This is the sixth of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll learn about machine learning, which uses algorithms and statistics to teach computer systems to discover patterns in data. Data professionals use machine learning to help analyze large amounts of data, solve complex problems, and make accurate predictions. You’ll 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 become familiar with specific models such as Naive Bayes, decision tree, random forest, and more.

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Get started with the course - 00:00
Categorical versus continuous data types and models - 11:41
Machine learning in everyday life - 18:48
Ethics in machine learning - 25:34
Utilize the Python toolbelt for machine learning - 33:16
Machine learning resources for data professionals - 43:41
Review: The different types of machine learning - 48:47
PACE in machine learning: The plan and analyze stages - 50:18
PACE in machine learning: The construct and execute stages - 1:14:59
Review: Workflow for building complex models - 1:32:31
Explore unsupervised learning and K-means - 1:33:49
Evaluate a K-means model - 1:48:01
Review: Unsupervised learning techniques - 2:04:57
Additional supervised learning techniques - 2:06:03
Tune tree-based models - 2:18:16
Bagging - 2:31:38
Boosting - 2:55:43
Review: Tree-based modeling - 3:17:32
Apply your skills to a workplace scenario - 3:19:26
Portfolio project wrap-up - 3:22:32
Course review: The nuts and bolts of machine learning - 3:25:53

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The Nuts and Bolts of Machine Learning | Google Advanced Data Analytics Certificate
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