Introduction to Artificial Intelligence for Beginners

Introduction to AI Course Overview

This Introduction to AI provides an overview of AI concepts and workflows, machine learning, deep learning, and performance metrics. You??l learn the difference between supervised, unsupervised, and reinforcement learning; be exposed to use cases, and see how clustering and classification algorithms help identify AI business applications.

What you'll learn

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  • Learn Research
  • Collect Usefull Data
  • Requirement Analysis Phase
  • Market competent Skills
  • Problem Solving Skills
  • Model Implementation Skills
  • Building or Developing Phase
  • Presenting and Testing Skills

    Course Includes:

  • 7+ hours on-demand video
  • 7+ articles
  • 20+ downloadable resources
  • Full access
  • Access on mobile and TV
  • Certificate of completion


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Learning — Blended to Perfection

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Introduction to AI Course Curriculum


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.


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

Course Content

Lesson 00 - Course Introduction
Lesson 01 - Decoding Artificial Intelligence
01 Decoding Artificial Intelligence
02 Meaning, Scope, and Stages Of Artificial Intelligence
03 Three Stages of Artificial Intelligence
04 Applications of Artificial Intelligence
05 Image Recognition
06 Applications of Artificial Intelligence - Examples
07 Effects of Artificial Intelligence on Society
08 Supervises Learning for Telemedicine
09 Solves Complex Social Problems
10 Benefits Multiple Industries
11 Key Takeaways
Knowledge Check
Lesson 02 - Fundamentals of Machine Learning and Deep Learning
01 Fundamentals Of Machine Learning and Deep Learning
02 Meaning of Machine Learning
03 Relationship between Machine Learning and Statistical Analysis
04 Process of Machine Learning
05 Types of Machine Learning
06 Meaning of Unsupervised Learning
07 Meaning of Semi-supervised Learning
08 Algorithms of Machine Learning
09 Regression
10 Naive Bayes
11 Naive Bayes Classification
12 Machine Learning Algorithms
13 Deep Learning
14 Artificial Neural Network Definition
15 Definition of Perceptron
16 Online and Batch Learning
17 Key Takeaways
Knowledge Check
Lesson 03 - Machine Learning Workflow
01 Learning Objective
02 Machine Learning Workflow
03 Get more data
04 Ask a Sharp Question
05 Add Data to the Table
06 Check for Quality
07 Transform Features
08 Answer the Questions
09 Use the Answer
11 Key takeaways
Knowledge Check
Lesson 04 - Performance Metrics
01 Performance Metrics
02 Need For Performance Metrics
03 Key Methods Of Performance Metrics
04 Confusion Matrix Example
05 Terms Of Confusion Matrix
06 Minimize False Cases
07 Minimize False Positive Example
08 Accuracy
09 Precision
10 Recall Or Sensitivity
11 Specificity
12 F1 Score
13 Key takeaways
Knowledge Check

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