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Ravikiran Srinivasulu
In this course, you'll learn how to prepare, clean up, and engineer new features from the data with Azure Machine Learning, so the dataset can be represented in a form that's easy for the learning algorithm to learn the patterns.
Read more
In this course, you'll learn how to prepare, clean up, and engineer new features from the data with Azure Machine Learning, so the dataset can be represented in a form that's easy for the learning algorithm to learn the patterns.
Read more
In this course, you'll learn how to prepare, clean up, and engineer new features from the data with Azure Machine Learning, so the dataset can be represented in a form that's easy for the learning algorithm to learn the patterns.
Data comes from many different sources. So when you join them, they are naturally inconsistent. In this course, Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure, you will be taken on a journey where you begin with data that's unsuitable for machine learning and use different modules in Azure Machine Learning to clean and preprocess the data. First, you will learn how to set up the data and workspace in Azure Machine Learning. Next, you will discover the role of feature engineering in machine learning. Finally, you will explore how to Identify specific data-level issues for machine learning models. When you’re finished with this course, you will have a clean dataset processed with azure machine learning modules that’s ready to build production-ready machine learning models.
Register for this course and see more details by visiting:
OpenCourser.com/course/1bw162/preparing
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What's inside
Syllabus
Course Overview
Getting Started with Azure Machine Learning
Differentiating Data, Features, Targets, and Models
Preparing Input Data for Machine Learning Models
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Syllabus
Course Overview
Getting Started with Azure Machine Learning
Differentiating Data, Features, Targets, and Models
Preparing Input Data for Machine Learning Models
Read more
Handling Missing Data
Role of Feature Engineering in Machine Learning
Split a Data Set into Training and Testing Subsets
Identify Data-level Issues In Machine Learning Models
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches students about the role of feature engineering in machine learning, which is recommended by many resources for machine learning tasks
Taught by Ravikiran Srinivasulu, who has a strong reputation for working in machine learning
Provides hands-on experience through Microsoft Azure
Offered through a multi-modal platform that includes videos and readings
Requires learners to bring in prior knowledge, which may not be suitable for absolute beginners
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Save Preparing Data for Feature Engineering and Machine Learning 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 Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure with these
activities:
Seek guidance from experienced practitioners in feature engineering
Show steps
Accelerate learning by connecting with experienced professionals in the field.
Browse courses on
Mentorship
Show steps
-
Identify experienced practitioners in feature engineering.
-
Reach out to potential mentors and request guidance.
-
Regularly engage with mentors to seek advice and insights.
Review concepts of dimensionality reduction
Show steps
Improve preparation for course content by reinforcing fundamental concepts of dimensionality reduction.
Browse courses on
Dimensionality Reduction
Show steps
-
Review notes or articles on dimensionality reduction techniques.
-
Consider examples of dimensionality reduction in real-world applications.
Discuss feature engineering challenges and solutions with peers
Show steps
Enhance understanding by sharing and discussing challenges and solutions related to feature engineering with peers.
Show steps
-
Join or form a study group with peers taking the course.
-
Regularly discuss feature engineering challenges encountered during the course.
-
Share and evaluate different approaches to solving these challenges.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice feature engineering on a simple dataset
Show steps
Gain hands-on experience in applying feature engineering techniques to improve model performance.
Browse courses on
Feature Engineering
Show steps
-
Select a small and easy-to-understand dataset.
-
Apply different feature engineering techniques to the dataset.
-
Evaluate the impact of each feature engineering technique on model performance.
Attend a workshop on feature engineering best practices
Show steps
Gain practical insights and learn from experts in the field of feature engineering.
Show steps
-
Research and identify workshops on feature engineering best practices.
-
Attend the workshop and actively participate in discussions.
-
Apply the learned best practices in future feature engineering tasks.
Develop a plan for feature engineering a specific dataset
Show steps
Deepen understanding of feature engineering by creating a plan for a specific dataset.
Browse courses on
Data Engineering
Show steps
-
Select a dataset and define the target variable.
-
Analyze the dataset to identify potential features.
-
Develop a plan to apply feature engineering techniques to the dataset.
-
Evaluate the plan and make necessary adjustments.
Follow tutorials on advanced feature engineering techniques
Show steps
Expand knowledge of feature engineering by exploring advanced techniques through tutorials.
Browse courses on
Machine Learning Algorithms
Show steps
-
Identify advanced feature engineering techniques that align with course content.
-
Find reputable tutorials or online courses covering these advanced techniques.
-
Follow the tutorials and apply the techniques to practice problems.
Share knowledge by mentoring junior learners in feature engineering
Show steps
Solidify understanding by explaining concepts and providing guidance to others.
Browse courses on
Mentoring
Show steps
-
Identify opportunities to mentor junior learners interested in feature engineering.
-
Share knowledge and provide guidance on feature engineering concepts and techniques.
-
Receive feedback and refine understanding through the mentoring process.
Seek guidance from experienced practitioners in feature engineering
Show steps
Accelerate learning by connecting with experienced professionals in the field.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- Identify experienced practitioners in feature engineering.
- Reach out to potential mentors and request guidance.
- Regularly engage with mentors to seek advice and insights.
Review concepts of dimensionality reduction
Show steps
Improve preparation for course content by reinforcing fundamental concepts of dimensionality reduction.
Browse courses on
Dimensionality Reduction
Show steps
Show steps
Show steps
- Review notes or articles on dimensionality reduction techniques.
- Consider examples of dimensionality reduction in real-world applications.
Discuss feature engineering challenges and solutions with peers
Show steps
Enhance understanding by sharing and discussing challenges and solutions related to feature engineering with peers.
Show steps
Show steps
Show steps
- Join or form a study group with peers taking the course.
- Regularly discuss feature engineering challenges encountered during the course.
- Share and evaluate different approaches to solving these challenges.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice feature engineering on a simple dataset
Show steps
Gain hands-on experience in applying feature engineering techniques to improve model performance.
Browse courses on
Feature Engineering
Show steps
Show steps
Show steps
- Select a small and easy-to-understand dataset.
- Apply different feature engineering techniques to the dataset.
- Evaluate the impact of each feature engineering technique on model performance.
Attend a workshop on feature engineering best practices
Show steps
Gain practical insights and learn from experts in the field of feature engineering.
Show steps
Show steps
Show steps
- Research and identify workshops on feature engineering best practices.
- Attend the workshop and actively participate in discussions.
- Apply the learned best practices in future feature engineering tasks.
Develop a plan for feature engineering a specific dataset
Show steps
Deepen understanding of feature engineering by creating a plan for a specific dataset.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Select a dataset and define the target variable.
- Analyze the dataset to identify potential features.
- Develop a plan to apply feature engineering techniques to the dataset.
- Evaluate the plan and make necessary adjustments.
Follow tutorials on advanced feature engineering techniques
Show steps
Expand knowledge of feature engineering by exploring advanced techniques through tutorials.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Identify advanced feature engineering techniques that align with course content.
- Find reputable tutorials or online courses covering these advanced techniques.
- Follow the tutorials and apply the techniques to practice problems.
Share knowledge by mentoring junior learners in feature engineering
Show steps
Solidify understanding by explaining concepts and providing guidance to others.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Identify opportunities to mentor junior learners interested in feature engineering.
- Share knowledge and provide guidance on feature engineering concepts and techniques.
- Receive feedback and refine understanding through the mentoring process.
Career center
Learners who complete Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for implementing machine learning algorithms to solve a variety of complex problems in diverse industries. Engineers use data from a variety of sources to train and test models, and they design and implement data pipelines to ensure that the data is ready for use in machine learning models. This course can help Machine Learning Engineers succeed by providing hands-on experience with Azure Machine Learning, one of the leading platforms for machine learning development. Engineers will learn how to prepare data for machine learning models, handle missing data, and identify data-level issues. This experience will help Machine Learning Engineers develop the skills they need to build and implement successful machine learning solutions.
Data Engineer
Data Engineer
Data Engineers are responsible for designing, building, and maintaining the data infrastructure that supports data-driven applications and initiatives. Data Engineers work with data from a variety of sources, and they use a variety of tools and technologies to transform, clean, and prepare data for analysis and machine learning. This course can help Data Engineers succeed by providing hands-on experience with Azure Machine Learning, one of the leading platforms for machine learning development. Data Engineers will learn how to prepare data for machine learning models, handle missing data, and identify data-level issues. This experience will help Data Engineers develop the skills they need to build and maintain data infrastructure that supports successful machine learning solutions.
Data Scientist
Data Scientist
Data Scientists are responsible for using data to solve complex problems and improve decision-making. Data Scientists work with data from a variety of sources, and they use a variety of statistical and machine learning techniques to analyze data and build predictive models. This course can help Data Scientists succeed by providing hands-on experience with Azure Machine Learning, one of the leading platforms for machine learning development. Data Scientists will learn how to prepare data for machine learning models, handle missing data, and identify data-level issues. This experience will help Data Scientists develop the skills they need to build and implement successful machine learning models.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts are responsible for collecting, analyzing, and interpreting data to provide insights that can help businesses make better decisions. Business Intelligence Analysts work with data from a variety of sources, and they use a variety of tools and technologies to transform, clean, and prepare data for analysis and reporting. This course can help Business Intelligence Analysts succeed by providing hands-on experience with Azure Machine Learning, one of the leading platforms for machine learning development. Business Intelligence Analysts will learn how to prepare data for machine learning models, handle missing data, and identify data-level issues. This experience will help Business Intelligence Analysts develop the skills they need to build and implement successful business intelligence solutions.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. Software Engineers work with a variety of programming languages and technologies, and they use a variety of tools and techniques to build and test software applications. This course can help Software Engineers succeed by providing hands-on experience with Azure Machine Learning, one of the leading platforms for machine learning development. Software Engineers will learn how to prepare data for machine learning models, handle missing data, and identify data-level issues. This experience will help Software Engineers develop the skills they need to build and implement successful software applications that incorporate machine learning.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to provide insights that can help businesses make better decisions. Data Analysts work with data from a variety of sources, and they use a variety of statistical and machine learning techniques to analyze data and build predictive models. This course can help Data Analysts succeed by providing hands-on experience with Azure Machine Learning, one of the leading platforms for machine learning development. Data Analysts will learn how to prepare data for machine learning models, handle missing data, and identify data-level issues. This experience will help Data Analysts develop the skills they need to build and implement successful data analysis solutions.
Database Administrator
Database Administrator
Database Administrators are responsible for designing, implementing, and maintaining database systems. Database Administrators work with a variety of database technologies, and they use a variety of tools and techniques to manage and optimize database systems. This course can help Database Administrators succeed by providing hands-on experience with Azure Machine Learning, one of the leading platforms for machine learning development. Database Administrators will learn how to prepare data for machine learning models, handle missing data, and identify data-level issues. This experience will help Database Administrators develop the skills they need to build and maintain database systems that support successful machine learning solutions.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data to provide insights that can help businesses make better decisions. Statisticians work with data from a variety of sources, and they use a variety of statistical techniques to analyze data and build predictive models. This course can help Statisticians succeed by providing hands-on experience with Azure Machine Learning, one of the leading platforms for machine learning development. Statisticians will learn how to prepare data for machine learning models, handle missing data, and identify data-level issues. This experience will help Statisticians develop the skills they need to build and implement successful statistical models.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts are responsible for using mathematical and analytical techniques to solve complex problems in a variety of industries. Operations Research Analysts work with data from a variety of sources, and they use a variety of optimization techniques to develop solutions to complex problems. This course can help Operations Research Analysts succeed by providing hands-on experience with Azure Machine Learning, one of the leading platforms for machine learning development. Operations Research Analysts will learn how to prepare data for machine learning models, handle missing data, and identify data-level issues. This experience will help Operations Research Analysts develop the skills they need to build and implement successful operations research solutions.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts are responsible for using mathematical and statistical techniques to analyze financial data and make investment decisions. Quantitative Analysts work with data from a variety of sources, and they use a variety of statistical and machine learning techniques to analyze data and build predictive models. This course can help Quantitative Analysts succeed by providing hands-on experience with Azure Machine Learning, one of the leading platforms for machine learning development. Quantitative Analysts will learn how to prepare data for machine learning models, handle missing data, and identify data-level issues. This experience will help Quantitative Analysts develop the skills they need to build and implement successful quantitative analysis models.
Actuary
Actuary
Actuaries are responsible for using mathematical and statistical techniques to assess risk and uncertainty. Actuaries work with data from a variety of sources, and they use a variety of statistical and machine learning techniques to analyze data and build predictive models. This course can help Actuaries succeed by providing hands-on experience with Azure Machine Learning, one of the leading platforms for machine learning development. Actuaries will learn how to prepare data for machine learning models, handle missing data, and identify data-level issues. This experience will help Actuaries develop the skills they need to build and implement successful actuarial models.
Financial Analyst
Financial Analyst
Financial Analysts are responsible for analyzing financial data and making investment recommendations. Financial Analysts work with data from a variety of sources, and they use a variety of statistical and machine learning techniques to analyze data and build predictive models. This course can help Financial Analysts succeed by providing hands-on experience with Azure Machine Learning, one of the leading platforms for machine learning development. Financial Analysts will learn how to prepare data for machine learning models, handle missing data, and identify data-level issues. This experience will help Financial Analysts develop the skills they need to build and implement successful financial analysis models.
Risk Analyst
Risk Analyst
Risk Analysts are responsible for identifying and assessing risks to businesses and organizations. Risk Analysts work with data from a variety of sources, and they use a variety of statistical and machine learning techniques to analyze data and build predictive models. This course can help Risk Analysts succeed by providing hands-on experience with Azure Machine Learning, one of the leading platforms for machine learning development. Risk Analysts will learn how to prepare data for machine learning models, handle missing data, and identify data-level issues. This experience will help Risk Analysts develop the skills they need to build and implement successful risk analysis models.
For more career information including salaries, visit:
OpenCourser.com/course/1bw162/preparing
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
Preparing Data for Feature Engineering and Machine Learning in Microsoft Azure.
Focuses specifically on feature engineering techniques and best practices. Explores feature selection, transformation, and creation, with a focus on real-world examples and case studies.
Provides a comprehensive guide to feature engineering, covering the principles and techniques used by data scientists.
Save
Serves as a foundational text for machine learning, covering a wide range of topics, including feature engineering, model selection, and evaluation. Offers a comprehensive overview of the theoretical foundations and practical applications of machine learning.
Serves as a classic reference for statistical learning methods, providing a comprehensive overview of statistical models and algorithms. While not directly related to Azure, it offers a strong theoretical foundation for the course content.
Serves as a resource for intermediate Python developers working in machine learning. Covers recipe-based approaches to common tasks, from preparing data for feature engineering to optimizing model performance.
Provides a comprehensive overview of machine learning in Python, encompassing practical examples and theoretical foundations.
Provides a comprehensive overview of machine learning in R, covering a wide range of topics, including data preparation, feature engineering, and model evaluation. While not specific to Azure, it offers a solid foundation for learners who prefer to use R for machine learning.
Save
Provides a comprehensive overview of data mining techniques, including feature engineering, clustering, and classification. While not specific to Azure, it offers a valuable reference for learners seeking a deeper understanding of the underlying principles.
Provides a foundational understanding of data science principles and techniques, including data cleaning, feature engineering, and model building. While not specifically focused on Azure, it offers a solid theoretical basis for the course content.
Serves as a gentle introduction to machine learning concepts and algorithms. Useful for learners who are new to the field or seeking a refresher on the fundamentals.
Introduces data science by discussing it from a business perspective, it provides readers with enough technical vocabulary to hold conversations with data scientists.
Save
Comprehensive textbook on data mining, covering a wide range of topics including data preparation, feature selection, and model evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1bw162/preparing
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Time
139 hours
Level
Introductory
Via
Pluralsight
Instructor
Ravikiran Srinivasulu
Language
English
Good to know
Teaches students about the role of feature engineering in machine learning, which is recommended by many resources for machine learning tasks
Taught by Ravikiran Srinivasulu, who has a strong reputation for working in machine learning
Provides hands-on experience through Microsoft Azure
Offered through a multi-modal platform that includes videos and readings
Requires learners to bring in prior knowledge, which may not be suitable for absolute beginners
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