Building Features from Text Data from Pluralsight

This course covers aspects of extracting information from text documents and constructing classification models including feature vectorization, locality-sensitive hashing, stopword removal, lemmatization, and more from natural language processing.

From chatbots to machine-generated literature, some of the hottest applications of ML and AI these days are for data in textual form.

In this course, Building Features from Text Data, you will gain the ability to structure textual data in a manner ideal for use in ML models.

First, you will learn how to represent documents as feature vectors using one-hot encoding, frequency-based, and prediction-based techniques. You will see how to improve these representations based on the meaning, or semantics, of the document.

Next, you will discover how to leverage various language modeling features such as stopword removal, frequency filtering, stemming and lemmatization, and parts-of-speech tagging.

Finally, you will see how locality-sensitive hashing can be used to reduce the dimensionality of documents while still keeping similar documents close together.

You will round out the course by implementing a classification model on text documents using many of these modeling abstractions.

When you’re finished with this course, you will have the skills and knowledge to use documents and textual data in conceptually and practically sound ways and represent such data for use in machine learning models.

What's inside

Syllabus

Course Overview

Representing Text as Features for Machine Learning

Building Feature Vector Representations of Text

Simplifying Text Processing Using Natural Language Processing

Reducing Dimensions in Text Using Hashing

Applying Text Feature Extraction Techniques to Machine Learning

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops text data structuring for use in ML models, which is a core skill for data scientists and machine learning engineers

Explores dimensionality reduction in text data using locality-sensitive hashing, which is a valuable technique in natural language processing

Uses Python libraries like NumPy and Scikit-learn, which are industry-standard tools for data manipulation and machine learning

Taught by Janani Ravi, who is recognized for her expertise in text mining and natural language processing

Covers language modeling features like stopword removal and lemmatization, which are essential for improving text representation

Requires learners to have a basic understanding of Python and natural language processing, which may be a barrier for some

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 Text Data with these activities:

Review the basics of NLP

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Refreshing your knowledge of NLP will provide a stronger foundation for learning the advanced concepts covered in this course.

Browse courses on Natural Language Processing

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Review core NLP concepts such as tokenization, stemming, and lemmatization
Read through existing NLP tutorials and articles
Complete a few practice exercises on NLP basics

Engage in peer discussions on text feature extraction

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Participating in peer discussions allows you to exchange knowledge and gain diverse perspectives on feature extraction techniques, strengthening your understanding.

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Find a peer group or online forum related to text feature extraction
Participate in discussions, ask questions, and share your experiences
Collaborate on projects or assignments with your peers

Follow a tutorial on feature engineering for text

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Completing a tutorial on feature engineering will help you better understand how to transform text data into a format suitable for machine learning models.

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Find a tutorial that aligns with your learning objectives
Follow the tutorial step-by-step
Complete any exercises or practice problems provided in the tutorial

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Practice text feature extraction

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Engaging in practice drills will provide you with hands-on experience in extracting features from text, reinforcing the techniques learned in the course.

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Find a dataset with text data
Apply feature extraction techniques to the dataset
Evaluate the performance of your feature extraction methods

Attend a workshop on advanced feature engineering techniques

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Attending a workshop provides you with the opportunity to learn from experts in the field and gain practical knowledge of cutting-edge feature engineering techniques.

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Research and find a suitable workshop
Register and attend the workshop
Actively participate in discussions and exercises
Apply the techniques learned to your own projects

Read and summarize a book on natural language processing (NLP)

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Reading and summarizing a comprehensive book will deepen your understanding of NLP and provide additional insights into feature extraction techniques.

View Natural Language Processing with Python:... on Amazon

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Read the book thoroughly
Take notes on key concepts and techniques
Write a summary of the book, outlining the main NLP concepts and how they relate to feature extraction

Create a blog post or presentation on the topic

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Creating content requires you to synthesize and communicate the key concepts of text feature extraction, reinforcing your understanding and enabling you to teach others.

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Choose a specific aspect of text feature extraction to focus on
Research and gather information on the topic
Create a blog post or presentation that explains the concept clearly

Develop a text classification project

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Hands-on experience in building a project using text feature extraction is a highly effective way to reinforce your learning and develop practical skills.

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Define the problem statement and data requirements
Collect and pre-process text data
Apply text feature extraction techniques
Train and evaluate a text classification model

Career center

Learners who complete Building Features from Text Data will develop knowledge and skills that may be useful to these careers:

Software Engineer

Software Engineers design, develop, and maintain software systems. This course may be useful for Software Engineers as it covers how to represent text as features for machine learning, build feature vector representations of text, and reduce dimensions in text using hashing.

See salaries and explore the career path for Software Engineer

Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models. This course may be useful for Machine Learning Engineers as it covers how to represent text as features for machine learning, build feature vector representations of text, and reduce dimensions in text using hashing. Additionally, this course can help Machine Learning Engineers gain the skills and knowledge to use documents and textual data in conceptually and practically sound ways.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use data to solve business problems. This course may be useful for Data Scientists as it covers how to represent text as features for machine learning, build feature vector representations of text, and reduce dimensions in text using hashing. Additionally, this course can help Data Scientists gain the skills and knowledge to use documents and textual data in conceptually and practically sound ways.

See salaries and explore the career path for Data Scientist

Natural Language Processing Engineer

Natural Language Processing Engineers develop and improve systems that allow computers to understand and generate human language. This course may be useful for Natural Language Processing Engineers as it covers how to represent text as features for machine learning, build feature vector representations of text, and reduce dimensions in text using hashing.

See salaries and explore the career path for Natural Language Processing Engineer

Product Manager

Product Managers develop and manage products. This course may be useful for Product Managers as it covers how to represent text as features for machine learning, build feature vector representations of text, and reduce dimensions in text using hashing.

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Data Architect

Data Architects design and manage data systems. This course may be useful for Data Architects as it covers how to represent text as features for machine learning, build feature vector representations of text, and reduce dimensions in text using hashing.

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Quantitative Analyst

Quantitative Analysts use data to make investment decisions. This course may be useful for Quantitative Analysts as it covers how to represent text as features for machine learning, build feature vector representations of text, and reduce dimensions in text using hashing.

See salaries and explore the career path for Quantitative Analyst

Business Analyst

Business Analysts use data to understand business needs and develop solutions. This course may be useful for Business Analysts as it covers how to represent text as features for machine learning, build feature vector representations of text, and reduce dimensions in text using hashing.

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Statistician

Statisticians use data to solve problems. This course may be useful for Statisticians as it covers how to represent text as features for machine learning, build feature vector representations of text, and reduce dimensions in text using hashing.

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Information Security Analyst

Information Security Analysts protect computer systems and networks from unauthorized access. This course may be useful for Information Security Analysts as it covers how to represent text as features for machine learning, build feature vector representations of text, and reduce dimensions in text using hashing.

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Database Administrator

Database Administrators manage and maintain databases. This course may be useful for Database Administrators as it covers how to represent text as features for machine learning, build feature vector representations of text, and reduce dimensions in text using hashing.

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Computer Programmer

Computer Programmers write and maintain code. This course may be useful for Computer Programmers as it covers how to represent text as features for machine learning, build feature vector representations of text, and reduce dimensions in text using hashing.

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Sales Representative

Sales Representatives sell products and services. This course may be useful for Sales Representatives as it covers how to represent text as features for machine learning, build feature vector representations of text, and reduce dimensions in text using hashing.

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Financial Analyst

Financial Analysts analyze financial data. This course may be useful for Financial Analysts as it covers how to represent text as features for machine learning, build feature vector representations of text, and reduce dimensions in text using hashing.

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Market Researcher

Market Researchers study market trends and customer behavior. This course may be useful for Market Researchers as it covers how to represent text as features for machine learning, build feature vector representations of text, and reduce dimensions in text using hashing.

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