ML Engineer: Become a Machine Learning Practitioner

This is a hands-on, outcome-based program designed to help learners build the technical foundation and practical engineering skills needed to develop real-world machine learning applications. In this course, you will explore machine learning fundamentals, supervised and unsupervised algorithms, regression, classification, clustering, feature engineering, model validation, ensemble learning, and real-world applications such as sentiment analysis and stock price prediction. Through case studies, live programming labs, quizzes, projects, and complete applications using Python and tools such as Scikit-learn, you will learn how to prepare data, select suitable algorithms, build and evaluate models, optimize performance, and move toward deploying machine learning solutions in production environments.