Length: 2 Days
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Fundamentals of AI and Machine Learning Training by Tonex

This course provides a foundational understanding of artificial intelligence (AI) and machine learning (ML). Participants will learn about the key concepts, algorithms, and applications of AI and ML, and how to implement these technologies in various domains.

Learning Objectives:

  • Understand the principles of AI and machine learning.
  • Learn about different AI and ML algorithms.
  • Explore applications of AI and ML in various industries.
  • Develop skills to implement AI and ML solutions.
  • Utilize AI and ML tools and software.
  • Stay updated with advancements in AI and ML technologies.

Audience:

  • Data scientists
  • Software engineers
  • IT professionals
  • Business analysts
  • Anyone interested in AI and machine learning

Program Modules:

Module 1: Introduction to Artificial Intelligence

  • Definition and significance of AI
  • History and development of AI
  • Key concepts and principles
  • Types of AI (narrow, general, superintelligent)
  • Applications of AI in various fields
  • Ethical considerations in AI

Module 2: Fundamentals of Machine Learning

  • Definition and significance of machine learning
  • Types of machine learning (supervised, unsupervised, reinforcement)
  • Key algorithms and techniques
  • Data preprocessing and feature engineering
  • Model training and evaluation
  • Tools and frameworks for machine learning

Module 3: Supervised Learning Techniques

  • Regression algorithms
  • Classification algorithms
  • Decision trees and random forests
  • Support vector machines
  • Neural networks and deep learning
  • Case studies on supervised learning

Module 4: Unsupervised Learning Techniques

  • Clustering algorithms
  • Dimensionality reduction techniques
  • Association rule learning
  • Anomaly detection
  • Principal Component Analysis (PCA)
  • Case studies on unsupervised learning

Module 5: Reinforcement Learning

  • Principles of reinforcement learning
  • Markov decision processes
  • Q-learning and deep Q-networks
  • Policy gradients
  • Applications of reinforcement learning
  • Case studies on reinforcement learning

Module 6: Advances and Future Trends in AI and Machine Learning

  • Recent advancements in AI and ML
  • Emerging applications and innovations
  • Challenges and limitations
  • Future directions of AI and ML research
  • Ethical and societal implications
  • Final project: Develop and implement an AI/ML solution

 

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