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E – 1930 Machine Learning

Intended Audience: All Engineers

PDH UNITS: 2

2 STUDENTS ENROLLED

Machine learning is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, in order to make predictions or decisions without being explicitly programmed to do so.  Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks.

Course Outline

This course narrates Applications and types of Machine Learning. Five categories of Machine learning namely supervised learning, unsupervised learning, reinforcement learning, deep learning and deep reinforcement learning are explained in simple language with suitable examples. Algorithms used for supervised and unsupervised learning are narrated with suitable illustrations. Skills required and platform available for Machine Learning is presented with future scope of work.

Learning Objectives

At the successful conclusion of this course, you’ll be able to identify and discuss:

  • What AI can do?
  • Traditional AI
  • About Machine Learning
  • Classification of Machine Learning
  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • Deep learning
  • Deep reinforcement learning
  • Algorithm used
  • Artificial Neural Network
  • Skills required for Machine Learning
  • How to implement Machine Learning

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