Fundamentals of Machine Learning

This course provides a comprehensive introduction to the Fundamentals of Machine Learning, covering both conceptual understanding and practical implementation across modern machine learning workflows. It focuses on building strong core foundations, preparing and evaluating data, applying supervised and unsupervised learning techniques, and implementing scalable machine learning solutions using cloud platforms such as AWS and Azure. Participants will gain hands-on experience in developing, training, evaluating, and optimizing machine learning models, along with exposure to advanced techniques such as GPU-accelerated workflows and MLOps. Real-world use cases, demos, and step-by-step guidance are included to ensure learners can confidently apply machine learning concepts in practical scenarios. By the end of this course, you will be able to learn how to: Understand and explain core machine learning concepts, terminology, and workflows Differentiate between AI, Machine Learning, and Deep Learning Prepare, preprocess, and evaluate data for machine learning models Build and evaluate supervised learning models for classification and regression problems Apply unsupervised learning techniques for clustering and pattern discovery Optimize models using cross-validation, hyperparameter tuning, and performance metrics Leverage GPU-accelerated workflows for large-scale machine learning tasks Design and implement machine learning solutions on AWS Build, manage, and operationalize ML workflows using Azure Machine Learning and MLOps best practices This course facilitates learners with approximately 6:30–7:00 hours of video lectures, delivering a balanced mix of theory and hands-on demonstrations. The course is divided into 6 modules, and each module is further split into focused lessons. To reinforce learning, each module includes assignments in the form of quizzes and in-video questions. Course Modules Module 1: Building Core Concepts and Foundations of Machine Learni