The Nuts and Bolts of Machine Learning

Google via Coursera

Coursera based on 508 ratings

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Overview

This is the sixth of seven courses in the Google Advanced Data Analytics Certificate. In this course, youll learn about machine learning, which uses algorithms and statistics to teach computer systems to discover patterns in data. Data professionals use machine learning to analyze large amounts of data, solve complex problems, and make accurate predictions. Youll focus on the two main types of machine learning: supervised and unsupervised. You'll learn how to apply different machine learning models to business problems and...

Syllabus

The different types of machine learning

Youll start by exploring the basic concepts of machine learning and the role of machine learning in data science. Then, youll review the four main types of machine learning: supervised, unsupervised, reinforcement, and deep learning.

Workflow for building complex models

Youll 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.

Unsupervised learning techniques

Youll 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, youll learn how to apply two unsupervised machine learning models: clustering and K-means.

Tree-based modeling

Next, youll focus on supervised learning. Youll learn how to test and validate the performance of supervised machine learning models such as decision tree, random forest, and gradient boosting.