Microsoft Azure Machine Learning for Data Scientists from Coursera

Machine learning is at the core of artificial intelligence, and many modern applications and services depend on predictive machine learning models. Training a machine learning model is an iterative process that requires time and compute resources. Automated machine learning can help make it easier. In this course, you will learn how to use Azure Machine Learning to create and publish models without writing code.

This is the second course in a five-course program that prepares you to take the DP-100: Designing and Implementing a Data Science Solution on Azurecertification exam.

The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at a cloud-scale using Azure Machine Learning. This specialization teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Each course teaches you the concepts and skills that are measured by the exam.

This Specialization is intended for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. It teaches data scientists how to create end-to-end solutions in Microsoft Azure. Students will learn how to manage Azure resources for machine learning; run experiments and train models; deploy and operationalize machine learning solutions, and implement responsible machine learning. They will also learn to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning.

What's inside

Syllabus

Use Automated Machine Learning in Azure Machine Learning

Training a machine learning model is an iterative process that requires time and compute resources. Automated machine learning can help make it easier. In this module, you'll learn how to identify different kinds of machine learning model and how to use the automated machine learning capability of Azure Machine Learning to train and deploy a predictive model.

Create a Regression Model with Azure Machine Learning Designer

Regression is a supervised machine learning technique used to predict numeric values. In this module, you will learn how to create regression models using Azure Machine Learning designer.

Create a Classification Model with Azure AI

Classification is a supervised machine learning technique used to predict categories or classes. In this module, you will learn how to create classification models using Azure Machine Learning designer.

Create a Clustering Model with Azure AI

Clustering is an unsupervised machine learning technique used to group similar entities based on their features. In this module, you will learn how to create clustering models using Azure Machine Learning designer.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches how to use Automated Machine Learning to train and deploy a predictive model

Provides hands-on training on creating regression models using Azure Machine Learning designer

Offers practical experience in building classification models with Azure Machine Learning designer

Includes a module on developing clustering models using Azure Machine Learning designer

Involves working with Azure Databricks to explore, prepare, and model data

Provides instruction on integrating Databricks machine learning processes with Azure Machine Learning

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Microsoft Azure Machine Learning for Data Scientists with these activities:

Brush Up on Python and Machine Learning Frameworks

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Review and practice using Python and popular machine learning frameworks like Scikit-Learn, PyTorch, and TensorFlow, ensuring you have a solid foundation to apply in the course.

Browse courses on Python

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Review core Python programming concepts and data structures
Explore tutorials and examples of using machine learning frameworks
Practice implementing simple machine learning algorithms using these frameworks

Review Machine Learning Basics

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Review the theory and fundamental concepts of regression and classification algorithms to strengthen your understanding of building machine learning models before starting the course.

Browse courses on Machine Learning

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Revisit fundamentals of regression and classification algorithms
Explore case studies and examples of these algorithms in practice
Practice implementing simple machine learning models using libraries like Scikit-Learn

Explore Hands-on Azure Machine Learning Tutorials

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Follow interactive tutorials provided by Microsoft or the Azure community to gain practical experience in using Azure Machine Learning, solidifying your understanding of its functionality.

Browse courses on Azure Machine Learning

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Identify tutorials aligned with your learning goals
Set aside dedicated time for completing the tutorials
Follow the step-by-step instructions and experiment with different parameters
Review the results and explore possible optimizations

Five other activities

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Attend a Workshop on Azure Machine Learning

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Participate in a workshop led by experts to gain hands-on experience with Azure Machine Learning's features and capabilities, enabling you to apply them effectively in your projects.

Browse courses on Azure Machine Learning

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Research and identify relevant workshops focusing on Azure Machine Learning
Register and attend the selected workshop
Actively participate in exercises and discussions
Network with professionals and learn from their experiences

Develop a Sample Machine Learning Project with Azure Machine Learning

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Build a mini-project utilizing Azure Machine Learning to solve a real-world problem, reinforcing your understanding of the platform and its capabilities in a practical setting.

Browse courses on Azure Machine Learning

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Identify a suitable problem statement and collect relevant data
Design and implement a machine learning solution using Azure Machine Learning
Train and evaluate the model, optimizing its performance
Document and present your project findings

Participate in Azure Machine Learning Forums and Discussions

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Engage in online forums and discussions dedicated to Azure Machine Learning, offering assistance to others while solidifying your own understanding through knowledge sharing.

Browse courses on Azure Machine Learning

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Join Azure Machine Learning forums and discussion groups
Monitor discussions and identify opportunities to assist others
Provide thoughtful responses and share your knowledge
Learn from the experiences and insights of others

Participate in Azure Machine Learning Challenges and Competitions

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Challenge your skills by participating in Azure Machine Learning competitions, testing your abilities against others and gaining valuable feedback to improve your ML proficiency.

Browse courses on Azure Machine Learning

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Identify relevant Azure Machine Learning challenges or competitions
Form a team or participate individually
Develop and submit your machine learning solutions
Analyze results, learn from feedback, and refine your approach

Compile and Review Course Notes and Resources

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Organize and review your notes, assignments, and resources from the course, reinforcing your understanding of the concepts and providing a valuable reference for future reference or revision.

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Gather and organize notes, assignments, and other course materials
Review and summarize key concepts from each module
Identify areas where further clarification or review is needed

Career center

Learners who complete Microsoft Azure Machine Learning for Data Scientists will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use Azure Machine Learning to develop, deploy and maintain predictive models for businesses. This course teaches learners about the core concepts of Azure Machine Learning, as well as how to use it to create and publish models without writing code. It can help learners to develop models faster and more efficiently.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers are responsible for the design, implementation, and maintenance of machine learning systems. This course can help learners to build a foundation in Azure Machine Learning, as well as learn how to use it to create and deploy machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Data Engineer

Data Engineers are responsible for the design, implementation, and maintenance of data pipelines. This course can help learners to develop the skills necessary to use Azure Machine Learning to create and deploy machine learning models.

See salaries and explore the career path for Data Engineer

Data Analyst

Data Analysts are responsible for analyzing data to identify trends and patterns. This course can help learners to use Azure Machine Learning to create and deploy machine learning models to analyze data more efficiently.

See salaries and explore the career path for Data Analyst

Business Intelligence Analyst

Business Intelligence Analysts are responsible for providing insights to businesses based on data analysis. This course can help learners to develop the skills to use Azure Machine Learning to create machine learning models that can be used to analyze data and make predictions.

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Software Engineer

Software Engineers are responsible for the design, development, and maintenance of software applications. This course can help learners to develop the skills necessary to use Azure Machine Learning to create and deploy machine learning models in software applications.

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Statistician

Statisticians use statistical methods to analyze data. This course can help learners to develop the skills necessary to use Azure Machine Learning to create and deploy machine learning models in their work.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze data. This course can help learners to build a foundation in Azure Machine Learning, as well as learn how to use it to create and deploy machine learning models in their work.

See salaries and explore the career path for Quantitative Analyst

DevOps Engineer

DevOps Engineers are responsible for the design, implementation, and maintenance of software development and delivery processes. This course can help learners to develop the skills necessary to use Azure Machine Learning to create and deploy machine learning models in software development and delivery processes.

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Operations Research Analyst

Operations Research Analysts use mathematical and analytical methods to solve problems in business and industry. This course can help learners to develop the skills necessary to use Azure Machine Learning to create and deploy machine learning models to solve problems in their work.

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Data Architect

Data Architects are responsible for the design and implementation of data management systems. This course can help learners to develop the skills necessary to use Azure Machine Learning to create and deploy machine learning models in data management systems.

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Database Administrator

Database Administrators are responsible for the management and maintenance of databases. This course can help learners to develop the skills necessary to use Azure Machine Learning to create and deploy machine learning models to analyze and manage data in databases.

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Information Security Analyst

Information Security Analysts are responsible for the design and implementation of information security systems. This course can help learners to develop the skills necessary to use Azure Machine Learning to create and deploy machine learning models to enhance information security systems.

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Product Manager

Product Managers are responsible for the development and management of products. This course can help learners to develop the skills necessary to use Azure Machine Learning to create and deploy machine learning models to improve products.

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Cloud Architect

Cloud Architects are responsible for the design and implementation of cloud computing systems. This course can help learners to develop the skills necessary to use Azure Machine Learning to create and deploy machine learning models in cloud computing systems.

See salaries and explore the career path for Cloud Architect