Machine Learning Basics Course (How To)

Machine learning encompasses many different ideas, programming languages, frameworks, and approaches to the subject, so the term "machine learning" is difficult to define in just a sentence or two. But essentially, machine learning is giving a computer the ability to write its own rules and learn about new things, on its own.

In this course, we'll explore some of the big ideas, and toward the end, we'll even write a little bit of code in Python that can make some intelligent predictions.

What you'll learn

• Fundamental concepts in machine learning
• Supervised versus Unsupervised learning
• Machine learning frameworks
• Machine learning using Python and scikit-learn

Introduction to Machine Learning

Machine learning actually is not as difficult as you might believe, and its applications are far reaching. You might be surprised where machine learning could show up in your life, and how it might be useful to you both now and in the coming years.

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• What is Machine Learning?

  5:06

• Supervised and Unsupervised Learning

  8:03

• Machine Learning Frameworks

  4:01

• Introduction to Machine Learning

  5 questions

Machine Learning Vocabulary

Machine learning is a huge topic with many new vocabulary terms. In order to learn more, we should formally define some of these new terms first.

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• Examples and Features

  3:02

• Labels and Classifiers

  2:39

• Machine Learning Vocabulary

  5 questions

Writing a Classifier

We're going to use a Python library called scikit-learn, which includes lots of well designed tools for performing common machine learning tasks. We're going to install scikit-learn and its dependencies using Anaconda, which is a Python-based platform focused on data science and machine learning.

Chevron 5 steps

• Installing scikit-learn using Anaconda

  6:07

• Loading a Dataset

  6:59

• Making Predictions with a Classifier

  7:18

• Machine Learning Review

  2:01

• Writing a Classifier

  5 questions