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A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will gain a foundational understanding of machine learning (ML) and how it is implemented on Microsoft Azure's cloud platform. You will begin by learning the fundamental concepts of machine learning, including types of learning, such as supervised, unsupervised, and reinforcement learning. With real-world case studies, you will explore how these ML techniques are applied in industries like healthcare, finance, and retail. You will also be introduced to the most important challenges in machine learning, such as overfitting, underfitting, and data quality concerns. As the course progresses, you'll dive into Azure Machine Learning Studio, understanding its interface, capabilities, and key features such as AutoML, data integration, and model management. You will learn how to set up experiments, connect to data sources, manage resources, and deploy machine learning models efficiently. The course will include practical demos to help solidify your understanding of data preprocessing, from importing and cleaning datasets to splitting and normalizing them for model training. By leveraging Azure’s flexible tools, you'll become comfortable with handling data, building, and deploying machine learning models. This course is designed for beginners and intermediate learners eager to gain hands-on experience with machine learning using Azure. It’s ideal for individuals looking to deepen their ML knowledge, as well as professionals looking to integrate machine learning into business solutions. The prerequisites include a basic understanding of programming and data science concepts, and an eagerness to explore machine learning through a cloud computing platform. By the end of the course, you will be able to build machine learning models, preprocess and clean datasets, utilize Azure’s tools for model training and deployment, and solve common ML challenges such as data imbalances and overfitting.