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Course Outline
Introduction to Artificial Intelligence
- What is AI and where is it used?
- AI vs. Machine Learning vs. Deep Learning
- Popular tools and platforms
Python for AI
- Python basics refresher
- Using Jupyter Notebook
- Installing and managing libraries
Working with Data
- Data preparation and cleaning
- Using Pandas and NumPy
- Visualization with Matplotlib and Seaborn
Machine Learning Basics
- Supervised vs. Unsupervised Learning
- Classification, regression, and clustering
- Model training, validation, and testing
Neural Networks and Deep Learning
- Neural network architecture
- Using TensorFlow or PyTorch
- Building and training models
Natural Language and Computer Vision
- Text classification and sentiment analysis
- Image recognition basics
- Pre-trained models and transfer learning
Deploying AI in Applications
- Saving and loading models
- Using AI models in APIs or web apps
- Best practices for testing and maintenance
Summary and Next Steps
Requirements
- An understanding of programming logic and structures
- Experience with Python or similar high-level programming languages
- Basic familiarity with algorithms and data structures
Audience
- IT systems professionals
- Software developers seeking to integrate AI
- Engineers and technical managers exploring AI-based solutions
40 Hours
