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Creating & Deploying Microsoft Azure Machine Learning Studio Solutions

This course will provide an introduction to the power, flexibility and scalability of Azure Machine Learning. You will learn to implement the data science process, to prepare data and integrate data sources for use in machine learning experiments.

Machine Learning and Data Science is an exciting, fast growing field which will provide you with the tools to gain deeper insights from your data. In this course, Creating & Deploying Microsoft Azure Machine Learning Studio Solutions, you'll be Creating & Deploying Microsoft Azure Machine Learning Studio Solutions. First, you’ll explore data import, cleansing, and transformation. Next, you’ll discover training, evaluating and refining Machine Learning Models. Finally, you’ll learn how to deploy and consume Predictive Web Services. When you’re finished with this course, you’ll know how to create data science experiments using a variety of machine learning algorithms using both a visual user interface and code first using Jupyter notebooks and Visual Studio Code.

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What's inside

Syllabus

Course Overview

Getting Started with the Azure Machine Learning Studio

Preparing Data and Data Sources

Cleaning, Normalizing and Transforming Raw Data (Feature Engineering)

Training, Evaluating and Refining Machine Learning Models

Automated Machine Learning

Deployment and Machine Learning Pipelines

Wrapping Up

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops expertise in Azure Machine Learning, a growing tool that's relevant in industry

Taught by recognized instructor Shawn Hainsworth

Covers a range of beginner and intermediate topics

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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 Creating & Deploying Microsoft Azure Machine Learning Studio Solutions with these activities:

Review Data Science Fundamentals

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Reviewing foundational concepts in data science will provide a stronger knowledge base for this course.

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Review linear algebra and statistics.
Refresh your knowledge of data analysis techniques.
Explore fundamental machine learning algorithms.

Join a Study Group or Discussion Forum

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Engaging in discussions and sharing knowledge with peers can enhance your understanding and identify areas for improvement.

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Join an online or offline study group.
Participate in discussions and ask questions.
Share your knowledge and insights.

Follow Tutorials on Azure Machine Learning Studio

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Following tutorials will provide hands-on experience and reinforce concepts covered in the course.

Browse courses on Azure Machine Learning Studio

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Find tutorials on Azure Machine Learning Studio.
Follow the tutorials step-by-step.
Experiment with different features and options.

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Practice Data Cleaning and Transformation

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Regularly practicing these concepts will solidify your understanding and improve your skills.

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Work on practice problems involving data cleaning.
Practice transforming data using different techniques.
Experiment with feature engineering techniques.

Participate in Azure Machine Learning Studio Community Forums

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Participating in community forums will expose you to different perspectives and insights, broadening your understanding.

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Find Azure Machine Learning Studio community forums.
Join the forums and introduce yourself.
Ask questions and participate in discussions.

Create a Resource Compilation on Azure Machine Learning Studio

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Creating a resource compilation will organize and synthesize information, improving your understanding and retention.

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Gather resources on Azure Machine Learning Studio.
Organize resources into categories or topics.
Create a document or website to share your compilation.

Build a Machine Learning Project Using Azure ML Studio

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Working on a project will provide practical experience and demonstrate your understanding of the course material.

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Identify a problem or use case for a machine learning project.
Gather and prepare the necessary data.
Build and train a machine learning model.
Deploy and evaluate your model.

Contribute to Open Source Projects Related to Azure ML

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Contributing to open source projects will provide practical experience, enhance your skills, and build your portfolio.

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Find open source projects related to Azure ML.
Review the project documentation and code.
Contribute fixes, features, or improvements.

Career center

Learners who complete Creating & Deploying Microsoft Azure Machine Learning Studio Solutions will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists are responsible for collecting, analyzing, and interpreting data to help organizations make informed decisions. This course would be particularly helpful for aspiring Data Scientists, as it provides a comprehensive introduction to the Microsoft Azure Machine Learning Studio, a powerful tool for building and deploying machine learning models. By completing this course, learners will gain the skills and knowledge necessary to prepare data, train and evaluate models, and deploy predictive web services.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design, develop, and maintain machine learning models. This course would be a valuable asset for aspiring Machine Learning Engineers, as it provides hands-on experience with the Microsoft Azure Machine Learning Studio. Through this course, learners will develop the skills needed to build and deploy machine learning solutions that can solve real-world problems.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts collect, clean, and analyze data to identify trends and patterns. This course would be beneficial for aspiring Data Analysts, as it provides a foundation in data preparation and transformation. By completing this course, learners will gain the skills necessary to prepare data for analysis and visualization.

See salaries and explore the career path for Data Analyst

Business Intelligence Analyst

Business Intelligence Analysts use data to help businesses make better decisions. This course would be helpful for aspiring Business Intelligence Analysts, as it provides an introduction to machine learning and its applications in business. By completing this course, learners will gain the skills needed to identify and solve business problems using machine learning.

See salaries and explore the career path for Business Intelligence Analyst

Software Engineer

Software Engineers design, develop, and maintain software systems. This course may be useful for aspiring Software Engineers who are interested in developing machine learning applications. By completing this course, learners will gain the skills needed to build and deploy machine learning models using the Microsoft Azure Machine Learning Studio.

See salaries and explore the career path for Software Engineer

Data Engineer

Data Engineers design, build, and maintain data pipelines. This course may be helpful for aspiring Data Engineers who are interested in building and deploying machine learning pipelines. By completing this course, learners will gain the skills needed to prepare data for machine learning models and deploy those models into production.

See salaries and explore the career path for Data Engineer

Statistician

Statisticians collect, analyze, and interpret data to provide insights and make predictions. This course may be helpful for aspiring Statisticians who are interested in using machine learning to analyze data. By completing this course, learners will gain the skills needed to prepare data for machine learning models and interpret the results of those models.

See salaries and explore the career path for Statistician

Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve business problems. This course may be helpful for aspiring Operations Research Analysts who are interested in using machine learning to solve business problems. By completing this course, learners will gain the skills needed to prepare data for machine learning models and interpret the results of those models.

See salaries and explore the career path for Operations Research Analyst

Market Research Analyst

Market Research Analysts collect and analyze data to understand consumer behavior and market trends. This course may be helpful for aspiring Market Research Analysts who are interested in using machine learning to analyze market data. By completing this course, learners will gain the skills needed to prepare data for machine learning models and interpret the results of those models.

See salaries and explore the career path for Market Research Analyst

Financial Analyst

Financial Analysts use data to make investment decisions. This course may be helpful for aspiring Financial Analysts who are interested in using machine learning to analyze financial data. By completing this course, learners will gain the skills needed to prepare data for machine learning models and interpret the results of those models.

See salaries and explore the career path for Financial Analyst

Actuary

Actuaries use mathematical and statistical techniques to assess risk and uncertainty. This course may be helpful for aspiring Actuaries who are interested in using machine learning to assess risk. By completing this course, learners will gain the skills needed to prepare data for machine learning models and interpret the results of those models.

See salaries and explore the career path for Actuary

Quantitative Analyst

Quantitative Analysts use mathematical and statistical techniques to analyze financial data. This course may be helpful for aspiring Quantitative Analysts who are interested in using machine learning to analyze financial data. By completing this course, learners will gain the skills needed to prepare data for machine learning models and interpret the results of those models.

See salaries and explore the career path for Quantitative Analyst

Risk Analyst

Risk Analysts use data to identify and assess risks. This course may be helpful for aspiring Risk Analysts who are interested in using machine learning to identify and assess risks. By completing this course, learners will gain the skills needed to prepare data for machine learning models and interpret the results of those models.

See salaries and explore the career path for Risk Analyst

Insurance Analyst

Insurance Analysts use data to assess risk and determine insurance premiums. This course may be helpful for aspiring Insurance Analysts who are interested in using machine learning to assess risk. By completing this course, learners will gain the skills needed to prepare data for machine learning models and interpret the results of those models.

See salaries and explore the career path for Insurance Analyst

Healthcare Analyst

Healthcare Analysts use data to analyze healthcare trends and improve patient care. This course may be helpful for aspiring Healthcare Analysts who are interested in using machine learning to analyze healthcare data. By completing this course, learners will gain the skills needed to prepare data for machine learning models and interpret the results of those models.

See salaries and explore the career path for Healthcare Analyst

Reading list

We've selected 15 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Creating & Deploying Microsoft Azure Machine Learning Studio Solutions.

The Elements of Statistical Learning

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A classic textbook on statistical learning, providing a comprehensive overview of machine learning methods and techniques.

The Elements of Statistical Learning: Data Mining,...

Hardcover

$$$

Creating & Deploying Microsoft Azure Machine Learning Studio Solutions

What's inside

Syllabus

Good to know

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Activities

Career center

Reading list

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