What You’ll Learn
Identify characteristics of the different types of machine learning
Prepare data for machine learning models
Build and evaluate supervised and unsupervised learning models using Python
Demonstrate proper model and metric selection for a machine learning algorithm
Skills You’ll Gain
Shareable Certificate
Earn a shareable certificate to add to your LinkedIn profile.
Develop Your Specialized Knowledge
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 from Google
There are 5 modules in this course
You’ll start by exploring the basic concepts of machine learning and the role of machine learning in data science. Then, you’ll review the four main types of machine learning: supervised, unsupervised, reinforcement, and deep learning.
You’ll learn how data professionals use a structured workflow for machine learning. You'll identify the main steps of the workflow and the importance of each step in the overall process. Then, you'll learn how to apply specific machine learning models to business problems.
You’ll learn more about one of the major types of machine learning: unsupervised learning. You'll begin by exploring the difference between supervised and unsupervised techniques and the benefits and uses of each approach. Then, you’ll learn how to apply two unsupervised machine learning models: clustering and K-means.
Next, you’ll focus on supervised learning. You’ll learn how to test and validate the performance of supervised machine learning models such as decision tree, random forest, and gradient boosting.
You’ll complete the final end-of-course project by applying different machine learning models to a workplace scenario dataset.