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Course Description

AI’s NLP machine learning algorithms possess an incredible knack for unearthing nonlinear relationships within text data. Yet their success is intimately tied to the quality of the data they're provided. The finesse of text pre-processing lies in refining written text, ensuring all irrelevant or erroneous content is eliminated, leaving only the essence or target meaning of words in your dataset. With a clean, distraction-free dataset, the Latent Dirichlet Allocation (LDA) algorithm can effectively group companies by topics based on similarities in their operational activities.

In this course, you'll discover how to meticulously identify and eliminate noisy or irrelevant words in business descriptions — words that provide scant context for the LDA algorithm. You'll gauge your success through the enhancement of word frequencies as inputs and model performance as outputs. The journey will take you from addressing punctuation and identifying low/high-frequency words of little relevance to evaluating the cleanliness of the resulting topic groupings via word clouds.

As you navigate this course, you'll employ a range of crucial text pre-processing techniques to iteratively refine descriptions, thereby optimizing the LDA model's performance in generating topic groupings that truly reflect the unique industry sectors represented across your business description datasets. This course aims to hone your text pre-processing skills, empowering you to maximize the potential of NLP algorithms in your business decision making.

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

  • Preparing Data for Natural Language Processing

Faculty Author

Chris Meredith

Benefits to the Learner

  • Replace dates and numbers with strings
  • Strip punctuation and distill terms
  • Strip noisy, infrequent, and irrelevant stopwords
  • Evaluate LDA model performance

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
Nov 27, 2024 to Dec 10, 2024
Total Number of Hours
16.0
Course Fee(s)
Regular Price $999.00
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
Jan 22, 2025 to Feb 04, 2025
Total Number of Hours
16.0
Course Fee(s)
Regular Price $999.00
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
Feb 19, 2025 to Mar 04, 2025
Total Number of Hours
16.0
Course Fee(s)
Regular Price $999.00
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
May 14, 2025 to May 27, 2025
Total Number of Hours
16.0
Course Fee(s)
Regular Price $999.00
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 06, 2025 to Aug 19, 2025
Total Number of Hours
16.0
Course Fee(s)
Regular Price $999.00
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 29, 2025 to Nov 11, 2025
Total Number of Hours
16.0
Course Fee(s)
Regular Price $999.00
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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