Unsupervised Text Classification for Marketing Analytics from Coursera

Marketing data is often so big that humans cannot read or analyze a representative sample of it to understand what insights might lie within. In this course, learners use unsupervised deep learning to train algorithms to extract topics and insights from text data. Learners walk through a conceptual overview of unsupervised machine learning and dive into real-world datasets through instructor-led tutorials in Python. The course concludes with a major project.

This course uses Jupyter Notebooks and the coding environment Google Colab, a browser-based Jupyter notebook environment. Files are stored in Google Drive.

This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

What's inside

Syllabus

What is topic modeling?

In this module, we will cover the fundamental concepts of topic modeling, also known as unsupervised machine learning on unstructured text documents. We will contrast unsupervised methods to supervised ones and survey common applications of topic modeling.

The Assumptions of a Topic Model, Bag of Words, and Natural Language Processing

In this module, we will go under the hood inside a topic modeling approach and understand what assumptions drive topic model fit. We will also uncover how bag-of-words approaches to topic modeling work, and the natural language processing required to produce meaningful topic modeling features.

Prepping Amazon Review Data

In this module, we will cover how to parse through JSON-like data and segment it to create a corpus that is ready for the topic modeling process. We will cover how the data for your project is structured and its taxonomy.

Pre-Processing Text and Training a Topic Model

In this module, we will take Amazon review data and load it into a corpus to preprocess it. We will cover how to build topic models from the data and also save those topic models.

Topic Modeling Evaluation, Classification, and Neural Network Approaches

In this module, we will learn how to evaluate the fit of topic models and use the best topic model to classify documents. We will also cover how to build topic models with pre-trained neural networks.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores topic modeling, a fundamental approach to analyzing unstructured text data

Teaches unsupervised machine learning techniques for extracting insights from text data

Uses Python and Google Colab for hands-on tutorials, providing practical experience

Covers key concepts such as topic modeling assumptions, bag-of-words approaches, and natural language processing

Offers a major project to apply topic modeling to real-world datasets, fostering practical skills

May require prior programming experience to fully benefit from the practical components

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 Unsupervised Text Classification for Marketing Analytics with these activities:

Find a Mentor in Topic Modeling or Related Fields

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Provides access to guidance and support from experienced professionals in the field, enhancing learning and career development.

Browse courses on Topic Modeling

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Attend industry events or reach out to professionals on LinkedIn.
Explain your interest in topic modeling and seek guidance.
Build a professional relationship by asking for advice and feedback.

Follow Tutorials on Unsupervised Machine Learning

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Provides a guided introduction to unsupervised machine learning and its applications in topic modeling.

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Find tutorials on Coursera, edX, or YouTube.
Follow the tutorials step-by-step and complete the exercises.
Ask questions and seek help in the course forums.

Read 'Natural Language Processing with Python'

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Provides a solid foundation in natural language processing, which is essential for topic modeling.

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

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Read Chapters 1-3 to gain an overview of NLP and Python libraries.
Work through the exercises in Chapter 4 to practice text preprocessing.
Complete the projects in Chapters 5-7 to apply NLP techniques to real-world problems.

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Join a Study Group for Topic Modeling

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Facilitates collaboration and knowledge-sharing among peers, reinforcing understanding of topic modeling concepts.

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Find a study group or form one with classmates.
Meet regularly to discuss course materials and work on projects together.
Collaborate on assignments and provide feedback to each other.

Practice Topic Modeling with Gensim

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Provides hands-on experience with Gensim, the popular Python library for topic modeling.

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Install Gensim and create a Python environment.
Load a sample dataset and preprocess the text.
Train a topic model using Gensim's LDA algorithm.
Evaluate the topic model and interpret the results.

Write a Blog Post on Topic Modeling

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Encourages students to synthesize their understanding of topic modeling and communicate it effectively to others.

Browse courses on Topic Modeling

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Choose a specific aspect of topic modeling to focus on.
Research the topic and gather relevant information.
Write a clear and concise blog post explaining the topic.
Share the blog post with classmates or on social media.

Participate in a Topic Modeling Hackathon

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Provides a challenging and practical application of topic modeling skills in a competitive environment.

Browse courses on Topic Modeling

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Find a hackathon that focuses on topic modeling or related topics.
Form a team or work individually on a project.
Develop a solution that addresses the hackathon's challenge.
Submit your project and present it to the judges.

Contribute to an Open-Source Topic Modeling Project

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Provides an opportunity to apply topic modeling knowledge, contribute to the community, and gain experience in collaborative development.

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Find an open-source topic modeling project on platforms like GitHub.
Identify an area where you can contribute, such as bug fixes or feature enhancements.
Fork the project and make your changes.
Submit a pull request and work with the project maintainers to get it merged.

Career center

Learners who complete Unsupervised Text Classification for Marketing Analytics will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts use their knowledge of data analysis tools and techniques to extract insights from data. This course can help Data Analysts build a foundation in unsupervised machine learning, which is a powerful tool for extracting insights from unstructured text data. The course also covers natural language processing, which is a key skill for working with text data.

See salaries and explore the career path for Data Analyst

Product Manager

Product Managers are responsible for the development and launch of new products. This course can help Product Managers understand how to use unsupervised machine learning to identify customer needs and develop products that meet those needs. The course also covers topic modeling, which is a technique for identifying the main themes in a set of documents.

See salaries and explore the career path for Product Manager

Marketing Manager

Marketing Managers are responsible for developing and executing marketing campaigns. This course can help Marketing Managers understand how to use unsupervised machine learning to segment their audience and target their marketing campaigns more effectively. The course also covers natural language processing, which is a key skill for working with social media data.

See salaries and explore the career path for Marketing Manager

Business Analyst

Business Analysts use data to identify and solve business problems. This course can help Business Analysts build a foundation in unsupervised machine learning, which is a powerful tool for extracting insights from unstructured text data. The course also covers natural language processing, which is a key skill for working with text data.

See salaries and explore the career path for Business Analyst

Data Scientist

Data Scientists use their knowledge of data science tools and techniques to solve business problems. This course can help Data Scientists build a foundation in unsupervised machine learning, which is a powerful tool for extracting insights from unstructured text data. The course also covers natural language processing, which is a key skill for working with text data.

See salaries and explore the career path for Data Scientist

Software Engineer

Software Engineers design, develop, and test software applications. This course can help Software Engineers build a foundation in unsupervised machine learning, which is a powerful tool for developing new software applications. The course also covers natural language processing, which is a key skill for working with text data.

See salaries and explore the career path for Software Engineer

Machine Learning Engineer

Machine Learning Engineers design, develop, and test machine learning models. This course can help Machine Learning Engineers build a foundation in unsupervised machine learning, which is a powerful tool for developing new machine learning models. The course also covers natural language processing, which is a key skill for working with text data.

See salaries and explore the career path for Machine Learning Engineer

Data Engineer

Data Engineers design, build, and maintain data pipelines. This course can help Data Engineers build a foundation in unsupervised machine learning, which is a powerful tool for developing new data pipelines. The course also covers natural language processing, which is a key skill for working with text data.

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Web Developer

Web Developers design, develop, and test websites. This course can help Web Developers build a foundation in unsupervised machine learning, which is a powerful tool for developing new websites. The course also covers natural language processing, which is a key skill for working with text data.

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UX Designer

UX Designers design user interfaces for websites and applications. This course can help UX Designers build a foundation in unsupervised machine learning, which is a powerful tool for developing new user interfaces. The course also covers natural language processing, which is a key skill for working with text data.

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Content Writer

Content Writers create written content for websites, blogs, and other marketing materials. This course can help Content Writers build a foundation in unsupervised machine learning, which is a powerful tool for generating new content. The course also covers natural language processing, which is a key skill for working with text data.

See salaries and explore the career path for Content Writer

Copywriter

Copywriters create written content for advertising and marketing campaigns. This course can help Copywriters build a foundation in unsupervised machine learning, which is a powerful tool for generating new copy. The course also covers natural language processing, which is a key skill for working with text data.

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Technical Writer

Technical Writers create written content for technical documentation. This course can help Technical Writers build a foundation in unsupervised machine learning, which is a powerful tool for generating new documentation. The course also covers natural language processing, which is a key skill for working with text data.

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IT Support Specialist

IT Support Specialists provide technical support to users of computer systems. This course may be useful to IT Support Specialists who want to learn more about unsupervised machine learning and natural language processing.

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Customer Success Manager

Customer Success Managers help customers get the most value from their products and services. This course may be useful to Customer Success Managers who want to learn more about unsupervised machine learning and natural language processing.

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