Artificial Intelligence and Machine Learning

Artificial intelligence (AI) can be defined as the development of computer systems which can perform tasks, such as recognizing patterns and pictures, understanding language, learning from experience, at par with human intelligence. Now, the question arises how do we define 'Intelligence'? Intelligence is the ability to learn, understand, and make judgments based on reason. It can also be defined as the ability to acquire and apply knowledge to real-world scenarios.

This concept of 'intelligence' forms the basis for the domain of AI. The idea of an 'intelligent' machine was first introduced by Alan Turing in the year 1950 when he proposed a test known as 'imitation game', which is better known as Turing Test. It was aimed to check whether the machine is 'intelligent' or not.



AI and ML Course Objectives

In this course, you will learn about:

  • Explain the definition of artificial intelligence
  • Distinguish between natural intelligence and artificial intelligence
  • Identify the components of AI
  • Explain different approaches to AI
  • Describe the trends accelerating AI
  • Describe the challenges in the development of AI
  • Explain the risks of using AI
  • Describe the applications of AI

Table of Contents Outline

  1. Fundamentals of AI
  2. Problem solving techniques used in AI
  3. Knowledge engineering in AI
  4. Using game theory in AI
  5. Machine Learning in Business
  6. Neural Networks
  7. Deep Learning in AI
  8. Natural Language Processing (NLP)
  9. Influence of AI on Social Media and GIS
  10. AI in sentiment analysis and gaming
  11. AI in core business

Exam Details

Exam Codes Star AI and ML S08-521 (Academy customers use the same codes)
Number of Questions 60
Type of questions Multiple Choice
Length of Test 120 mins
Passing Score 70%
Recommended Experience Beginner with no knowledge of AI. The learner can be from IT or Non-IT domain.
Languages English
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