The Nuts and Bolts of Machine Learning

Instructor: Google Career Certificates

Advanced Level • 1 week at 10 hours a week • Flexible Schedule

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

Performance Tuning
Machine Learning
Advanced Analytics
Data analysis
Python Programming
Supervised Learning
Feature Engineering
Unsupervised Learning
Statistical Machine Learning
Machine Learning Algorithms
Predictive Modeling
Data Ethics

Shareable Certificate

Earn a shareable certificate to add to your LinkedIn profile

Outcomes

  • 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.