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.

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.

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.

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.

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.

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.