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Overview

Course Description

Artificial Intelligence has been predicted to be the most in-demand job in the coming years. According to IDC, the total spending on products and services that incorporate Augmented Reality and/or Virtual Reality concepts will soar from 11.4 billion as of 2017, to almost 215 billion by the year 2021. This is great news for AI career aspirants as the demand for such IT professionals will reach the sky in the coming years.

Here, one of the booming technologies of computer science is Artificial Intelligence which is ready to create a new revolution in the world by making intelligent machines.The Artificial Intelligence is now all around us. It is currently working with a variety of subfields, ranging from general to specific, such as self-driving cars, playing chess, proving theorems, playing music, Painting, etc.

Who should go for this training?

The following professionals can go for this Introduction to Artificial Intelligence course:

  • Developers aspiring to be AI engineers

  • Analytics managers

  • Information architects

  • Analytics professionals

  • Graduates looking to build a career in artificial intelligence or machine learning

Requirements

  • Computer or laptop or Smartphone with Highspeed Internet Connection

  • There are no prerequisites for doing this Introduction to Artificial Intelligence Course.

  • There is no need of Programming orIT background, making it ideal for professionals from all walks of corporate life.

Course Syllabus

  • What is AI
  • History of AI
  • Processing Power
  • Potential of AI
  • Meaning, Scope, and Stages Of Artificial Intelligence
  • Three Stages of Artificial Intelligence
  • Applications of Artificial Intelligence
  • Image Recognition
  • Effects of Artificial Intelligence on Society
  • Supervises Learning for Telemedicine
  • Solves Complex Social Problems
  • Benefits Multiple Industries
  • Question-Answer Session
  • Fundamentals Of Machine Learning and Deep Learning
  • Meaning of Machine Learning
  • Relationship between Machine Learning and Statistical Analysis
  • Process of Machine Learning
  • Types of Machine Learning
  • Meaning of Unsupervised Learning
  • Meaning of Semi-supervised Learning
  • Algorithms of Machine Learning
  • Regression
  • Naive Bayes
  • Naive Bayes Classification
  • Machine Learning Algorithms
  • Deep Learning
  • Artificial Neural Network Definition
  • Definition of Perceptron
  • Online and Batch Learning
  • Question-Answer Session
  • Machine Learning Workflow
  • Get more data
  • Ask a Sharp Question
  • Add Data to the Table
  • Check for Quality
  • Transform Features
  • Answer the Questions
  • Use the Answer
  • Question-Answer Session
  • What is Performance Metrics?
  • Need For Performance Metrics
  • Key Methods Of Performance Metrics
  • Confusion Matrix Example
  • Terms Of Confusion Matrix
  • Minimize False Cases
  • Minimize False Positive Example
  • Accuracy
  • Precision
  • Recall Or Sensitivity
  • Specificity
  • F1 Score
  • Question-Answer Session

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