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Course Outline

Overview of Artificial Intelligence

  • Definitions and applications of AI
  • Distinctions among AI, Machine Learning, and Deep Learning
  • Key tools and platforms for government

Python Implementation for AI

  • Refresher on Python fundamentals
  • Operational use of Jupyter Notebook
  • Installation and management of required libraries

Data Management and Preparation

  • Procedures for data cleaning and preprocessing
  • Application of Pandas and NumPy
  • Data visualization techniques using Matplotlib and Seaborn

Foundations of Machine Learning

  • Comparative analysis of supervised and unsupervised learning
  • Core methods: classification, regression, and clustering
  • Protocols for model training, validation, and testing

Neural Networks and Deep Learning Frameworks

  • Principles of neural network architecture
  • Utilization of TensorFlow or PyTorch environments
  • Development and training of model systems

Natural Language Processing and Computer Vision

  • Techniques for text classification and sentiment analysis
  • Fundamentals of image recognition
  • Application of pre-trained models and transfer learning

Deployment of AI Solutions in Applications

  • Standards for saving and loading model assets
  • Integration of AI models into APIs and web applications
  • Best practices for system testing and ongoing maintenance

Summary and Strategic Next Steps

Requirements

  • Competence in programming logic and structural design
  • Hands-on experience with Python or comparable high-level programming languages
  • Foundational knowledge of algorithms and data structures

Audience

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

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