Marc Kelechava

Published in Towards Data Science

·Pinned

Urban Sound Classification with Librosa — tricky cross-validation

Featuring the Leave One Group Out strategy using scikit-learn — Outline The goal of this post is two-fold: I’ll show an example of implementing the results of an interesting research paper on classifying audio clips based on their sonic content. This will include applications of the librosa library, which is a Python package for music and audio analysis. …

Data Science

11 min read

Urban Sound Classification with Librosa — tricky cross-validation

Data Science

11 min read

Published in The Craft

·Sep 19

Monitoring machine learning systems at Faire

Introduction Faire’s wholesale marketplace allows our 500,000+ retailer buyers to discover new products from over 70,000 independent brands globally. Our Search and Recommendation team designs and implements models and algorithms to personalize product rankings across the search results page, category navigation, brand page, and recommendation carousels (for example: “Products You May…

Machine Learning

11 min read

Machine Learning

11 min read

Published in Towards Data Science

·Mar 4, 2019

Using LDA Topic Models as a Classification Model Input

Predicting Future Yelp Review Sentiment — Topic Modeling Overview Topic Modeling in NLP seeks to find hidden semantic structure in documents. They are probabilistic models that can help you comb through massive amounts of raw text and cluster similar groups of documents together in an unsupervised way. This post specifically focuses on Latent Dirichlet Allocation (LDA), which was a…

Machine Learning

13 min read

Using LDA Topic Models as a Classification Model Input

Machine Learning

13 min read