Fundamentals of Machine Learning

Summary

Learn introductory concepts about machine learning.

Description

In this course you’ll learn fundamental concepts about machine learning. First, we’ll review machine learning basics - what it is, why it’s used, and how it relates to data science. Then, we’ll explore the two primary categories that machine learning problems are categorized into - supervised and unsupervised learning. Finally, we’ll review how the machine learning workflow fits into the data science process.

Learning objectives

  • Explain how machine learning is used as an analysis tool in data science.

  • Summarize the relationship between the data science process and the machine learning workflow.

  • Describe the two primary categories that machine learning problems are categorized into.

  • Describe popular machine learning techniques within the two primary categories of machine learning.

  • Determine the machine learning technique that should be used to analyze data in a given real-world scenario.

Prerequisites

  • Beginning knowledge about concepts related to the big data landscape helpful but not required (i.e. big data types, analysis techniques, processing techniques, etc.)

  • We recommend taking the Databricks Academy course "Fundamentals of Big Data" before taking this course.

Learning path

  • This course is part of the Business Leader learning path.

Proof of completion

  • Upon 80% completion of this course, you will receive a proof of completion.