3 Main Approaches to Machine Learning Models

3 Main Approaches to Machine Learning Models

Machine learning encompasses a vast set of conceptual approaches. We classify the three main algorithmic methods based on mathematical foundations to guide your exploration for developing models. By Ajit Jaokar, FutureText and Oxford. comments In Sep...

Digible: Data Engineer [Denver, CO]

Digible: Data Engineer [Denver, CO]

Seeking an experienced Data Engineer to wrangle data, build data pipelines, and architect, build, and maintain our data warehouse & science infrastructure.  In partnership with our Data Scientist, the Engineer will also have the opportunity to scale...

Choosing Between Model Candidates

Choosing Between Model Candidates

Models are useful because they allow us to generalize from one situation to another. When we use a model, we’re working under the assumption that there is some underlying pattern we want to measure, but it has some error on top of it. By Brandon Rohr...

Probability Mass and Density Functions

Probability Mass and Density Functions

This content is part of a series about the chapter 3 on probability from the Deep Learning Book by Goodfellow, I., Bengio, Y., and Courville, A. (2016). It aims to provide intuitions/drawings/python code on mathematical theories and is constructed as...

The Data Fabric for Machine Learning – Part 1

The Data Fabric for Machine Learning – Part 1

How the new advances in semantics and the __data fabric can help us be better at Machine Learning By Favio Vazquez, Founder at Ciencia y Datos. comments Image by Héizel Vázquez Read part 1-b: Deep Learning on the __data fabric here: The data Fabric f...

Your Guide to Natural Language Processing (NLP)

Your Guide to Natural Language Processing (NLP)

This extensive post covers NLP use cases, basic examples, Tokenization, Stop Words Removal, Stemming, Lemmatization, Topic Modeling, the future of NLP, and more. comments By Diego Lopez Yse, Moody's Operations LATAM. Everything we express (either ver...