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Course Outline
Introduction to Artificial Intelligence
- Overview of AI and its applications across various sectors, including for government operations.
- Differentiating between AI, Machine Learning, and Deep Learning.
- Common tools and platforms used in the development and deployment of AI solutions.
Python for AI
- Refreshing fundamental Python programming concepts essential for AI development.
- Utilizing Jupyter Notebook for interactive data analysis and model building.
- Installing and managing necessary libraries and dependencies for AI projects.
Working with Data
- Techniques for data preparation, cleaning, and preprocessing to ensure high-quality input for models.
- Using Pandas and NumPy for efficient data manipulation and analysis.
- Visualizing data with Matplotlib and Seaborn to gain insights and communicate findings effectively.
Machine Learning Basics
- Understanding the differences between supervised and unsupervised learning methods.
- Exploring classification, regression, and clustering techniques in machine learning.
- Best practices for training, validating, and testing machine learning models to ensure accuracy and reliability.
Neural Networks and Deep Learning
- Overview of neural network architecture and its components.
- Utilizing TensorFlow or PyTorch for building and training deep learning models.
- Practical steps for constructing and optimizing neural networks for various applications.
Natural Language and Computer Vision
- Techniques for text classification and sentiment analysis to extract meaningful insights from textual data.
- Fundamentals of image recognition and computer vision.
- Leveraging pre-trained models and transfer learning to enhance the performance of AI systems.
Deploying AI in Applications
- Methods for saving and loading trained models for deployment.
- Integrating AI models into APIs or web applications for real-world use.
- Best practices for testing, maintenance, and continuous improvement of deployed AI solutions.
Summary and Next Steps
Requirements
- An understanding of programming logic and structures for government applications.
- Experience with Python or similar high-level programming languages used in governmental systems.
- Basic familiarity with algorithms and data structures relevant to public sector projects.
Audience
- IT systems professionals working for government agencies.
- Software developers seeking to integrate AI solutions in government operations.
- Engineers and technical managers exploring AI-based solutions for government initiatives.
40 Hours
Testimonials (1)
Lecturer's knowledge in advanced usage of copilot & Sufficient and efficient practical session
