Introduction to Artificial Intelligence

Overview:

Introduction:

This Introduction to AI for beginners is ideal for developers aspiring to be AI engineers, as well as for analytics managers, information architects, analytics professionals, and graduates looking to build a career in artificial intelligence or machine learning.
 
Artificial Intelligence is a branch of computer science that involves the development of computer systems that mimic a human brain and enable them to perform tasks that usually require human intelligence. Computers can be trained to accomplish tasks by processing large volumes of data and recognizing patterns in that data using different AI techniques.

Course Objectives:

At the end of this course the participants will be able to:

• Artificial Intelligence is one of the disruptive technologies that have the potential to change the way businesses operate. Companies are harnessing the power of AI and self-driving cars, virtual assistants, facial recognition, personalized shopping are only the initial applications of AI. The demand for skilled AI engineers is soaring and so one should learn AI to become a part of this exciting domain.
• Knowledge of core math concepts like statistics, calculus, linear algebra, and probability along with proficiency in any one programming language helps to get started in AI without much difficulty. Our Introduction to AI course is specifically designed for beginners and covers everything from the basics.

Targeted Audience:

There are no prerequisites for opting for this Introduction to Artificial Intelligence for beginners. It does not require programming or an IT background, making it ideal for professionals from all walks of corporate life. 

Course Outlines:

Unit 1: Introduction to Artificial Intelligence

• Course Introduction 
• Introduction

Unit 2: Decoding Artificial Intelligence

• Decoding Artificial Intelligence
• Meaning, Scope, and Stages of Artificial Intelligence
• Three Stages of Artificial Intelligence
• Applications of Artificial Intelligence
• Image Recognition
• Applications of Artificial Intelligence - Examples
• Effects of Artificial Intelligence on Society
• Supervises Learning for Telemedicine
• Solves Complex Social Problems
• Benefits Multiple Industries
• 11 Key Takeaways
• Knowledge Check

Unit 3: 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
• Key Takeaways
• Knowledge Check

Unit 4: Machine Learning Workflow

• Learning Objective
• 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
• Key takeaways
• Knowledge Check

Unit 5: Performance Metrics

• 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
• Key takeaways
• Knowledge Check