MACHINE LEARNING

Description

This course provides a comprehensive introduction to machine learning, focusing on its principles, techniques, and applications. Students will explore supervised, unsupervised, and reinforcement learning methods, developing the skills to build predictive models, identify patterns in data, and make data-driven decisions. Key topics include linear regression, decision trees, clustering, neural networks, and deep learning.

Through hands-on projects and case studies, students will gain practical experience using popular tools and frameworks such as Python, TensorFlow, and scikit-learn. Emphasis will be placed on data preprocessing, feature engineering, model evaluation, and optimization.

By the end of the course, participants will be equipped to tackle real-world problems in areas like healthcare, finance, marketing, and more, as well as understand the ethical implications and challenges of deploying machine learning solutions in diverse contexts. This course is suitable for beginners with a basic understanding of programming and statistics.