NLP for FinanceCornell

Certificate Program

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

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

With your text data effectively cleaned and primed for an algorithm, you're now poised to put it into practical use. While you've created Latent Dirichlet Allocation (LDA) models in prior courses, you've done so using default settings, which may not be ideal for the specific data at hand. To fully ready your models for active portfolio management, you need to train and evaluate them against an industry standard. Only with this assurance can you make associations that are relevant within an investment context, enabling you to construct portfolios of companies that align with a desired industry sector or theme.

In this course, you'll train a variety of LDA topic models in an iterative process to enhance their performance. You'll evaluate their alignment with widely accepted industry classifications to compile lists of comparable companies relevant to a specific investment theme. The process will range from fine-tuning various hyperparameters to optimize the LDA algorithm's learning curve to calculating distance metrics for comparable companies to ascertain their topic similarity with respect to an investment benchmark.

As you progress through the course, you'll conduct an array of comparative analyses to discern the strengths and weaknesses of the LDA approach. Recognizing these aspects is crucial when it comes to the construction and management of investment portfolios. By the end of the course, you'll be adept at training, refining, and applying LDA models, paving the way for smarter, data-driven investment decisions.

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

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