Fundamentals of Machine Learning

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Overview

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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 Learning Module 2: ML Development, Data Preparation, and Evaluation Module 3: Unsupervised Learning Techniques – Clustering and Pattern Discovery Module 4: Advanced Machine Learning Techniques and GPU-Accelerated Workflows Module 5: Designing and Implementing Machine Learning Solutions on AWS Module 6: Building & Managing ML Workflows with Azure Machine Learning and MLOps This course is ideal for learners and professionals who want to build a strong foundation in machine learning and progress toward real-world, cloud-based ML implementations using industry-standard tools and best practices.

Syllabus

Building Core Concepts and Foundations of ML

Welcome to Week 1 of the Fundamentals of Machine Learning course. In this week, you will be introduced to the core concepts of machine learning and set clear expectations for what you’ll learn throughout the course. We’ll begin by understanding what machine learning is and how it differs from artificial intelligence and deep learning. You’ll explore the major types of machine learning and gain a foundational understanding of supervised learning, including classification and regression techniques. We’ll also walk through the end-to-end steps involved in building a machine learning solution. By the end of this week, you will have a strong conceptual foundation in machine learning, enabling you to understand key terminology, learning paradigms, and the overall ML lifecycle.

ML Development, Data Preparation, and Evaluation

Welcome to Week 2. This week focuses on the practical aspects of building and evaluating machine learning models. You will learn how to prepare data through preprocessing techniques, select and train appropriate models, and evaluate their performance using standard metrics. Through hands-on demos, you will explore classification tasks, understand confusion matrices, and apply evaluation metrics for both classification and regression models. By the end of the week, you will be able to assess model performance effectively and make informed decisions during the model training and evaluation process.

Unsupervised Learning Techniques: Clustering and Pattern Discovery

Welcome to Week 3. This week, we will dive into unsupervised machine learning techniques used to uncover hidden patterns and structures in data. You will learn the fundamentals of clustering, including K-Means, hierarchical clustering, and density-based clustering, along with hands-on demonstrations. We will also explore association rule mining to understand relationships within datasets. By the end of the week, you will be able to apply unsupervised learning methods to discover insights without labeled data.

Advanced ML Techniques and GPU-Accelerated Workflows

Welcome to Week 4. In this week, we will focus on advanced machine learning techniques and performance optimization. You will be introduced to NVIDIA RAPIDS and learn how GPUs can significantly accelerate data processing and machine learning workflows through hands-on demonstrations. We will explore model optimization techniques such as cross-validation using GridSearch and RandomizedSearch to improve model performance and reliability. Finally, you will learn the fundamentals of time series analysis using the ARIMA model and implement it through practical demos. By the end of the week, you will be able to optimize ML workflows, select well-tuned models, and apply time-series techniques to real-world forecasting problems.

Designing and Implementing Machine Learning Solutions on AWS

Welcome to Week 5. This week focuses on applying machine learning in real-world scenarios. You will learn how to identify suitable machine learning use cases, understand the differences between AI, machine learning, and deep learning, and explore AWS services that support ML workloads. We will also cover how ML and deep learning models are used in production, including serving data for model training and designing effective data ingestion strategies. By the end of the week, you will be able to align ML solutions with business needs and design practical, production-ready ML workflows.

Building & Managing ML Workflows with Azure ML and MLOps

Welcome to Week 6. This week focuses on building and operationalizing machine learning solutions using Azure Machine Learning and MLOps practices. You will learn how to organize and manage Azure Machine Learning environments, understand the role of the Azure Machine Learning workspace, and explore the end-to-end workflow involved in developing, training, and deploying machine learning models. The week also introduces core machine learning concepts, including different types of machine learning tasks, commonly used algorithms, and the use of AutoML to simplify model selection and optimization. By the end of the week, you will be able to design an effective MLOps architecture and implement structured, scalable, and production-ready machine learning workflows using Azure Machine Learning.