Python-bloggers

AI for Engineers

October 9, 2019 | John Mount

For the last year we (Nina Zumel, and myself: John Mount) have had the honor of teaching the AI200 portion of LinkedIn’s AI Academy. John Mount at the LinkedIn campus Nina Zumel designed most of the material, and John Mount has been delivering it and bringing her feedback. We’...

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Calling covered data

October 8, 2019 | OSM

In our last post on covered calls we introduced the CBOE’s buy-write index (or BXM), whose underlying is the S&P500 index. We looked at some of the historical data, made a few comparisons between the index and the S&P, and noted that there was a report that ...

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Calling covered data

October 8, 2019 | OSM

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vtreat Cross Validation

October 6, 2019 | John Mount

Nina Zumel finished new documentation on how vtreat‘s cross validation works, which I want to share here. vtreat is a system that makes data preparation for machine learning a “one-liner” (available in R or available in Python). We have a set of starting off points here. These documents describe ... [...Read more...]

New vtreat Documentation (Starting with Multinomial Classification)

October 1, 2019 | John Mount

Nina Zumel finished some great new documentation showing how to use Python vtreat to prepare data for multinomial classification mode. And I have finally finished porting the documentation to R vtreat. So we now have good introductions on how to use vtreat to prepare data for the common tasks of: ... [...Read more...]

Matplotlib Cheat Sheet: Plotting in Python

October 1, 2019 | Karlijn Willems

Data visualization and storytelling with your data are essential skills that every data scientist needs to communicate insights gained from analyses effectively to any audience out there. For most beginners, the first package that they use to get in ... [...Read more...]

101 Data Science Interview Questions, Answers, and Key Concepts

September 27, 2019 | Tooba Mukhtar

Interviews are difficult for most people. You don't want to mess up during it, but you always think of a better response after-the-fact. Here are 101 data science interview questions with responses and suggestions from large tech companies like Amazon, Google, and Microsoft.

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How to Prepare Data

September 26, 2019 | John Mount

Real world data can present a number of challenges to data science workflows. Even properly structured data (each interesting measurement already landed in distinct columns), can present problems, such as missing values and high cardinality categorical variables. In this note we describe some great tools for working with such data. ... [...Read more...]

Preparing Data for Supervised Classification

September 24, 2019 | John Mount

Nina Zumel has been polishing up new vtreat for Python documentation and tutorials. They are coming out so good that I find to be fair to the R community I must start to back-port this new documentation to vtreat for R. vtreat is a package for systematically preparing data for ... [...Read more...]

The Advantages of Record Transform Specifications

September 18, 2019 | John Mount

Nina Zumel had a really great article on how to prepare a nice Keras performance plot using R. I will use this example to show some of the advantages of cdata record transform specifications. The model performance data from Keras is in the following...

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