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Course Description

The Latent Dirichlet Allocation (LDA) algorithm is undoubtedly a powerful tool for text data analysis. Like any tool, however, it has certain limitations that need to be acknowledged before its application in real-world scenarios. It’s therefore beneficial to examine other algorithms to compare their performance and application, helping you choose the most fitting method for your NLP projects. Enter the Doc2Vec algorithm, another frequently used tool for text data analysis. It takes a unique approach by creating numerical vectors that encapsulate the context and relation of words to documents, instead of generating topics based on word frequency. Despite its own limitations, Doc2Vec possesses certain strengths that are extremely relevant to the construction and management of investment portfolios.

In this course, we'll explore the Doc2Vec algorithm as an alternative approach to text data analysis. You'll replicate many of the same general operations you performed in previous courses with the LDA algorithm. Your journey will involve training and evaluating an initial Doc2Vec model then crafting your own custom vectors to build lists of comparable companies relevant to specific investment themes.

As we delve into the course, you'll introduce additional algorithms as part of your analysis. You'll explore different ways to customize and visualize results, comparing them against an industry standard and real-world investment portfolios. By the end of this course, you will have gained a deep understanding of multiple NLP algorithms, their strengths and weaknesses, and how to make an informed choice for your specific needs in the financial markets.

The following course is required to be completed before taking this course:

  • Preparing Data for Natural Language Processing
  • Cleaning Text Data to Optimize Model Performance
  • Tuning your NLP Model for Market Relevance

Faculty Author

Chris Meredith

Benefits to the Learner

  • Train and compare a semantic NLP model with an industry standard
  • Map semantic vectors to companies for portfolio construction
  • Enhance portfolio relevance through ensembling

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
Jul 10, 2024 to Jul 23, 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
Oct 02, 2024 to Oct 15, 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
Dec 25, 2024 to Jan 07, 2025
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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