Artificial intelligence Course

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Overview

Artificial intelligence Course Overview

Artificial intelligence (AI) is a research field that studies how to realize intelligent human behaviors on a computer. … The main research topics in AI include: problem-solving, reasoning, planning, natural language understanding, computer vision, automatic programming, machine learning, and so on

Artificial Intelligence (AI) course will provide you with a wide understanding of the concepts of Artificial Intelligence (AI) to make computer programs solve problems and achieve goals in the world. Artificial Intelligence (AI) makes computers perform tasks such as speech recognition, decision-making, and visual perception which normally requires human intelligence that aims to develop intelligent machines by using the tools such as Python, Jupyter, Numpy, SciPy, R, SAS, TensorFlow, Excel, Tableau, AWS, EC2, Amazon S3

In this Artificial Intelligence (AI) course, you will be able to

Understand the basics of AI and how these technologies are re-defining the AI industry

Learn the key terminology used in AI space

Learn major applications of AI through use cases

Understand what is AI, its applications and use cases and how it is transforming our lives

Describe several issues and ethical concerns surrounding AI

Explain terms like Machine Learning, Deep Learning and Neural Networks

Articulate advice from experts about learning and starting a career in AI

About Course

In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks
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What you'll learn?

  • Introduction to Artificial Intelligence domain
  • How Data Science and Artificial Intelligence overlap
  • Importance of Python coding for data analytics
  • The efficient design of Machine Learning systems
  • SAS tool for data analytics, modeling, and visualization
  • Working with Tableau interactive dashboard and reports
  • MS Excel calculations, tables, and formulae
  • R Statistical Computing for Data Science
  • Building of Artificial Neural Networks and Statistical Models
  • Deep Learning techniques and working with TensorFlow

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