Azure ML Bootcamp: Machine Learning on the Cloud

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Overview

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This specialization features Coursera Coach! 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 specialization. Learners will gain hands-on experience with Azure Machine Learning (Azure ML), a powerful platform for building, training, and deploying machine learning models. The course starts with foundational concepts such as supervised and unsupervised learning, model evaluation, and applications in industries like healthcare, finance, and retail. You will build the skills to effectively use Azure ML Studio and gain confidence in deploying machine learning models. As the course progresses, you'll dive deeper into the Azure ML ecosystem, covering essential topics like data handling, preprocessing, and advanced deployment strategies using MLOps. Practical demos throughout the specialization will allow you to explore Azure’s tools, including model building with pre-built modules, hyperparameter tuning, and integrating Azure services. You’ll also tackle real-world challenges like data quality issues, model drift, and version control. This specialization is perfect for those interested in machine learning and cloud technologies, ideal for beginners or those with a technical background. A basic understanding of programming or data science is helpful but not required.

Syllabus

Course 1: Foundations of Machine Learning with Azure
Course 2: Building, Evaluating, and Operationalizing ML Models
Course 3: Advanced Deployment, MLOps, and Generative AI in Azure

Courses

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9 hours 13 minutes
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This course features Coursera Coach! 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 master advanced deployment strategies, MLOps, and generative AI using Azure ML Studio. You’ll explore techniques to scale machine learning workloads with parallel processing, distributed training, and serverless deployments, including deployment on edge devices and Kubernetes. Learn to manage machine learning workflows with Azure DevOps, GitHub Actions, and Infrastructure as Code (IaC), ensuring seamless integration and security. You’ll also dive into the fundamentals of generative AI, understanding how models like GPT, DALL·E, and others are revolutionizing the AI landscape, and how to fine-tune these models for specific tasks. Throughout the course, you’ll gain hands-on experience with real-time and batch inference, logging, and model monitoring using Azure Monitor and Application Insights. You will also work with cutting-edge tools to optimize models for inference speed and deploy them in production environments. The course will equip you with the skills to operationalize machine learning models effectively, from deployment to monitoring, ensuring they stay efficient and secure over time. This course is designed for professionals and developers looking to advance their skills in machine learning operations (MLOps) and explore the transformative potential of generative AI models. You will work with practical demos to apply what you learn in real-world scenarios, building deployable models that integrate seamlessly with your existing systems. By the end of the course, you will be able to deploy machine learning models using advanced strategies like distributed training and serverless deployment. Implement MLOps pipelines with Azure DevOps and GitHub Actions for end-to-end automation, and Fine-tune and optimize generative AI models like GPT and DALL·E for customized tasks.
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7 hours 49 minutes
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This course features Coursera Coach! 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 dive into the entire process of building, evaluating, and operationalizing machine learning (ML) models. Starting with data exploration, you'll learn how to select the right algorithms for regression and classification tasks and fine-tune them for optimal performance. As you progress, you'll gain hands-on experience with tools like Azure ML Studio, experimenting with model customization, feature engineering, and advanced algorithms such as XGBoost and Neural Networks. You'll also discover how to evaluate models, optimize performance, and deploy them effectively. The course offers practical demos, empowering you to implement everything from simple models to complex pipelines in ML workflows. Throughout the course, you'll explore model evaluation techniques, including cross-validation and performance metrics, and learn how to address issues like overfitting and model drift. You'll also engage with ML-Ops concepts, discovering how to structure scalable pipelines, automate workflows, and manage the lifecycle of your models. This course is perfect for those looking to gain real-world ML skills, especially those interested in Azure ML Studio and automated pipelines. Whether you’re starting with basic ML concepts or expanding your knowledge, this course provides a comprehensive guide. You’ll learn to make data-driven decisions while optimizing the end-to-end machine learning lifecycle. By the end, you'll be prepared to build and deploy models that are both efficient and scalable, while also staying on top of versioning and performance monitoring.
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7 hours 29 minutes
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This course features Coursera Coach! 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.