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Course Outline

Introduction to Artificial Intelligence

  • Definition of AI and its applications
  • Distinctions between AI, Machine Learning, and Deep Learning
  • Overview of popular tools and platforms

Python for AI

  • Refresher on Python fundamentals
  • Utilizing Jupyter Notebook
  • Installation and management of libraries

Working with Data

  • Data preparation and cleaning processes
  • Leveraging Pandas and NumPy
  • Data visualization with Matplotlib and Seaborn

Machine Learning Basics

  • Supervised vs. Unsupervised Learning
  • Classification, regression, and clustering techniques
  • Model training, validation, and testing

Neural Networks and Deep Learning

  • Neural network architecture
  • Utilizing TensorFlow or PyTorch
  • Constructing and training models

Natural Language and Computer Vision

  • Text classification and sentiment analysis
  • Fundamentals of image recognition
  • Pre-trained models and transfer learning

Deploying AI in Applications

  • Saving and loading models
  • Integrating AI models into APIs or web applications
  • Best practices for testing and maintenance

Summary and Next Steps

Requirements

  • Proficiency in programming logic and structures
  • Experience with Python or comparable high-level programming languages
  • Basic familiarity with algorithms and data structures

Audience

  • IT systems professionals
  • Software developers aiming to integrate AI
  • Engineers and technical managers investigating AI-based solutions
 40 Hours

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