Machine Learning

December 1, 2022

This course aims to provide a comprehensive and practical introduction to the topic, enabling students to develop a strong foundation in the principles and techniques of machine learning. We cover essential topics such as supervised and unsupervised learning, deep learning, and various algorithms. Emphasis is placed on real-world applications and hands-on experience through coding exercises and projects. Throughout the course, students will be exposed to state-of-the-art tools and libraries, including TensorFlow, Keras, and Scikit-learn, to prepare them for solving complex machine learning problems. The primary objectives of the Machine Learning course are to equip students with the skills and knowledge required to design, implement, and evaluate machine learning models. Key objectives of the course include: (1) Developing an understanding of supervised and unsupervised learning algorithms. (2) Exploring deep learning techniques. (3) Gaining familiarity with various machine learning algorithms and their applications. (4) Emphasizing real-world use cases and hands-on coding experience. (5) Familiarizing students with popular tools and libraries like TensorFlow, Keras, and Scikit-learn. (6) Teaching students to critically assess the suitability of machine learning algorithms for specific tasks. (7) Guiding students in fine-tuning and validating their models for optimal performance. By the end of the course, students will have the necessary skills to apply machine learning techniques effectively and make informed decisions in designing and implementing machine learning models