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Course Description

In today's fast-paced business world, staying ahead of the competition necessitates swiftly understanding and capitalizing on enormous volumes of data. AI’s machine learning algorithms can certainly assist in deciphering that data, but when it comes to text, a different strategy is needed. Text, rich in context and information, needs to be compressed, evaluated, and contextualized differently than numerical data. This is where natural language processing, a fascinating branch of machine learning, comes into play. Businesses are increasingly leveraging NLP to mine insights from unstructured text data.

This course invites you to delve into various techniques to obtain, prepare, and refine data for NLP applications. We'll be focusing our efforts on prepping text data for efficient processing by the Latent Dirichlet Allocation (LDA) algorithm. From identifying the types of business text data relevant for investment applications, you'll move on to training and evaluating the LDA model, ensuring the output aligns with the topics present in the data.

Along this journey, you'll harness the power of word frequencies in your data to create and visualize topic groupings. By fine-tuning the composition of the input data, you'll be able to optimize the performance of the LDA algorithm. This course provides you with a thorough understanding of how to transform textual data into a format suitable for insightful analysis, ultimately boosting your business decision-making

Faculty Author

Chris Meredith

Benefits to the Learner

  • Outline a business classification project
  • Prepare business data for natural language processing
  • Describe how the LDA topic modeling algorithm works
  • Evaluate word frequencies to identify and strip irrelevant terms
  • Apply the LDA algorithm to cleaned business descriptions

Target Audience

  • Financial analysts
  • Quant finance investors
  • Market analysts and business analysts
  • Data scientists
  • Software engineers

Applies Towards the Following Certificates

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Enroll Now - Select a section to enroll in
Type
2 week
Dates
May 29, 2024 to Jun 11, 2024
Total Number of Hours
20.0
Course Fee(s)
Contract Fee $100.00
Section Notes

IMPORTANT COURSE INFORMATION

  • Please note that the content in the NLP for Finance course curriculum was developed to be completed in sequential order as course concepts build throughout the program. With this in mind, please be sure you are scheduled to complete or have completed the courses in order; for example, JCB661 prior to JCB662, JCB662 prior to JCB663, etc.
  • In order to be successful in this program, students should have a working knowledge of Python programming as well as sufficient English language fluency as some aspects of the data cleaning have relations to English.
Type
2 week
Dates
Aug 21, 2024 to Sep 03, 2024
Total Number of Hours
16.0
Course Fee(s)
Contract Fee $100.00
Section Notes

IMPORTANT COURSE INFORMATION

  • Please note that the content in the NLP for Finance course curriculum was developed to be completed in sequential order as course concepts build throughout the program. With this in mind, please be sure you are scheduled to complete or have completed the courses in order; for example, JCB661 prior to JCB662, JCB662 prior to JCB663, etc.
  • In order to be successful in this program, students should have a working knowledge of Python programming as well as sufficient English language fluency as some aspects of the data cleaning have relations to English.
Type
2 week
Dates
Nov 13, 2024 to Nov 26, 2024
Total Number of Hours
16.0
Course Fee(s)
Contract Fee $100.00
Section Notes

IMPORTANT COURSE INFORMATION

  • Please note that the content in the NLP for Finance course curriculum was developed to be completed in sequential order as course concepts build throughout the program. With this in mind, please be sure you are scheduled to complete or have completed the courses in order; for example, JCB661 prior to JCB662, JCB662 prior to JCB663, etc.
  • In order to be successful in this program, students should have a working knowledge of Python programming as well as sufficient English language fluency as some aspects of the data cleaning have relations to English.
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