Data Science - FloydHub Blog

Sign in Subscribe

Data Science

A collection of 9 posts

FloydHub Cloud Setup Challenge: Jupyter + TensorFlow in 44 seconds [WR]

FloydHub Cloud Setup Challenge: Jupyter + TensorFlow in 44 seconds [WR]

Is it possible for data science beginners to get up and running in under 90 seconds? FloydHub’s team takes on the setup cloud challenge - and walks away with the trophy. (For now!)

Naïve Bayes for Machine Learning – From Zero to Hero

Naïve Bayes for Machine Learning – From Zero to Hero

Bayes’ Theorem is about more than just conditional probability, and Naive Bayes is a flavor of the theorem which adds to its complexity and usefulness.

A Pirate's Guide to Accuracy, Precision, Recall, and Other Scores

A Pirate's Guide to Accuracy, Precision, Recall, and Other Scores

Once you've built your classifier, you need to evaluate its effectiveness with metrics like accuracy, precision, recall, F1-Score, and ROC curve.

Multiprocessing vs. Threading in Python: What Every Data Scientist Needs to Know

Multiprocessing vs. Threading in Python: What Every Data Scientist Needs to Know

This deep dive on Python parallelization libraries - multiprocessing and threading - will explain which to use when for different data scientist problem sets.

When Not to Choose the Best NLP Model

When Not to Choose the Best NLP Model

The world of NLP already contains an assortment of pre-trained models and techniques. This article discusses how to best discern which model will work for your goals.

N-Shot Learning: Learning More with Less Data

N-Shot Learning: Learning More with Less Data

Is it possible to use machine learning with small data? Yes, it is! Here's N-Shot Learning.

Becoming One With the Data

Becoming One With the Data

This article discusses effective ways of handling the data in machine learning projects.

DIY Data: Web Scraping with Python and BeautifulSoup

DIY Data: Web Scraping with Python and BeautifulSoup

Getting sufficient clean, reliable data is one of the hardest parts of data science. Web scraping automates the process of visiting web pages, downloading the data, and cleaning the results. With this technique, we can create new datasets from a large compendium of web pages.

Statistics for Data Science

Statistics for Data Science

The article elucidates the importance of statistics in the field of data science, wherein "Statistics" is imagined as a friend to a data scientist and their friendship is unraveled.