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Artificial Intelligence Introductory Training

Artificial Intelligence Introductory Training

Introduction to Artificial Intelligence course is designed to help learners decode the mystery of artificial intelligence and its business applications.

The course provides an overview of AI concepts and workflows, machine learning and deep learning, and performance metrics. You’ll 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.

2 Days Program

Course Outline

Day 1

    1. Introduction to 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

      Fundamentals of Machine 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

Day 2

    1. 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
      • 10 Key takeaways
      • Knowledge Check

      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

FAQ

Who should take the course?

  1. Software developers
  2. IT managers
  3. Service management professionals
  4. Technology Managers
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