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
Testimonials (3)
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
Michal Maj - XL Catlin Services SE (AXA XL)
Course - GitHub Copilot for Developers
Trainer able to adjust the course level during training to fit our understanding level on the topic, so that we could gain more useful knowledge that could further help us harness the tools in our daily works.
