Machine Learning ML

Course / Course Details

Machine Learning (ML)

Data Science and Analytics

By - Dr. Muhammad Sajid
1 students
N/A
(0)

Course Requirements

Students are expected to:

Have basic knowledge of programming and statistics.
Participate actively in lectures, laboratory sessions, and practical exercises.
Complete assignments, quizzes, and machine learning projects within specified deadlines.
Use machine learning tools and frameworks for data analysis and model development.
Demonstrate ethical and responsible use of data and AI technologies.
Successfully complete a final project involving the design, implementation, and evaluation of a machine learning solution for a real-world problem.

Course Description

This course introduces the fundamental theories, techniques, and applications of Machine Learning (ML). Students will learn how machines can learn from data to make predictions, classifications, and intelligent decisions. The course covers supervised and unsupervised learning, model evaluation, feature engineering, optimization techniques, and practical implementation of machine learning algorithms. Through hands-on projects and real-world case studies, students will develop the skills required to build, evaluate, and deploy machine learning solutions across various domains.

Course Outcomes

Upon successful completion of this course, students will be able to:

Explain the fundamental concepts, methodologies, and types of machine learning, including supervised, unsupervised, and reinforcement learning.
Apply appropriate machine learning algorithms to solve classification, regression, clustering, and prediction problems using real-world datasets.
Evaluate machine learning models using performance metrics, validation techniques, and model optimization strategies.
Develop end-to-end machine learning solutions, from data preprocessing and feature engineering to model deployment and interpretation.