Introduction to Machine Learning

Introduction to Machine Learning

The Introduction to Machine Learning course will allow you to learn about specific techniques used in supervised, unsupervised, and semi-supervised learning, including which applications each type of machine learning is best suited for and the type of training data each requires.

You will discover how to differentiate offline and online training and predictions, automated machine learning, and how the cloud environment affects machine learning functions. Additionally, you will explore some of the most significant areas in the field of machine learning research.

Syllabus

• Lesson 1 – Introduction to Machine Learning
• Lesson 2 – Which Problems Can Machine Learning Solve?
• Lesson 3 – The Machine Learning Pipeline
• Lesson 4 – Working with Data
• Lesson 5 – Supervised Learning: Regression
• Lesson 6 – Supervised Learning: Classification
• Lesson 7 – Ensemble Methods
• Lesson 8 – Unsupervised Learning
• Lesson 9 – Semi-Supervised Learning
• Lesson 10 – Reinforcement Learning
• Lesson 11 – Building and Deploying Machine Learning Apps
• Lesson 12 – Beyond Machine Learning

Requirements:
Hardware Requirements:

• This course can be taken on either a PC, Mac, or Chromebook.

Software Requirements:

• PC: Windows 8 or later.
• Mac: macOS 10.6 or later.
• Browser: The latest version of Google Chrome or Mozilla Firefox are preferred. Microsoft Edge and Safari are also compatible.
• Adobe Acrobat Reader.
• Software must be installed and fully operational before the course begins.

Other:
Email capabilities and access to a personal email account.

Prerequisites:
The Intro to Machine Learning course will look to build on concepts learned within the Intro to AI course. However, students should still be able to take the ML course without the AI.

Instructional Material Requirements:
The instructional materials required for this course are included in enrollment and will be available online.

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