Ali Baghban

detail:

This course provides a foundational understanding of machine learning (ML) concepts, techniques, and applications. It is designed for beginners with a basic understanding of programming and mathematics, aiming to bridge the gap between theory and practical implementation. Participants will learn key ML principles, algorithms, and workflows while gaining hands-on experience with real-world datasets. By the end of this course, participants will be able to: Learning Objectives Understand the basic concepts and terminology of machine learning. Distinguish between supervised, unsupervised, and reinforcement learning. Implement fundamental ML algorithms such as linear regression, decision trees, and clustering. Evaluate and improve model performance using appropriate metrics and techniques. Work with ML libraries and frameworks like Scikit-learn, TensorFlow, or Keras. Apply ML methods to solve real-world problems.