Artificial Intelligence & Machine Learning with Python
Description
This course provides a comprehensive introduction to artificial intelligence and machine learning using Python. Participants learn foundational concepts, apply key algorithms, and practice with popular Python libraries like NumPy, Pandas, and Scikit-learn.
Key Objectives
By the end of this module, participants will be able to:
- Understand foundational AI and machine learning concepts and terminology.
- Use Python libraries such as NumPy, Pandas, and Scikit-learn for data preprocessing and modeling.
- Implement supervised learning algorithms including linear regression, decision trees, and logistic regression.
- Evaluate model performance using cross-validation and relevant metrics.
- Explore advanced topics like ensemble methods, unsupervised learning, and neural networks.
Target Audience
- Roles: Aspiring Data Scientists, Machine Learning Engineers, Analysts, and Software Developers
- Seniority Level: Beginner to intermediate professionals aiming to transition into or strengthen their capabilities in AI/ML
Prerequisite Knowledge
- Basic programming experience (preferably in Python)
- Familiarity with general mathematical and statistical concepts
- No prior experience with ML frameworks required
Classroom
Sessions can be delivered:
- Live online via video conferencing platforms, with recording available for later review
- Interactive workshops with practical exercises, real-time demonstrations, and collaborative activities
- Hybrid approach combining live online delivery with on-site support if needed
The teaching methodology combines presentations, live demonstrations, hands-on exercises, and interactive discussions to ensure participants actively practice AI in realistic work scenarios.
