Introduction to Topic Modelling in R from Coursera

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Syllabus

Project Overview

By the end of this project, you will know how to load and pre-process a data set of text documents by converting the data set into a document feature matrix and reducing it’s dimensionality. You will also know how to run an unsupervised machine learning LDA topic model (Latent Dirichlet Allocation). You will know how to plot the change in topics over time as well as explore the distribution of topic probability in each document. This project is aimed at beginners who have a basic familiarity with the statistical programming language R and the RStudio environment, or people with a small amount of experience who would like to learn how to apply topic modelling on textual data.

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Suitable for people who have basic familiarity with the statistical programming language R and the RStudio environment

Specifically designed for people with basic familiarity with the statistical programming language R and the RStudio environment

Useful for people who have a small amount of experience who would like to learn how to apply topic modelling on textual data

Provides hands-on experience with loading and pre-processing a data set of text documents, converting the data set into a document feature matrix, reducing its dimensionality, and running an unsupervised machine learning LDA topic model

Covers essential concepts of topic modeling, including topic distribution, topic probability, and the change in topics over time

Taught by Nicole Baerg, who has expertise in topic modelling and natural language processing

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 Introduction to Topic Modelling in R with these activities:

R and R Studio refresher

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Warm up your R and RStudio skills before starting the course to make learning more efficient.

Browse courses on R

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Review a tutorial on the basics of R
Practice writing and running simple R scripts in RStudio
Load and explore a sample dataset in R

Read 'Data Science for Business'

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This book provides a comprehensive overview of data science concepts and techniques, including LDA.

View Data Science for Business: What You Need to... on Amazon

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Read Chapters 6-8 on unsupervised learning
Focus on the sections on LDA and other topic modeling techniques
Complete the exercises and review the case studies

Complete tutorials on LDA in R

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Following tutorials can help you reinforce your understanding of LDA and its implementation in R.

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Work through the tutorial on LDA in the `tidymodels` package
Complete the LDA exercises in the `r-tidymodels` book
Explore other LDA tutorials and resources online

Five other activities

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Create a visual representation of LDA topics

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Visualizing LDA topics can help you understand the structure of your text data.

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Use the `tidytext` package to extract LDA topics from your data
Create a visualization of the topics using a word cloud or other method
Interpret the results and discuss the insights gained

Attend an industry conference on machine learning

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Networking with professionals in the field can expose you to new ideas and applications of LDA.

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Research industry conferences that focus on machine learning
Attend sessions on LDA and other topic modeling techniques
Connect with attendees and discuss your interests in LDA

Practice LDA modeling with different parameters

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Experimenting with different LDA parameters can help you optimize your model for your specific data.

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Modify the number of topics in your LDA model
Adjust the alpha and beta hyperparameters
Evaluate the results of each model using perplexity or other metrics

Write a blog post on LDA for beginners

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Explaining LDA to others can help you solidify your understanding of the technique.

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Gather resources and information on LDA
Outline the key concepts and applications of LDA
Write and publish your blog post

Develop an LDA-based text classification model

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Applying LDA to a real-world text classification task will deepen your understanding of the technique.

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Collect and clean a dataset of text documents
Train an LDA model on the data and extract topics
Use the LDA topics to build a text classifier

Career center

Learners who complete Introduction to Topic Modelling in R will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts study data using specialized applications and programming languages to extract meaningful insights. They help businesses understand trends, solve complex issues, and improve their operations. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Data Analysts. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of customer feedback, social media data, and other forms of unstructured data. This knowledge can help you make better decisions and drive business growth.

See salaries and explore the career path for Data Analyst

Market Researcher

Market Researchers gather and analyze data about consumer behavior, market trends, and competitors. They use this information to help businesses make informed decisions about product development, marketing campaigns, and pricing. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Market Researchers. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of customer feedback, social media data, and other forms of unstructured data. This knowledge can help you make better decisions and drive business growth.

See salaries and explore the career path for Market Researcher

Business Analyst

Business Analysts study business processes and systems to identify areas for improvement. They use data analysis and modeling techniques to develop solutions that can help businesses improve their efficiency, productivity, and profitability. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Business Analysts. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of customer feedback, employee surveys, and other forms of unstructured data. This knowledge can help you make better recommendations and drive business growth.

See salaries and explore the career path for Business Analyst

Data Scientist

Data Scientists use data to solve complex problems and make predictions. They use a variety of statistical and machine learning techniques to analyze data and develop models that can help businesses make better decisions. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Data Scientists. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of customer feedback, social media data, and other forms of unstructured data. This knowledge can help you build better models and drive business growth.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine learning models. They use a variety of programming languages and software tools to build models that can learn from data and make predictions. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Machine Learning Engineers. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of customer feedback, social media data, and other forms of unstructured data. This knowledge can help you build better models and drive business growth.

See salaries and explore the career path for Machine Learning Engineer

Software Engineer

Software Engineers design, develop, and maintain software applications. They use a variety of programming languages and software tools to build applications that meet the needs of users. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Software Engineers. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of user feedback, bug reports, and other forms of unstructured data. This knowledge can help you build better software and drive business growth.

See salaries and explore the career path for Software Engineer

Product Manager

Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring products to market that meet the needs of customers. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Product Managers. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of customer feedback, market research, and other forms of unstructured data. This knowledge can help you make better decisions and drive product success.

See salaries and explore the career path for Product Manager

Marketing Manager

Marketing Managers are responsible for developing and executing marketing campaigns. They work with a variety of teams to create and implement marketing plans that reach target audiences and achieve business goals. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Marketing Managers. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of customer feedback, market research, and other forms of unstructured data. This knowledge can help you make better decisions and drive marketing success.

See salaries and explore the career path for Marketing Manager

Sales Manager

Sales Managers are responsible for leading and motivating sales teams. They work with salespeople to develop and execute sales strategies that achieve business goals. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Sales Managers. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of customer feedback, sales data, and other forms of unstructured data. This knowledge can help you make better decisions and drive sales growth.

See salaries and explore the career path for Sales Manager

Customer Success Manager

Customer Success Managers are responsible for building and maintaining relationships with customers. They work with customers to ensure that they are satisfied with their products or services and that they are getting the most value from them. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Customer Success Managers. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of customer feedback, support tickets, and other forms of unstructured data. This knowledge can help you make better decisions and drive customer success.

See salaries and explore the career path for Customer Success Manager

Content Strategist

Content Strategists are responsible for developing and executing content strategies. They work with a variety of teams to create and implement content that meets the needs of target audiences and achieves business goals. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Content Strategists. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of customer feedback, market research, and other forms of unstructured data. This knowledge can help you make better decisions and drive content success.

See salaries and explore the career path for Content Strategist

Public relations manager

Public Relations Managers are responsible for managing the public relations of an organization. They work with a variety of stakeholders to build and maintain a positive image of the organization. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Public Relations Managers. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of public sentiment, media coverage, and other forms of unstructured data. This knowledge can help you make better decisions and drive public relations success.

See salaries and explore the career path for Public relations manager

Communications Manager

Communications Managers are responsible for developing and executing communications strategies. They work with a variety of teams to create and implement communications that reach target audiences and achieve business goals. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Communications Managers. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of customer feedback, market research, and other forms of unstructured data. This knowledge can help you make better decisions and drive communications success.

See salaries and explore the career path for Communications Manager

Technical Writer

Technical Writers are responsible for creating and maintaining technical documentation. They work with a variety of stakeholders to create documentation that is clear, concise, and accurate. This course can help you develop the skills needed to analyze text data, which is a valuable skill for Technical Writers. By learning how to identify topics and patterns in text data, you can gain a deeper understanding of user feedback, product documentation, and other forms of unstructured data. This knowledge can help you make better decisions and drive technical writing success.

See salaries and explore the career path for Technical Writer

Reading list

We've selected seven 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 Introduction to Topic Modelling in R.

Binary Neural Networks

Save

Provides a comprehensive overview of topic models, including the mathematical foundations, algorithms, and applications. It valuable resource for researchers and practitioners who want to learn more about topic modeling.

Binary Neural Networks: Algorithms, Architectures,...

Kindle Edition

$$$

R概率图模型入门与实践

Save

This paper introduces the Latent Dirichlet Allocation (LDA) model, a generative statistical model for collections of discrete data such as text corpora. It is one of the most widely used topic models and has been applied to a variety of problems in natural language processing.

Information Theory, Inference and Learning...

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Provides a practical introduction to generative models, including topic models. It valuable resource for researchers and practitioners who want to learn more about generative models and their applications.

Information Theory, Inference and Learning...

Hardcover

$$$

Natural Language Processing with Python

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Provides a practical introduction to natural language processing with Python. It covers a variety of topics, including topic modeling, part-of-speech tagging, and named entity recognition.

Natural Language Processing with Python: Analyzing...

Paperback

$$$

Natural Language Processing with Python: Analyzing...

Kindle Edition

Text Mining with R

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Provides a practical introduction to text mining with R. It covers a variety of topics, including topic modeling, text classification, and sentiment analysis.

Practical Data Science for Information Professionals

Save

This paper provides a gentle introduction to topic modeling. It valuable resource for researchers and practitioners who want to learn more about topic modeling and its applications.

Practical Data Science For Information Professionals

Paperback

Practical Text Analytics

Save

This paper introduces the Probabilistic Latent Semantic Analysis (PLSA) model, a probabilistic model for text data. It valuable resource for researchers and practitioners who want to learn more about PLSA and its applications.

Practical Text Analytics: Maximizing the Value of...

Kindle Edition

$$$

Introduction to Topic Modelling in R

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