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Artificial Intelligence

Artificial Intelligence Training Program

This comprehensive program offers an in-depth exploration of artificial intelligence (AI), covering its fundamentals, applications, and advanced techniques. Participants will gain a complete understanding of AI concepts, algorithms, and tools, enabling them to leverage AI for concrete problem-solving and business applications.

Artificial Intelligence Training Program

Overview

Program Objectives

  • •Define and explain fundamental concepts of artificial intelligence.
  • •Identify distinct types of AI and their applications.
  • •Understand the building blocks of AI, including machine learning, deep learning, and natural language processing.
  • •Explore AI applications in various sectors (marketing, finance, healthcare, education, etc.).
  • •Implement machine learning algorithms for data analysis and prediction.
  • •Use deep learning techniques for image recognition, natural language processing, and other complex tasks.
  • •Understand ethical issues and challenges related to AI development and deployment.

Learning Outcomes

  • ✓Explain the history, evolution, and future of AI.
  • ✓Differentiate AI types (narrow, general, super intelligence).
  • ✓Describe key AI components (machine learning, deep learning, NLP).
  • ✓Identify and discuss AI applications in various industries.
  • ✓Implement machine learning algorithms for analysis and prediction.
  • ✓Use deep learning techniques for image recognition, NLP, etc.
  • ✓Analyze ethical issues related to AI.
  • ✓Develop a basic understanding of AI strategy for businesses.

Curriculum

Session I

Introduction to Artificial Intelligence

  • •Introduction to AI
  • •History of AI
  • •Types of AI (functionality/capabilities)
  • •Importance of AI
  • •AI vs Human Intelligence
  • •Building blocks of AI
  • •AI trends and statistics
  • •Summary and quiz
Session II

Applications of Artificial Intelligence

  • •AI applications in: marketing, finance, defense, telecom, healthcare, automotive, gaming, e-commerce, social media, robotics, education, chatbots, agriculture, supply chain, navigation, lifestyle, HR, etc.
  • •Summary and discussion
Session III

Fundamental Concepts of Machine Learning

  • •Overview and history of ML
  • •ML Algorithms
  • •Supervised learning (regression, classification, decision trees, random forests, etc.)
  • •Unsupervised learning (clustering, association, HMM, etc.)
  • •Reinforcement learning
  • •ML Steps
  • •Pros/cons and future of ML
  • •Summary and quiz
Session IV

Machine Learning Use Cases

  • •Automatic translation
  • •Medical diagnosis
  • •Image recognition
  • •Voice recognition
  • •Summary and quiz
Session V

Deep Learning in Brief

  • •Introduction and importance of deep learning
  • •Operation and process of deep learning
  • •ML vs DL comparison
  • •Activation and loss functions, optimizers
  • •Creating DL models
  • •Applications and limits of deep learning
  • •Summary and discussion
Session VI

NLP, NLG, NLU: Fundamentals

  • •Introduction to NLP, NLG, NLU
  • •Techniques, functioning, and applications of NLP
  • •NLP/NLG/NLU comparisons
  • •AI/ML/DL comparisons
  • •Summary
Session VII

Hybrid AI - Machine as Creative Partners

  • •Hybrid model and ANN
  • •CNN, autoencoders, feedforward and recurrent neural networks, mixture density network
  • •Pros/cons and applications
  • •Summary and discussion
Session VIII

AI Strategy for Business

  • •AI strategies for business performance
  • •Assessing AI capabilities
  • •Roadmap and AI implementation plan
  • •ABCDE Framework, myths and facts, top AI jobs
  • •Summary and discussion

Requirements

  • !
    Basics in mathematics and statistics.
  • !
    Programming concepts (optional).
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