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Steph Locke
In machine learning, feature sets can quickly become complicated and unwieldy. This course will give you the skills needed to reduce the complexity of your feature sets to help ensure you get better and more consistent insights into your data.
Read more
In machine learning, feature sets can quickly become complicated and unwieldy. This course will give you the skills needed to reduce the complexity of your feature sets to help ensure you get better and more consistent insights into your data.
Read more
In machine learning, feature sets can quickly become complicated and unwieldy. This course will give you the skills needed to reduce the complexity of your feature sets to help ensure you get better and more consistent insights into your data.
If you're building models for data science, your feature sets can quickly become complicated and hard to understand. In this course, Reducing Complexity in Data in Microsoft Azure, you will learn how to reduce the complexity of feature sets, making models more understandable, more straightforward to build, and more robust. First, you will learn to understand feature set complexity and how it impacts your models. Next, you will discover a range of different techniques to improve the complexity of your feature sets. Finally, you will explore various advanced methods for feature set complexity reduction. When you are finished with this course, you will have the skills and knowledge needed to reduce the complexity of your models, and create more straightforward and manageable models, leading to better and more consistent insights into your data.
This course is no longer available. Find something similar by browsing:
Feature Reduction
Principal Component Analysis
Numeric Feature Sets
Categorical Feature Sets
Text Feature Sets
Model Complexity
Register for this course and see more details by visiting:
OpenCourser.com/course/kd61s9/reducing
What's inside
Syllabus
Course Overview
Understanding How Feature Set Complexity Impacts Model Quality
Applying Criteria-based Feature Reduction Techniques
Using Principal Component Analysis to Reduce Numeric Feature Sets
Read more
Syllabus
Course Overview
Understanding How Feature Set Complexity Impacts Model Quality
Applying Criteria-based Feature Reduction Techniques
Using Principal Component Analysis to Reduce Numeric Feature Sets
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Teaches advanced feature reduction techniques in machine learning, which are beneficial for model clarity, efficiency, and performance
Relevant for data scientists and machine learning practitioners seeking to improve the quality and interpretability of their models
Features instructors with expertise in machine learning
Provides a comprehensive understanding of feature set complexity and its impact on model quality
Covers industry-standard techniques such as Principal Component Analysis and criteria-based feature reduction
Requires proficiency in machine learning fundamentals
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Reviews summary
Feature reduction for azure data
According to students, this course provides a solid introduction to reducing complexity in data in Microsoft Azure, particularly emphasizing feature reduction techniques like Principal Component Analysis (PCA). Learners frequently praise the instructor's clear explanations and the practical, hands-on labs and demos that help solidify understanding. While many find it a valuable starting point for applying these methods, some more experienced learners indicate the course is too introductory and could benefit from deeper dives into advanced methods beyond PCA or more extensive coverage of Azure-specific tooling for very large datasets. Overall, it's highly recommended for data professionals seeking to build a strong foundation in this critical area within the Azure ecosystem.
Explores feature reduction within Azure, though some want more.
"This course was a solid introduction to feature reduction techniques... within the Azure environment."
"The focus on Azure was perfect for my needs."
"The Azure-specific parts were useful but not extensively covered."
"It felt more like a general feature reduction course with Azure as a backdrop rather than a deep dive into Azure-specific tooling."
Principal Component Analysis is thoroughly explained and applied.
"This course demystified PCA..."
"It provides a foundational understanding of data complexity and practical methods like PCA."
"While it covers PCA well, I agree with some others that the 'beyond PCA' section could be expanded."
Hands-on activities enhance understanding and real-world use.
"I found the practical demos very helpful for understanding how to apply these methods."
"The labs were well-structured and directly applicable to my work."
"It taught me practical ways to reduce features in my ML models, which is directly impacting my model performance."
"The hands-on exercises help solidify understanding."
Concepts are presented clearly and accessibly.
"The instructor explained complex concepts clearly."
"This course demystified PCA and offered practical strategies for categorical and text data."
"I appreciated the clear explanations, especially for someone who isn't a math expert."
"The instructor made complex topics accessible."
Best for beginners/intermediate; advanced users may seek more.
"My only minor critique is that it could go a bit deeper into real-world scenarios beyond the basics."
"I felt the course was a bit too introductory. If you're already familiar with some ML concepts, you might find it a bit slow."
"I was hoping for more advanced Azure-specific strategies and deeper dives into optimization for very large datasets."
"Better for absolute beginners in feature engineering, not for intermediate/advanced users."
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 Reducing Complexity in Data in Microsoft Azure with these
activities:
Organize Course Materials
Show steps
Organizing course materials will help students stay organized and focused throughout the course, which will improve their understanding and retention of the concepts covered in feature reduction.
Show steps
-
Review and categorize course materials.
-
Create a system for storing and organizing the materials.
-
Review and update the materials regularly.
Read 'Feature Engineering for Machine Learning'
Show steps
This book provides a comprehensive overview of feature engineering techniques, which will help students understand the importance of feature selection and feature extraction in machine learning.
View
Feature Engineering for Machine Learning:...
on Amazon
Show steps
Participate in Peer Review Sessions
Show steps
Students will benefit from peer feedback and collaboration by participating in peer review sessions, which will help them identify areas for improvement and gain a deeper understanding of feature reduction techniques.
Show steps
-
Form study groups or connect with peers.
-
Share and review feature reduction projects or assignments.
-
Provide constructive feedback and engage in discussions.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Apply Feature Reduction Techniques
Show steps
Students will learn to apply feature selection and feature importance techniques to real-world datasets, which will help them improve the accuracy and efficiency of their machine learning models.
Browse courses on
Feature Selection
Show steps
-
Identify candidate features for reduction.
-
Apply feature selection techniques such as filter methods (e.g., correlation analysis, ANOVA) and wrapper methods (e.g., recursive feature elimination).
-
Evaluate the impact of feature reduction on model performance using metrics such as accuracy, precision, and recall.
Develop a Feature Extraction Pipeline
Show steps
Students will gain hands-on experience in designing and implementing a feature extraction pipeline, which will enable them to extract relevant features from raw data and improve the performance of their machine learning models.
Browse courses on
Feature Extraction
Show steps
-
Design a feature extraction pipeline that includes preprocessing, transformation, and feature selection steps.
-
Implement the pipeline using a programming language such as Python or R.
-
Evaluate the performance of the pipeline using real-world datasets.
Explore Advanced Feature Reduction Methods
Show steps
Students will gain exposure to advanced feature reduction methods such as principal component analysis (PCA) and t-SNE, which will enable them to handle complex datasets and improve the interpretability of their machine learning models.
Browse courses on
Dimensionality Reduction
Show steps
-
Understand the concepts and algorithms behind PCA and t-SNE.
-
Apply these methods to real-world datasets.
-
Evaluate the effectiveness of these methods in reducing feature complexity and improving model performance.
Build a Feature Reduction Tool
Show steps
Students will apply their knowledge of feature reduction to develop a software tool or application, which will enhance their problem-solving skills and deepen their understanding of real-world machine learning applications.
Browse courses on
Software Development
Show steps
-
Define the requirements and scope of the tool.
-
Design and implement the tool using appropriate programming languages and libraries.
-
Test and evaluate the performance of the tool.
Develop a Whitepaper on Feature Reduction
Show steps
Students will have the opportunity to synthesize their knowledge and communicate their understanding of feature reduction techniques by writing a technical whitepaper, which will enhance their communication and writing skills.
Browse courses on
Technical Writing
Show steps
-
Research and gather information on feature reduction techniques.
-
Organize and outline the content of the whitepaper.
-
Write and edit the whitepaper, ensuring clarity and accuracy.
Organize Course Materials
Show steps
Organizing course materials will help students stay organized and focused throughout the course, which will improve their understanding and retention of the concepts covered in feature reduction.
Show steps
Show steps
Show steps
- Review and categorize course materials.
- Create a system for storing and organizing the materials.
- Review and update the materials regularly.
Read 'Feature Engineering for Machine Learning'
Show steps
This book provides a comprehensive overview of feature engineering techniques, which will help students understand the importance of feature selection and feature extraction in machine learning.
View
Feature Engineering for Machine Learning:...
on Amazon
Show steps
Show steps
Show steps
Participate in Peer Review Sessions
Show steps
Students will benefit from peer feedback and collaboration by participating in peer review sessions, which will help them identify areas for improvement and gain a deeper understanding of feature reduction techniques.
Show steps
Show steps
Show steps
- Form study groups or connect with peers.
- Share and review feature reduction projects or assignments.
- Provide constructive feedback and engage in discussions.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Apply Feature Reduction Techniques
Show steps
Students will learn to apply feature selection and feature importance techniques to real-world datasets, which will help them improve the accuracy and efficiency of their machine learning models.
Browse courses on
Feature Selection
Show steps
Show steps
Show steps
- Identify candidate features for reduction.
- Apply feature selection techniques such as filter methods (e.g., correlation analysis, ANOVA) and wrapper methods (e.g., recursive feature elimination).
- Evaluate the impact of feature reduction on model performance using metrics such as accuracy, precision, and recall.
Develop a Feature Extraction Pipeline
Show steps
Students will gain hands-on experience in designing and implementing a feature extraction pipeline, which will enable them to extract relevant features from raw data and improve the performance of their machine learning models.
Browse courses on
Feature Extraction
Show steps
Show steps
Show steps
- Design a feature extraction pipeline that includes preprocessing, transformation, and feature selection steps.
- Implement the pipeline using a programming language such as Python or R.
- Evaluate the performance of the pipeline using real-world datasets.
Explore Advanced Feature Reduction Methods
Show steps
Students will gain exposure to advanced feature reduction methods such as principal component analysis (PCA) and t-SNE, which will enable them to handle complex datasets and improve the interpretability of their machine learning models.
Browse courses on
Dimensionality Reduction
Show steps
Show steps
Show steps
- Understand the concepts and algorithms behind PCA and t-SNE.
- Apply these methods to real-world datasets.
- Evaluate the effectiveness of these methods in reducing feature complexity and improving model performance.
Build a Feature Reduction Tool
Show steps
Students will apply their knowledge of feature reduction to develop a software tool or application, which will enhance their problem-solving skills and deepen their understanding of real-world machine learning applications.
Browse courses on
Software Development
Show steps
Show steps
Show steps
- Define the requirements and scope of the tool.
- Design and implement the tool using appropriate programming languages and libraries.
- Test and evaluate the performance of the tool.
Develop a Whitepaper on Feature Reduction
Show steps
Students will have the opportunity to synthesize their knowledge and communicate their understanding of feature reduction techniques by writing a technical whitepaper, which will enhance their communication and writing skills.
Browse courses on
Technical Writing
Show steps
Show steps
Show steps
- Research and gather information on feature reduction techniques.
- Organize and outline the content of the whitepaper.
- Write and edit the whitepaper, ensuring clarity and accuracy.
Career center
Learners who complete Reducing Complexity in Data in Microsoft Azure will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics and machine learning to build models that can predict future events. Reducing Complexity in Data in Microsoft Azure can help you reduce the complexity of your feature sets, making your models more understandable, more straightforward to build, and more robust. This course can be especially helpful for Data Scientists who want to build more accurate and reliable models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and build machine learning models. They work with Data Scientists to identify the right data to use, and they develop algorithms that can learn from that data. Reducing Complexity in Data in Microsoft Azure can help Machine Learning Engineers reduce the complexity of their models, making them more efficient and easier to maintain. This course can be especially helpful for Machine Learning Engineers who want to build models that can scale to large datasets.
Data Analyst
Data Analyst
Data Analysts use data to identify trends and patterns. They work with Data Scientists and Machine Learning Engineers to build models that can predict future events. Reducing Complexity in Data in Microsoft Azure can help Data Analysts reduce the complexity of their feature sets, making their models more understandable, more straightforward to build, and more robust. This course can be especially helpful for Data Analysts who want to build models that can be easily communicated to stakeholders.
Business Analyst
Business Analyst
Business Analysts work with businesses to identify their needs and develop solutions that meet those needs. They use data to understand the business and to make recommendations for improvement. Reducing Complexity in Data in Microsoft Azure can help Business Analysts reduce the complexity of their data sets, making them easier to understand and analyze. This course can be especially helpful for Business Analysts who want to develop more effective solutions for their clients.
Software Developer
Software Developer
Software Developers design, develop, and maintain software applications. They work with Data Scientists and Machine Learning Engineers to build models that can be used in software applications. Reducing Complexity in Data in Microsoft Azure can help Software Developers reduce the complexity of their models, making them more efficient and easier to maintain. This course can be especially helpful for Software Developers who want to build software applications that can handle large amounts of data.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. They work with Data Scientists and Machine Learning Engineers to ensure that data is stored and accessed efficiently. Reducing Complexity in Data in Microsoft Azure can help Database Administrators reduce the complexity of their databases, making them easier to manage and maintain. This course can be especially helpful for Database Administrators who want to manage databases that can handle large amounts of data.
Systems Analyst
Systems Analyst
Systems Analysts design, develop, and maintain computer systems. They work with Data Scientists and Machine Learning Engineers to build models that can be used in computer systems. Reducing Complexity in Data in Microsoft Azure can help Systems Analysts reduce the complexity of their models, making them more efficient and easier to maintain. This course can be especially helpful for Systems Analysts who want to build computer systems that can handle large amounts of data.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They work with Data Scientists and Machine Learning Engineers to build models that can predict future events. Reducing Complexity in Data in Microsoft Azure can help Statisticians reduce the complexity of their models, making them more understandable, more straightforward to build, and more robust. This course can be especially helpful for Statisticians who want to build models that can be easily communicated to stakeholders.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve problems in business and industry. They work with Data Scientists and Machine Learning Engineers to build models that can be used to improve decision-making. Reducing Complexity in Data in Microsoft Azure can help Operations Research Analysts reduce the complexity of their models, making them more efficient and easier to maintain. This course can be especially helpful for Operations Research Analysts who want to build models that can handle large amounts of data.
Financial Analyst
Financial Analyst
Financial Analysts use data to evaluate the financial performance of companies and make recommendations for investment. Reducing Complexity in Data in Microsoft Azure can help Financial Analysts reduce the complexity of their models, making them more understandable, more straightforward to build, and more robust. This course can be especially helpful for Financial Analysts who want to build models that can be easily communicated to stakeholders.
Market Researcher
Market Researcher
Market Researchers collect and analyze data about customers and markets. They work with Data Scientists and Machine Learning Engineers to build models that can be used to predict customer behavior. Reducing Complexity in Data in Microsoft Azure can help Market Researchers reduce the complexity of their models, making them more understandable, more straightforward to build, and more robust. This course can be especially helpful for Market Researchers who want to build models that can be easily communicated to stakeholders.
Data Management Analyst
Data Management Analyst
Data Management Analysts develop and implement strategies for managing data. They work with Data Scientists and Machine Learning Engineers to ensure that data is stored and accessed efficiently. Reducing Complexity in Data in Microsoft Azure can help Data Management Analysts reduce the complexity of their data management strategies, making them more efficient and easier to implement. This course can be especially helpful for Data Management Analysts who want to develop data management strategies that can handle large amounts of data.
Information Security Analyst
Information Security Analyst
Information Security Analysts protect computer systems and networks from unauthorized access. They work with Data Scientists and Machine Learning Engineers to build models that can be used to detect and prevent cyberattacks. Reducing Complexity in Data in Microsoft Azure can help Information Security Analysts reduce the complexity of their models, making them more efficient and easier to maintain. This course can be especially helpful for Information Security Analysts who want to build models that can handle large amounts of data.
Computer Network Architect
Computer Network Architect
Computer Network Architects design and build computer networks. They work with Data Scientists and Machine Learning Engineers to build models that can be used to optimize network performance. Reducing Complexity in Data in Microsoft Azure can help Computer Network Architects reduce the complexity of their models, making them more efficient and easier to maintain. This course can be especially helpful for Computer Network Architects who want to build models that can handle large amounts of data.
Database Developer
Database Developer
Database Developers design and develop databases. They work with Data Scientists and Machine Learning Engineers to build models that can be used to store and access data efficiently. Reducing Complexity in Data in Microsoft Azure can help Database Developers reduce the complexity of their models, making them more efficient and easier to maintain. This course can be especially helpful for Database Developers who want to build databases that can handle large amounts of data.
For more career information including salaries, visit:
OpenCourser.com/course/kd61s9/reducing
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
Reducing Complexity in Data in Microsoft Azure.
This comprehensive guide to feature engineering provides a solid theoretical foundation and practical techniques for reducing feature set complexity in machine learning models. It covers a wide range of techniques, including criteria-based feature reduction, principal component analysis, and more advanced methods for numeric feature sets.
This hands-on guide to feature engineering with Python provides a comprehensive overview of the topic, from data preparation and transformation to feature selection and dimensionality reduction. It includes practical examples and case studies to help learners apply the techniques covered in the course.
This classic book on statistical learning provides a comprehensive overview of the field, including a chapter on feature engineering. It covers the key concepts and techniques involved in feature engineering, making it a valuable resource for learners looking to deepen their understanding of the topic.
Provides a probabilistic perspective on machine learning, including a chapter on feature engineering. It covers the key concepts and techniques involved in feature engineering, making it a valuable resource for learners looking to deepen their understanding of the topic.
This comprehensive book on pattern recognition and machine learning provides a solid theoretical foundation for feature engineering. It covers the key concepts and techniques involved in feature engineering, making it a valuable resource for learners looking to deepen their understanding of the topic.
Save
Provides a comprehensive overview of data mining, including a chapter on feature engineering. It covers the key concepts and techniques involved in feature engineering, making it a useful reference for learners looking to gain a basic understanding of the topic.
Provides a practical introduction to data science, including a chapter on feature engineering. It covers the key concepts and techniques involved in feature engineering, making it a useful reference for learners looking to gain a basic understanding of the topic.
Provides a comprehensive overview of machine learning for predictive data analytics, including a chapter on feature engineering. It covers the key concepts and techniques involved in feature engineering, making it a useful reference for learners looking to gain a basic understanding of the topic.
Save
Provides a practical guide to predictive modeling, including a chapter on feature engineering. It covers the key concepts and techniques involved in feature engineering, making it a useful reference for learners looking to gain a basic understanding of the topic.
Provides a comprehensive overview of machine learning with R, including a chapter on feature engineering. It covers the key concepts and techniques involved in feature engineering, making it a useful reference for learners looking to gain a basic understanding of the topic.
Provides a practical guide to machine learning, including a chapter on feature engineering. It covers the key concepts and techniques involved in feature engineering, making it a useful reference for learners looking to gain a basic understanding of the topic.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/kd61s9/reducing
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Time
134 hours
Level
Advanced
Via
Pluralsight
Instructor
Steph Locke
Language
English
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