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Course Outline
Introduction to Artificial Intelligence
- What is AI and where is it applied?
- AI versus Machine Learning versus Deep Learning
- Popular tools and platforms
Python for AI
- Refresher on Python basics
- Utilizing 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 versus 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
- Basics 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
- 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
- Engineers and technical managers exploring AI-based solutions
40 Hours
Testimonials (2)
The session was highly interactive and applicable to the business.
Jorge Boscan - Chevron Global Technology Services Company
Course - Advanced GitHub Copilot & AI for Projects and Infrastructure
Machine Translated
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
