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