discussing the limits and dangers of machine learning.