data science

The Computer Vision Pipeline, Part 4: feature extraction

From Deep Learning for Vision Systems by Mohamed Elgendy

In this part, we will take a look at feature extraction—a core component of the computer vision pipeline.

Combining Human and Machine Intelligence

From Human-in-the-Loop Machine Learning by Robert (Munro) Monarch

Maximise Customer Retention

From Fighting Churn with Data by Carl Gold

Is my Problem a Graph Problem?

From Graph Databases in Action by Dave Bechberger and Josh Perryman

In this article, we’ll review what makes a problem a good graph use case. We’ll start by examining a few general categories of problems and discussing why they might make for good graph use case.  Finally, we’ll analyze a general framework that we can use to help us decide if our problem is a good graph use case.

The Data Scientist’s Survival Guide

From Build a Career in Data Science by Emily Robinson and Jacqueline Nolis


Want to Learn Machine Learning Inside Out?

From Grokking Machine Learning by Luis G. Serrano


Working with Large Datasets Faster: using the map function

From Mastering Large Datasets by JT Wolohan

This article explores using the map function creatively in a data project.

Tidying, Manipulating, and Plotting Data with the tidyverse

From Machine Learning with R, tidyverse, and mlr by Hefin Rhys

This article covers:

• What the tidyverse is
• What’s meant by tidy data
• How to install and load the tidyverse
• How to use the tibble, dplyr, ggplot2 and tidyr packages of the tidyverse

Data Analytics on Azure

From Azure Storage, Streaming, and Batch Analytics
By Richard L. Nuckolls

Machine Learning from the Ground up

From Machine Learning with R, tidyverse, and mlr by Hefin I. Rhys


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