Three-Project Series

Traditional and Neural Topic Modeling you own this product

intermediate Python • linear algebra • probability • basics of machine learning • basics of deep learning
skills learned
implementing simplified versions of NMF and LDA algorithms from scratch • preprocessing a text corpus and converting into a document-to-word matrix • visualizing derived topics with a variety of techniques • evaluating generated topics using Coherence and Diversity metrics
Aneesha Bakharia
3 weeks · 6-8 hours per week average · INTERMEDIATE

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5, 10 or 20 seats+ for your team - learn more

In this series of liveProjects, you’ll explore different techniques for topic modeling. Topic modeling is an incredibly useful unsupervised machine learning technique that allows you to find topics in text without needing any manual labelling. It’s a great way to quickly derive insights from text data and share them with key stakeholders. You’ll work with a variety of different text data corpuses to go hands-on with NMF algorithms from scikit-learn, LDA algorithms from Gensim, and even new neural network techniques using the OCTIS (Optimizing and Comparing Topic Models is Simple!) library.

These projects are designed for learning purposes and are not complete, production-ready applications or solutions.

here's what's included

Project 1 Non-negative Matrix Factorization

In this liveProject you’ll use scikit-learn’s non-negative matrix factorization algorithm to perform topic modeling on a dataset of Twitter posts. You’ll step into the role of a data scientist tasked with summarizing Twitter discussions for the customer support team of an airline company and use this powerful algorithm to rapidly make sense of a large and complex text corpus. You’ll build a text preprocessing pipeline from scratch, visualize topic models, and finally compile a report of support topics for the customer services team.

Project 2 Latent Dirichlet Allocation

In this liveProject, you’ll use the latent dirichlet allocation (LDA) algorithm from the Gensim library to model topics from a magazine’s article back catalog. Thanks to your work on topic modeling, the new Policy and Ethics editor will be better equipped to strategically commission new articles for under-represented topics. You’ll build your text preprocessing pipeline, use topic coherence to find the number of topics, and visualize and curate the algorithm’s output for your stakeholders to easily read.

Project 3 Neural Topic Models

In this liveProject, you’ll use the neural network-inspired Contextual Topic Model to identify and visualize all of the articles in a scientific magazine’s back catalog. This cutting-edge technique is made easy by the OCTIS (Optimizing and Comparing Topic Models is Simple!) library. Once you’ve established your text-processing pipeline, you’ll use coherence and diversity metrics to evaluate the output of your topic models, tune your neural network’s hyperparameters to improve results, and visualize your results for printing on posters and other media.

book resources

When you start each of the projects in this series, you'll get full access to the following book for 90 days.

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project author

Aneesha Bakharia
Aneesha Bakharia completed her PhD in interactive topic modeling at Queensland University of Technology in Australia. She is currently the Manager of Learning Analytics at The University of Queensland where she leads a team of programmers and data scientists. She has written 10 books on programming and web development for Cengage publishing. She also blogs about data science related topics, including topic modeling and she publishes academic peer reviewed publications on educational technologies and learning analytics.


This liveProject series is for data scientists and developers who are confident programming with Python and the Python data ecosystem. To begin this liveProject you will need to be familiar with the following:

  • Intermediate Python
  • Basics of Jupyter Notebook
    • Linear algebra
    • Probability
    • Referencing Matrix cells by row and column index
    • Matrix subtraction, multiplication, and division
    • Basics of machine learning and deep learning

you will learn

In this liveProject, you’ll master topic modeling—an amazing skill for quickly analyzing textual datasets. You’ll learn the ins and outs of applying the NMF, LDA, and neural inspired Contextual Topic Model (CTM) algorithms in real-world settings:

  • Implementing simplified versions of NMF and LDA algorithm
  • Preprocessing a text corpus (tokenization, lemmatization and stop word removal) into a document-to-word matrix
  • Deriving topics using the scikit-learn NMF, Gensim LDA and OCTIS CTM algorithms
  • Evaluating topics with Coherence and Diversity metrics
  • Visualizing derived topics with a variety of techniques
  • Exploring the vector representations produced by the Bidirectional Encoder Representations from Transformers (BERT) neural network and their use in the Contextual Topic Model (CTM)
  • Using the Contextual Topic Model and comparing derived topics with LDA


You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.