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Course Outline
Introduction to Artificial Intelligence
- Defining AI and its application areas
- Distinguishing AI from Machine Learning and Deep Learning
- Overview of prevalent tools and platforms
Python for AI
- Refresher on Python fundamentals
- Working with Jupyter Notebook
- Installing and managing essential libraries
Data Processing
- Data preparation and cleansing techniques
- Leveraging Pandas and NumPy
- Data visualization using Matplotlib and Seaborn
Machine Learning Fundamentals
- Supervised versus Unsupervised Learning
- Techniques for classification, regression, and clustering
- Model training, validation, and testing protocols
Neural Networks and Deep Learning
- Understanding neural network structures
- Implementation with TensorFlow or PyTorch
- Constructing and training intelligent models
Natural Language and Computer Vision
- Text categorization and sentiment analysis
- Fundamentals of image recognition
- Utilizing pre-trained models and transfer learning
AI Deployment in Applications
- Model persistence: saving and loading
- Integrating AI models into APIs or web applications
- Best practices for ongoing testing and maintenance
Recap and Future Directions
Requirements
- Solid command of programming logic and structural design
- Proficiency in Python or comparable high-level programming languages
- Foundational knowledge of algorithms and data structures
Target Audience
- IT systems experts
- Software developers aiming to incorporate AI capabilities
- Engineers and technical leaders investigating AI-centric 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
