Foundations of Machine Learning
Course Summary
This course provides a comprehensive introduction to machine learning (ML), covering fundamental concepts, algorithms, and practical applications. Participants will learn the mathematical foundations of ML, including supervised and unsupervised learning, model evaluation, and feature engineering. The course will also introduce popular ML libraries such as Scikit-learn and TensorFlow, enabling participants to build and train machine learning models for real-world applications.
Key Learning Outcomes
- Understand the core principles of machine learning and its applications.
- Learn supervised learning (regression, classification) and unsupervised learning (clustering, dimensionality reduction).
- Implement machine learning models using Python and Scikit-learn.
- Evaluate and optimize model performance using cross-validation and performance metrics.
- Gain insights into real-world applications of ML in research and industry.
