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Course Outline

Introduction to Artificial Intelligence

  • Understanding 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 processes.
  • Leveraging Pandas and NumPy.
  • Visualization techniques using Matplotlib and Seaborn.

Machine Learning Basics

  • Differences between Supervised and Unsupervised Learning.
  • Classification, regression, and clustering methods.
  • Model training, validation, and testing procedures.

Neural Networks and Deep Learning

  • Architecture of neural networks.
  • 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

  • A solid understanding of programming logic and structures.
  • Practical experience with Python or comparable high-level programming languages.
  • Basic familiarity with algorithms and data structures.

Audience

  • IT systems professionals.
  • Software developers looking to integrate AI.
  • Engineers and technical managers exploring AI-driven solutions.
 40 Hours

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