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Mike West
Applying statistical techniques to your data within Azure Machine Learning Service will often boost model performance. This course will teach you the basics of data cleansing, including basic syntax and functions.
Read more
Applying statistical techniques to your data within Azure Machine Learning Service will often boost model performance. This course will teach you the basics of data cleansing, including basic syntax and functions.
Read more
Applying statistical techniques to your data within Azure Machine Learning Service will often boost model performance. This course will teach you the basics of data cleansing, including basic syntax and functions.
At the core of applied machine learning is data. In this course, Building Features from Nominal and Numeric Data in Microsoft Azure, you will learn how to cleanse data within the confines of Azure Machine Learning Service. First, you will discover the sundry options you have within Azure Machine Learning Service for building your models end to end. Next, you will explore the importance of applying statistical techniques to your data to improve model performance. Finally, you will learn how to apply various data cleansing techniques to your data for enhancing real-world performance. When you are finished with this course, you will have a foundational knowledge of Azure Machine Learning Service and a solid understating of how to apply statistical techniques to your data that will help you as you move forward to becoming a machine learning engineer.
Register for this course and see more details by visiting:
OpenCourser.com/course/xuupl5/building
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What's inside
Syllabus
Course Overview
Setting the Stage
Approaching Normalization and Standardization
Defining Normalization and Standardization Techniques
Read more
Syllabus
Course Overview
Setting the Stage
Approaching Normalization and Standardization
Defining Normalization and Standardization Techniques
Read more
Leveraging Nominal Data in Machine Learning
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores basic syntax and functions, which is standard in industry
Develops data cleansing and statistical techniques, which are core for enhancing real-world machine learning performance
Taught by Mike West, who is recognized for their work in machine learning
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Save Building Features from Nominal and Numeric Data in Microsoft Azure to your list so you can find it easily later:
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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 Building Features from Nominal and Numeric Data in Microsoft Azure with these
activities:
Review Azure Machine Learning Service
Show steps
Review the basics of Azure Machine Learning Service to refresh your knowledge and better prepare for the course.
Browse courses on
Azure Machine Learning Service
Show steps
-
Read the Azure Machine Learning Service documentation
-
Create a free Azure account and explore the Azure Machine Learning Service portal
-
Follow the Azure Machine Learning Service Quickstart tutorial
Join a Study Group
Show steps
Join a study group to discuss the course material and collaborate on projects.
Show steps
-
Find a study group or create your own
-
Meet regularly to discuss the material
-
Work together on projects
Data Cleansing Practice Exercises
Show steps
Complete some practice exercises to reinforce your understanding of data cleansing techniques.
Browse courses on
Data Cleansing
Show steps
-
Download the data cleansing practice dataset
-
Use Python or R to cleanse the data
-
Compare your results with the provided solutions
Two other activities
Expand to see all activities and additional details
Show all five activities
Follow a Tutorial on Statistical Techniques for Data Cleansing
Show steps
Follow a tutorial to learn how to apply statistical techniques to your data to improve model performance.
Browse courses on
Data Normalization
Show steps
-
Find a tutorial on statistical techniques for data cleansing
-
Follow the tutorial and complete the exercises
-
Apply the techniques to your own data
Mentor a Junior Data Scientist
Show steps
Mentoring others can help you solidify your own understanding of data cleansing techniques.
Show steps
-
Find a junior data scientist to mentor
-
Share your knowledge and experience
-
Provide feedback and guidance
Review Azure Machine Learning Service
Show steps
Review the basics of Azure Machine Learning Service to refresh your knowledge and better prepare for the course.
Browse courses on
Azure Machine Learning Service
Show steps
Show steps
Show steps
- Read the Azure Machine Learning Service documentation
- Create a free Azure account and explore the Azure Machine Learning Service portal
- Follow the Azure Machine Learning Service Quickstart tutorial
Join a Study Group
Show steps
Join a study group to discuss the course material and collaborate on projects.
Show steps
Show steps
Show steps
- Find a study group or create your own
- Meet regularly to discuss the material
- Work together on projects
Data Cleansing Practice Exercises
Show steps
Complete some practice exercises to reinforce your understanding of data cleansing techniques.
Browse courses on
Data Cleansing
Show steps
Show steps
Show steps
- Download the data cleansing practice dataset
- Use Python or R to cleanse the data
- Compare your results with the provided solutions
Two other activities
Expand to see all activities and additional details
Show all five activities
Follow a Tutorial on Statistical Techniques for Data Cleansing
Show steps
Follow a tutorial to learn how to apply statistical techniques to your data to improve model performance.
Browse courses on
Data Normalization
Show steps
Show steps
Show steps
- Find a tutorial on statistical techniques for data cleansing
- Follow the tutorial and complete the exercises
- Apply the techniques to your own data
Mentor a Junior Data Scientist
Show steps
Mentoring others can help you solidify your own understanding of data cleansing techniques.
Show steps
Show steps
Show steps
- Find a junior data scientist to mentor
- Share your knowledge and experience
- Provide feedback and guidance
Career center
Learners who complete Building Features from Nominal and Numeric Data in Microsoft Azure will develop knowledge and skills
that may be useful to these careers:
Data Engineer
Data Engineer
Data Engineers build and maintain the infrastructure and tools that allow data to be collected, stored, and analyzed. They work with data scientists and other stakeholders to ensure that the data is clean, accurate, and accessible. A course like Building Features from Nominal and Numeric Data in Microsoft Azure can help Data Engineers build a foundation in the statistical techniques that are used to cleanse and prepare data for analysis. The course also covers how to use Azure Machine Learning Service to build end-to-end machine learning models, which can be used to automate data cleansing and preparation tasks.
Data Scientist
Data Scientist
Data Scientists use statistical techniques and machine learning algorithms to extract insights from data. They work with businesses to identify and solve problems using data-driven solutions. A course like Building Features from Nominal and Numeric Data in Microsoft Azure can help Data Scientists build a foundation in the statistical techniques that are used to prepare data for analysis. The course also covers how to use Azure Machine Learning Service to build end-to-end machine learning models, which can be used to automate data cleansing and preparation tasks.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and deploy machine learning models. They work with data scientists and other stakeholders to ensure that machine learning models are accurate, reliable, and scalable. A course like Building Features from Nominal and Numeric Data in Microsoft Azure can help Machine Learning Engineers build a foundation in the statistical techniques that are used to prepare data for analysis. The course also covers how to use Azure Machine Learning Service to build end-to-end machine learning models, which can be used to automate data cleansing and preparation tasks.
Statistician
Statistician
Statisticians use statistical techniques to collect, analyze, and interpret data. They work with businesses and organizations to help them make informed decisions based on data. A course like Building Features from Nominal and Numeric Data in Microsoft Azure can help Statisticians build a foundation in the statistical techniques that are used to prepare data for analysis. The course also covers how to use Azure Machine Learning Service to build end-to-end machine learning models, which can be used to automate data cleansing and preparation tasks.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They work with businesses and organizations to help them make informed decisions based on data. A course like Building Features from Nominal and Numeric Data in Microsoft Azure can help Data Analysts build a foundation in the statistical techniques that are used to prepare data for analysis. The course also covers how to use Azure Machine Learning Service to build end-to-end machine learning models, which can be used to automate data cleansing and preparation tasks.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make informed decisions. They work with stakeholders to identify problems and opportunities, and develop solutions that can improve business performance. A course like Building Features from Nominal and Numeric Data in Microsoft Azure can help Business Analysts build a foundation in the statistical techniques that are used to prepare data for analysis. The course also covers how to use Azure Machine Learning Service to build end-to-end machine learning models, which can be used to automate data cleansing and preparation tasks.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with businesses and organizations to create software solutions that meet their specific needs. A course like Building Features from Nominal and Numeric Data in Microsoft Azure can help Software Engineers build a foundation in the statistical techniques that are used to prepare data for analysis. The course also covers how to use Azure Machine Learning Service to build end-to-end machine learning models, which can be used to automate data cleansing and preparation tasks. This knowledge can be helpful for Software Engineers who are working on data-driven applications.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use statistical techniques and mathematical models to analyze financial data. They work with investment banks and other financial institutions to help them make informed investment decisions. A course like Building Features from Nominal and Numeric Data in Microsoft Azure can help Quantitative Analysts build a foundation in the statistical techniques that are used to prepare data for analysis. The course also covers how to use Azure Machine Learning Service to build end-to-end machine learning models, which can be used to automate data cleansing and preparation tasks.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to understand consumer behavior. They work with businesses and organizations to help them develop marketing strategies that are effective in reaching their target audience. A course like Building Features from Nominal and Numeric Data in Microsoft Azure can help Market Researchers build a foundation in the statistical techniques that are used to prepare data for analysis. The course also covers how to use Azure Machine Learning Service to build end-to-end machine learning models, which can be used to automate data cleansing and preparation tasks.
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists use data visualization tools to create visual representations of data. They work with businesses and organizations to help them communicate insights from data in a clear and concise way. A course like Building Features from Nominal and Numeric Data in Microsoft Azure can help Data Visualization Specialists build a foundation in the statistical techniques that are used to prepare data for analysis. The course also covers how to use Azure Machine Learning Service to build end-to-end machine learning models, which can be used to automate data cleansing and preparation tasks.
Database Administrator
Database Administrator
Database Administrators design, implement, and maintain databases. They work with businesses and organizations to ensure that their data is secure, reliable, and accessible. A course like Building Features from Nominal and Numeric Data in Microsoft Azure can help Database Administrators build a foundation in the statistical techniques that are used to prepare data for analysis. The course also covers how to use Azure Machine Learning Service to build end-to-end machine learning models, which can be used to automate data cleansing and preparation tasks.
Information Security Analyst
Information Security Analyst
Information Security Analysts protect computers and networks from unauthorized access, use, disclosure, disruption, modification, or destruction. They work with businesses and organizations to develop and implement security measures that protect their data and systems from cyber threats. A course like Building Features from Nominal and Numeric Data in Microsoft Azure may be useful for Information Security Analysts who are working with data-driven security systems. The course covers how to use Azure Machine Learning Service to build end-to-end machine learning models, which can be used to automate data cleansing and preparation tasks for security analysis.
Computer Systems Analyst
Computer Systems Analyst
Computer Systems Analysts design, implement, and maintain computer systems. They work with businesses and organizations to ensure that their computer systems are efficient, reliable, and secure. A course like Building Features from Nominal and Numeric Data in Microsoft Azure may be useful for Computer Systems Analysts who are working with data-driven systems. The course covers how to use Azure Machine Learning Service to build end-to-end machine learning models, which can be used to automate data cleansing and preparation tasks for system analysis.
Network Administrator
Network Administrator
Network Administrators design, implement, and maintain computer networks. They work with businesses and organizations to ensure that their networks are efficient, reliable, and secure. A course like Building Features from Nominal and Numeric Data in Microsoft Azure may be useful for Network Administrators who are working with data-driven networks. The course covers how to use Azure Machine Learning Service to build end-to-end machine learning models, which can be used to automate data cleansing and preparation tasks for network analysis.
Web Developer
Web Developer
Web Developers design, develop, and maintain websites and web applications. They work with businesses and organizations to create websites that are user-friendly, informative, and effective in achieving their business goals. A course like Building Features from Nominal and Numeric Data in Microsoft Azure may be useful for Web Developers who are working with data-driven websites. The course covers how to use Azure Machine Learning Service to build end-to-end machine learning models, which can be used to automate data cleansing and preparation tasks for web development.
For more career information including salaries, visit:
OpenCourser.com/course/xuupl5/building
Reading list
We've selected 12 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
Building Features from Nominal and Numeric Data in Microsoft Azure.
Provides a comprehensive overview of machine learning with R. It covers a wide range of topics, including data preparation, model building, and model evaluation. It valuable resource for anyone who wants to learn more about machine learning.
Provides a comprehensive overview of mathematics for machine learning. It covers a wide range of topics, including linear algebra, calculus, and probability. It valuable resource for anyone who wants to learn more about mathematics for machine learning.
Provides a comprehensive overview of statistical methods for machine learning. It covers a wide range of topics, including data preparation, model building, and model evaluation. It valuable resource for anyone who wants to learn more about statistical methods for machine learning.
Provides a comprehensive overview of machine learning with Python. It covers a wide range of topics, including data preparation, model building, and model evaluation. It valuable resource for anyone who wants to learn more about machine learning.
Provides a comprehensive overview of natural language processing with Python. It covers a wide range of topics, including text preprocessing, feature engineering, and model evaluation. It valuable resource for anyone who wants to learn more about natural language processing.
Provides a comprehensive overview of deep learning with Python. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for anyone who wants to learn more about deep learning.
Provides a comprehensive overview of Python for data analysis. It covers a wide range of topics, including data preparation, data manipulation, and data visualization. It valuable resource for anyone who wants to learn more about Python for data analysis.
Provides a comprehensive overview of machine learning techniques for text analysis with Python. It covers a wide range of topics, including text preprocessing, feature engineering, and model evaluation. It valuable resource for anyone who wants to learn more about machine learning for text analysis.
Provides a comprehensive overview of machine learning with R. It covers a wide range of topics, including data preparation, model building, and model evaluation. It valuable resource for anyone who wants to learn more about machine learning.
Provides a collection of recipes for common Azure Machine Learning tasks. It valuable resource for anyone who wants to learn how to use Azure Machine Learning to solve real-world problems.
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Provides a gentle introduction to machine learning with Python. It covers a wide range of topics, including data preparation, model building, and model evaluation. It valuable resource for anyone who wants to learn more about machine learning.
Provides a gentle introduction to machine learning. It covers a wide range of topics, including data preparation, model building, and model evaluation. It valuable resource for anyone who wants to learn more about machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/xuupl5/building
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Time
80 hours
Level
Introductory
Via
Pluralsight
Instructor
Mike West
Language
English
Good to know
Explores basic syntax and functions, which is standard in industry
Develops data cleansing and statistical techniques, which are core for enhancing real-world machine learning performance
Taught by Mike West, who is recognized for their work in machine learning
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