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Course Outline

Introduction to Artificial Intelligence

  • Defining AI and its applications
  • Distinguishing between AI, Machine Learning, and Deep Learning
  • Overview of popular tools and platforms

Python for AI

  • Refresher on Python fundamentals
  • Utilizing Jupyter Notebook
  • Installing and managing libraries

Working with Data

  • Data preparation and cleaning techniques
  • Leveraging Pandas and NumPy
  • Visualization tools: Matplotlib and Seaborn

Machine Learning Basics

  • Supervised versus Unsupervised Learning
  • Exploring classification, regression, and clustering
  • Model training, validation, and testing processes

Neural Networks and Deep Learning

  • Understanding neural network architecture
  • Applying TensorFlow or PyTorch
  • Building and training models

Natural Language and Computer Vision

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

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

  • A solid understanding of programming logic and structures
  • Prior 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 capabilities
  • Engineers and technical managers exploring AI-based solutions
 40 Hours

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