Preparing Data for Feature Engineering and Machine Learning from Pluralsight

This course covers categories of feature engineering techniques used to get the best results from a machine learning model, including feature selection, and several feature extraction techniques to re-express features in the most appropriate form.

However well designed and well implemented a machine learning model is, if the data fed in is poorly engineered, the model’s predictions will be disappointing.

In this course, Preparing Data for Feature Engineering and Machine Learning, you will gain the ability to appropriately pre-process your data -- in effect engineer it -- so that you can get the best out of your ML models.

First, you will learn how feature selection techniques can be used to find predictors that contain the most information. Feature selection can be broadly grouped into three categories known as filter, wrapper, and embedded techniques and we will understand and implement all of these.

Next, you will discover how feature extraction differs from feature selection, in that data is substantially re-expressed, sometimes in forms that are hard to interpret. You will then understand techniques for feature extraction from image and text data.

Finally, you will round out your knowledge by understanding how to leverage powerful Python libraries for working with images, text, dates, and geo-spatial data.

When you’re finished with this course, you will have the skills and knowledge to identify the correct feature engineering techniques, and the appropriate solutions for your use-case.

What's inside

Syllabus

Course Overview

Understanding the Role of Features in Machine Learning

Preparing Data for Machine Learning

Understanding and Implementing Feature Selection

Exploring Feature Extraction Techniques

Implementing Feature Extraction

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores feature engineering techniques used in industry

Taught by Janani Ravi, a recognized expert in the field

Covers categories of feature engineering techniques for best results from a machine learning model

Develops feature selection and extraction techniques to re-express features appropriately

Uses Python libraries for working with images, text, dates, and geo-spatial data

Requires students to have some background knowledge in machine learning

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 with these activities:

Review feature selection techniques

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Ensure that you have a strong grasp of different feature selection techniques to be able to effectively apply them in the course.

Browse courses on Feature Selection

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Read textbook chapters or online articles on feature selection.
Review notes from previous courses or self-study on feature selection.
Complete practice problems or exercises on feature selection.

Follow tutorials on feature extraction techniques

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Gain practical experience with feature extraction techniques to enhance your understanding and application skills.

Browse courses on Feature Extraction

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Identify reputable online platforms or resources offering tutorials on feature extraction.
Select tutorials that cover the specific techniques you need to learn.
Follow the tutorials step-by-step, implementing the techniques in a coding environment.
Troubleshoot any issues encountered during the implementation.

Participate in peer discussion groups on feature engineering

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Engage with peers to exchange knowledge, clarify doubts, and deepen your understanding of feature engineering concepts.

Browse courses on Feature Engineering

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Join online forums or discussion groups dedicated to feature engineering.
Actively participate in discussions, sharing your insights and asking questions.
Review contributions from others and engage in constructive dialogue.

Four other activities

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Perform practice drills on feature engineering

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Solidify your understanding and improve your proficiency in feature engineering through repetitive exercises.

Browse courses on Feature Engineering

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Find online platforms or resources that provide practice drills on feature engineering.
Select drills that cover a range of feature engineering techniques.
Complete the drills, paying attention to the underlying concepts and methodologies.
Review your solutions and identify areas for improvement.

Create a summary of key feature engineering concepts

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Reinforce your understanding by creating a concise and structured summary of the key concepts covered in the course.

Browse courses on Feature Engineering

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Review your notes and identify the most important feature engineering concepts.
Organize the concepts into a logical structure.
Write a clear and concise summary that captures the essence of each concept.
Proofread your summary and make any necessary revisions.

Contribute to open-source projects related to feature engineering

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Gain practical experience and enhance your skills by contributing to real-world feature engineering projects.

Browse courses on Feature Engineering

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Identify open-source projects that utilize feature engineering techniques.
Review the project documentation and codebase.
Identify areas where you can contribute, such as implementing new feature engineering methods or improving existing ones.
Submit your contributions to the project and engage with the community.

Mentor other students in feature engineering

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Solidify your understanding by explaining concepts and providing guidance to others, while also developing your leadership and communication skills.

Browse courses on Feature Engineering

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Identify opportunities to mentor other students, such as through online forums or study groups.
Prepare materials and resources to support your mentees.
Establish regular communication channels with your mentees.
Provide tailored guidance and support to meet the individual needs of your mentees.

Career center

Learners who complete Preparing Data for Feature Engineering and Machine Learning will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use their knowledge of feature engineering techniques to prepare and engineer data for use in machine learning models. They also use their knowledge of statistics and machine learning to develop and evaluate models. This course provides a comprehensive overview of feature engineering techniques, including feature selection, feature extraction, and data pre-processing. It also covers the use of Python libraries for working with images, text, dates, and geo-spatial data. By completing this course, you will gain the skills and knowledge necessary to become a successful Data Scientist.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers are responsible for preparing and engineering data for use in machine learning models. They use their knowledge of feature engineering techniques to select and extract the most relevant features from data, which can then be used to build more accurate and effective models. This course provides a comprehensive overview of feature engineering techniques, including feature selection, feature extraction, and data pre-processing. It also covers the use of Python libraries for working with images, text, dates, and geo-spatial data. By completing this course, you will gain the skills and knowledge necessary to become a successful Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts use their knowledge of feature engineering techniques to prepare and engineer data for use in data analysis. They also use their knowledge of statistics and data analysis to develop and evaluate data analysis models. This course provides a comprehensive overview of feature engineering techniques, including feature selection, feature extraction, and data pre-processing. It also covers the use of Python libraries for working with images, text, dates, and geo-spatial data. By completing this course, you will gain the skills and knowledge necessary to become a successful Data Analyst.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

Quantitative Analysts use their knowledge of feature engineering techniques to prepare and engineer data for use in financial models. They also use their knowledge of statistics and financial modeling to develop and evaluate financial models. This course provides a comprehensive overview of feature engineering techniques, including feature selection, feature extraction, and data pre-processing. It also covers the use of Python libraries for working with images, text, dates, and geo-spatial data. By completing this course, you will gain the skills and knowledge necessary to become a successful Quantitative Analyst.

See salaries and explore the career path for Quantitative Analyst

Machine Learning Research Scientist

Machine Learning Research Scientists may use feature engineering techniques to improve the performance of their machine learning models. This course provides a comprehensive overview of feature engineering techniques, including feature selection, feature extraction, and data pre-processing. It also covers the use of Python libraries for working with images, text, dates, and geo-spatial data. By completing this course, you will gain the skills and knowledge necessary to become a successful Machine Learning Research Scientist.

See salaries and explore the career path for Machine Learning Research Scientist

Statistician

Statisticians may use feature engineering techniques to improve the performance of their statistical models. This course provides a comprehensive overview of feature engineering techniques, including feature selection, feature extraction, and data pre-processing. It also covers the use of Python libraries for working with images, text, dates, and geo-spatial data. By completing this course, you will gain the skills and knowledge necessary to become a successful Statistician.

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Data Visualization Engineer

Data Visualization Engineers may use feature engineering techniques to improve the performance of their data visualizations. This course provides a comprehensive overview of feature engineering techniques, including feature selection, feature extraction, and data pre-processing. It also covers the use of Python libraries for working with images, text, dates, and geo-spatial data. By completing this course, you will gain the skills and knowledge necessary to become a successful Data Visualization Engineer.

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Database Administrator

Database Administrators may use feature engineering techniques to improve the performance of their databases. This course provides a comprehensive overview of feature engineering techniques, including feature selection, feature extraction, and data pre-processing. It also covers the use of Python libraries for working with images, text, dates, and geo-spatial data. By completing this course, you will gain the skills and knowledge necessary to become a successful Database Administrator.

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Marketing Analyst

Marketing Analysts may use feature engineering techniques to improve the performance of their marketing campaigns. This course provides a comprehensive overview of feature engineering techniques, including feature selection, feature extraction, and data pre-processing. It also covers the use of Python libraries for working with images, text, dates, and geo-spatial data. By completing this course, you will gain the skills and knowledge necessary to become a successful Marketing Analyst.

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Product Manager

Product Managers may use feature engineering techniques to improve the performance of their products. This course provides a comprehensive overview of feature engineering techniques, including feature selection, feature extraction, and data pre-processing. It also covers the use of Python libraries for working with images, text, dates, and geo-spatial data. By completing this course, you will gain the skills and knowledge necessary to become a successful Product Manager.

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Data Engineer

Data Engineers may use feature engineering techniques to improve the performance of their data pipelines. This course provides a comprehensive overview of feature engineering techniques, including feature selection, feature extraction, and data pre-processing. It also covers the use of Python libraries for working with images, text, dates, and geo-spatial data. By completing this course, you will gain the skills and knowledge necessary to become a successful Data Engineer.

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Cloud Architect

Cloud Architects may use feature engineering techniques to improve the performance of their cloud-based applications. This course provides a comprehensive overview of feature engineering techniques, including feature selection, feature extraction, and data pre-processing. It also covers the use of Python libraries for working with images, text, dates, and geo-spatial data. By completing this course, you will gain the skills and knowledge necessary to become a successful Cloud Architect.

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Software Engineer

Software Engineers may use feature engineering techniques to improve the performance of their software applications. This course provides a comprehensive overview of feature engineering techniques, including feature selection, feature extraction, and data pre-processing. It also covers the use of Python libraries for working with images, text, dates, and geo-spatial data. By completing this course, you will gain the skills and knowledge necessary to become a successful Software Engineer.

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Operations Research Analyst

Operations Research Analysts may use feature engineering techniques to improve the performance of their operations. This course provides a comprehensive overview of feature engineering techniques, including feature selection, feature extraction, and data pre-processing. It also covers the use of Python libraries for working with images, text, dates, and geo-spatial data. By completing this course, you will gain the skills and knowledge necessary to become a successful Operations Research Analyst.

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Business Analyst

Business Analysts may use feature engineering techniques to improve the performance of their business processes. This course provides a comprehensive overview of feature engineering techniques, including feature selection, feature extraction, and data pre-processing. It also covers the use of Python libraries for working with images, text, dates, and geo-spatial data. By completing this course, you will gain the skills and knowledge necessary to become a successful Business Analyst.

See salaries and explore the career path for Business Analyst