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Xavier Morera
One of the most important aspects of Machine Learning is using the right data in the right format for your models. In this course you will learn how to extract, normalize, and select the best features for your models using Azure Machine Learning Studio.
Read more
One of the most important aspects of Machine Learning is using the right data in the right format for your models. In this course you will learn how to extract, normalize, and select the best features for your models using Azure Machine Learning Studio.
Read more
One of the most important aspects of Machine Learning is using the right data in the right format for your models. In this course you will learn how to extract, normalize, and select the best features for your models using Azure Machine Learning Studio.
It is no secret that Data Scientists spend a very large proportion of their time preparing data. In this course, Feature Selection and Extraction in Microsoft Azure, you'll gain the ability to prepare your data for use in your machine learning models. First, you'll learn how to extract features from raw data, including non-text formats. Next, you'll discover how to normalize features, converting your data to a common scale without distorting your data. Finally, you'll explore how to select those features that are more relevant to your model. When you're finished with this course, you'll have the skills and knowledge of feature extraction, normalization, and selection needed to prepare your data. Software required: Azure ML Studio classic.
Register for this course and see more details by visiting:
OpenCourser.com/course/yv72fn/feature
What's inside
Syllabus
Course Overview
Exploring Your Dataset for Feature Selection and Extraction
Performing Feature Extraction
Performing Feature Normalization
Read more
Syllabus
Course Overview
Exploring Your Dataset for Feature Selection and Extraction
Performing Feature Extraction
Performing Feature Normalization
Read more
Performing Feature Selection
Final Takeaway
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers data preparation, which is essential for machine learning models
Introduces Microsoft's Azure ML Studio classic for practical application
Teaches in-demand skills for data scientists, who spend a significant amount of time preparing data
Targeted at learners with a solid understanding of machine learning concepts
Save this course
Save Feature Selection and Extraction 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 Feature Selection and Extraction in Microsoft Azure with these
activities:
Review 'Feature Engineering for Machine Learning'
Show steps
Provide a strong foundation of knowledge for this course by reviewing the key concepts of feature engineering.
View
Feature Engineering for Machine Learning:...
on Amazon
Show steps
-
Read the first four chapters of the book.
-
Summarize the main concepts of feature engineering.
-
Identify the different types of feature engineering techniques.
-
Discuss the pros and cons of different feature engineering techniques.
Follow Azure ML Studio documentation
Show steps
Expand your knowledge of Azure ML Studio by reviewing the official documentation.
Browse courses on
Azure Machine Learning Studio
Show steps
-
Read the Azure ML Studio documentation on feature selection.
-
Read the Azure ML Studio documentation on feature extraction.
-
Read the Azure ML Studio documentation on feature normalization.
Follow Azure ML Studio tutorials
Show steps
Build a stronger understanding of the tools and techniques used in this course by completing tutorials provided by Azure ML Studio.
Browse courses on
Azure Machine Learning Studio
Show steps
-
Complete the 'Introduction to Azure Machine Learning Studio' tutorial.
-
Complete the 'Data Preparation with Azure Machine Learning Studio' tutorial.
-
Complete the 'Feature Scaling with Azure Machine Learning Studio' tutorial.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Organize and review notes
Show steps
Enhance your understanding and retention of the course material by organizing and reviewing your notes.
Show steps
-
Gather all of your notes from the course.
-
Organize your notes by topic.
-
Review your notes regularly.
Practice feature normalization
Show steps
Strengthen your understanding of feature normalization by practicing with different machine learning algorithms.
Browse courses on
Data Scaling
Show steps
-
Use the Azure ML Studio to normalize features for a regression problem.
-
Use the Azure ML Studio to compare the performance of different feature normalization algorithms.
-
Use the Azure ML Studio to apply feature normalization to a real-world dataset.
Practice feature selection and extraction
Show steps
Strengthen your understanding of feature selection and extraction by practicing with different machine learning algorithms.
Browse courses on
Feature Selection
Show steps
-
Use the Azure ML Studio to select features for a classification problem.
-
Use the Azure ML Studio to extract features from a text dataset.
-
Use the Azure ML Studio to compare the performance of different feature selection algorithms.
Create a data preparation pipeline
Show steps
Combine the skills and knowledge acquired in this course by creating a data preparation pipeline that includes feature selection and extraction.
Browse courses on
Data Preparation
Show steps
-
Design the data preparation pipeline.
-
Implement the data preparation pipeline using Azure ML Studio.
-
Evaluate the performance of the data preparation pipeline.
Review 'Feature Engineering for Machine Learning'
Show steps
Provide a strong foundation of knowledge for this course by reviewing the key concepts of feature engineering.
View
Feature Engineering for Machine Learning:...
on Amazon
Show steps
Show steps
Show steps
- Read the first four chapters of the book.
- Summarize the main concepts of feature engineering.
- Identify the different types of feature engineering techniques.
- Discuss the pros and cons of different feature engineering techniques.
Follow Azure ML Studio documentation
Show steps
Expand your knowledge of Azure ML Studio by reviewing the official documentation.
Browse courses on
Azure Machine Learning Studio
Show steps
Show steps
Show steps
- Read the Azure ML Studio documentation on feature selection.
- Read the Azure ML Studio documentation on feature extraction.
- Read the Azure ML Studio documentation on feature normalization.
Follow Azure ML Studio tutorials
Show steps
Build a stronger understanding of the tools and techniques used in this course by completing tutorials provided by Azure ML Studio.
Browse courses on
Azure Machine Learning Studio
Show steps
Show steps
Show steps
- Complete the 'Introduction to Azure Machine Learning Studio' tutorial.
- Complete the 'Data Preparation with Azure Machine Learning Studio' tutorial.
- Complete the 'Feature Scaling with Azure Machine Learning Studio' tutorial.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Organize and review notes
Show steps
Enhance your understanding and retention of the course material by organizing and reviewing your notes.
Show steps
Show steps
Show steps
- Gather all of your notes from the course.
- Organize your notes by topic.
- Review your notes regularly.
Practice feature normalization
Show steps
Strengthen your understanding of feature normalization by practicing with different machine learning algorithms.
Browse courses on
Data Scaling
Show steps
Show steps
Show steps
- Use the Azure ML Studio to normalize features for a regression problem.
- Use the Azure ML Studio to compare the performance of different feature normalization algorithms.
- Use the Azure ML Studio to apply feature normalization to a real-world dataset.
Practice feature selection and extraction
Show steps
Strengthen your understanding of feature selection and extraction by practicing with different machine learning algorithms.
Browse courses on
Feature Selection
Show steps
Show steps
Show steps
- Use the Azure ML Studio to select features for a classification problem.
- Use the Azure ML Studio to extract features from a text dataset.
- Use the Azure ML Studio to compare the performance of different feature selection algorithms.
Create a data preparation pipeline
Show steps
Combine the skills and knowledge acquired in this course by creating a data preparation pipeline that includes feature selection and extraction.
Browse courses on
Data Preparation
Show steps
Show steps
Show steps
- Design the data preparation pipeline.
- Implement the data preparation pipeline using Azure ML Studio.
- Evaluate the performance of the data preparation pipeline.
Career center
Learners who complete Feature Selection and Extraction in Microsoft Azure will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers apply Machine Learning algorithms to real-world problems by building, deploying, and maintaining Machine Learning models. Feature Selection and Extraction in Microsoft Azure covers essential data preparation tasks for Machine Learning Engineers, including feature extraction, normalization, and selection. By taking this course, you gain valuable skills for a successful career as a Machine Learning Engineer.
Data Scientist
Data Scientist
A Data Scientist extracts, transforms, and prepares data for use in Machine Learning models and works closely with Data Analysts and Data Engineers. This course, Feature Selection and Extraction in Microsoft Azure, provides essential skills for a Data Scientist by covering feature extraction, normalization, and selection. By learning how to work with data in a structured way, you build your foundation for success as a Data Scientist.
Data Analyst
Data Analyst
Data Analysts discover insights and trends in data to help businesses make informed decisions. Feature Selection and Extraction in Microsoft Azure provides foundational skills for Data Analysts by covering data extraction, normalization, and selection. By leveraging this knowledge, Data Analysts can effectively prepare data for analysis, enhancing their ability to drive data-driven decision-making.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines to ensure the availability and quality of data for analysis. Feature Selection and Extraction in Microsoft Azure provides valuable knowledge for Data Engineers, as it covers essential data preparation tasks. By learning how to extract, normalize, and select features, Data Engineers can effectively prepare data for analysis, ensuring the data is accurate and consistent for downstream applications.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to provide insights and recommendations to help businesses improve their operations. Feature Selection and Extraction in Microsoft Azure is a valuable resource for Business Intelligence Analysts, as it provides essential skills for data preparation. By learning how to extract, normalize, and select features, Business Intelligence Analysts can ensure the data they use for analysis is accurate and relevant, leading to more effective insights and recommendations.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data to help businesses and organizations make informed decisions. Feature Selection and Extraction in Microsoft Azure can be a useful resource for Statisticians, as it covers essential data preparation tasks. By learning how to extract, normalize, and select features, Statisticians can ensure the data they use for analysis is accurate and relevant, leading to more reliable and insightful results.
Database Administrator
Database Administrator
Database Administrators ensure the availability, performance, and security of databases. Feature Selection and Extraction in Microsoft Azure may be useful for Database Administrators who want to enhance their data preparation skills. By learning how to extract, normalize, and select features, Database Administrators can improve the quality and efficiency of data retrieval and analysis.
Data Architect
Data Architect
Data Architects design and manage data architectures to ensure the availability, reliability, and scalability of data. Feature Selection and Extraction in Microsoft Azure may be useful for Data Architects who want to enhance their data preparation skills. By learning how to extract, normalize, and select features, Data Architects can improve the quality and efficiency of data integration and management within their data architectures.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. Feature Selection and Extraction in Microsoft Azure may be useful for Software Engineers who want to gain knowledge in data preparation. By learning how to extract, normalize, and select features, Software Engineers can improve the quality and efficiency of data integration and processing within their software applications.
Information Security Analyst
Information Security Analyst
Information Security Analysts protect computer systems and networks from unauthorized access, use, disclosure, disruption, modification, or destruction. Feature Selection and Extraction in Microsoft Azure may be useful for Information Security Analysts who want to enhance their data preparation skills. By learning how to extract, normalize, and select features, Information Security Analysts can improve the quality and efficiency of data analysis for security monitoring and incident response.
Computer Scientist
Computer Scientist
Computer Scientists research and develop new computing technologies and applications. Feature Selection and Extraction in Microsoft Azure may be useful for Computer Scientists who want to gain knowledge in data preparation. By learning how to extract, normalize, and select features, Computer Scientists can improve the quality and efficiency of data analysis and modeling for their research and development projects.
Actuary
Actuary
Actuaries assess and manage financial risks for insurance companies and other financial institutions. Feature Selection and Extraction in Microsoft Azure may be useful for Actuaries who want to enhance their data preparation skills. By learning how to extract, normalize, and select features, Actuaries can improve the quality and efficiency of data analysis for risk assessment and insurance product pricing.
Financial Analyst
Financial Analyst
Financial Analysts evaluate and make recommendations on investments. Feature Selection and Extraction in Microsoft Azure may be useful for Financial Analysts who want to enhance their data preparation skills. By learning how to extract, normalize, and select features, Financial Analysts can improve the quality and efficiency of data analysis for investment research and portfolio management.
Market Researcher
Market Researcher
Market Researchers gather and analyze data on consumer behavior and market trends to help businesses make informed decisions. Feature Selection and Extraction in Microsoft Azure may be useful for Market Researchers who want to enhance their data preparation skills. By learning how to extract, normalize, and select features, Market Researchers can improve the quality and efficiency of data analysis for market segmentation and product development.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. Feature Selection and Extraction in Microsoft Azure may be useful for Quantitative Analysts who want to enhance their data preparation skills. By learning how to extract, normalize, and select features, Quantitative Analysts can improve the quality and efficiency of data analysis for risk assessment and portfolio management.
For more career information including salaries, visit:
OpenCourser.com/course/yv72fn/feature
Reading list
We've selected 11 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
Feature Selection and Extraction in Microsoft Azure.
Covers feature engineering techniques including feature extraction, transformation, and selection. It also discusses how to evaluate feature engineering and provides case studies in various domains like finance, healthcare, and manufacturing.
Provides practical guidance on feature engineering for machine learning models, including feature extraction, transformation, and selection. It also covers feature engineering techniques specifically for natural language processing, computer vision, and time series data.
Provides a comprehensive overview of feature engineering for data analytics, including feature extraction, transformation, and selection. It also discusses feature engineering best practices and provides case studies in various domains like marketing, finance, and healthcare.
Save
Provides a comprehensive overview of data mining techniques, including feature extraction, transformation, and selection. It also covers data mining algorithms and their applications in various domains like marketing, finance, and healthcare.
Save
Provides a comprehensive overview of deep learning concepts and algorithms, including feature extraction and transformation techniques. It also covers deep learning architectures and their applications in various domains like computer vision, natural language processing, and speech recognition.
Provides a comprehensive overview of pattern recognition and machine learning concepts and algorithms, including feature extraction and selection techniques. It also covers statistical learning theory and provides case studies in various domains like computer vision, natural language processing, and speech recognition.
Provides a practical introduction to machine learning for non-experts, including feature engineering techniques like feature extraction and selection. It also covers machine learning algorithms and their applications in various domains like web development, social media, and finance.
Provides a comprehensive introduction to machine learning using Python, including feature engineering techniques like feature extraction, transformation, and selection. It also covers machine learning algorithms and their applications in various domains like natural language processing, computer vision, and speech recognition.
Provides a comprehensive introduction to machine learning using R, including feature engineering techniques like feature extraction, transformation, and selection. It also covers machine learning algorithms and their applications in various domains like marketing, finance, and healthcare.
Provides a practical introduction to machine learning using Python, including feature engineering techniques like feature extraction and selection. It also covers machine learning algorithms and their applications in various domains like natural language processing, computer vision, and speech recognition.
Provides a comprehensive overview of statistical learning concepts and algorithms, including feature extraction, transformation, and selection techniques. It also covers statistical learning theory and provides case studies in various domains like marketing, finance, and healthcare.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/yv72fn/feature
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Time
87 hours
Level
Intermediate
Via
Pluralsight
Instructor
Xavier Morera
Language
English
Good to know
Covers data preparation, which is essential for machine learning models
Introduces Microsoft's Azure ML Studio classic for practical application
Teaches in-demand skills for data scientists, who spend a significant amount of time preparing data
Targeted at learners with a solid understanding of machine learning concepts
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