Subject

Data

Serverless AI Solutions

From AI as a Service by Peter Elger, Eoin Shanaghy, and Johannes Ahlmann

slideshare-serverless-ai-solutions

Machine Learning from the Ground up

From Machine Learning for Mortals (Mere and Otherwise) by Hefin I. Rhys

slideshare-machine-learning-from-the-ground-up

Beyond Beyond Spreadsheets

Six Questions for Jonathan Carroll, author of Beyond Spreadsheets with R

By Frances Lefkowitz

Jonathan Carroll is a data science consultant providing R programming services. He holds a PhD in theoretical physics.

Modern Data Solutions with Python

From Python for Big Datasets by John T. Wolohan

The Inner Workings of Spark

spark_in_act

From Spark in Action, Second Edition by Jean George Perrin

The Magic of Graphs and Machine Learning

PMachineLearning-MM
From Graph-Powered Machine Learning by Alessandro Negro

The Random Cut Forest Algorithm

From Machine Learning for Business by Doug Hudgeon and Richard Nichol

In this article, you’ll see how SageMaker and the Random Cut Forest algorithm can be used to create a model that will highlight the invoice lines that Brett should query with the law firm. The result will be a repeatable process that Brett can apply to every invoice that will keep the lawyers working for his bank on their toes and will save the bank hundreds of thousands of dollars per year. Off we go!

Building Linear Models with Dask ML

From Data Science at Scale with Python and Dask by Jesse C. Daniel

This article delves into building linear models using Dask-ML.

Handling Data with R

From Practical Data Science with R, 2nd Ed. By Nina Zumel and John Mount

In this article, we demonstrate some ways to get to know your data, and discuss some of the potential issues that you’re looking for as you explore.

Ingesting Data from Files with Spark, Part 4

From Spark in Action, 2nd Ed. by Jean Georges Perrin

This is the last in a series of 4 articles on the topic of ingesting data from files with Spark. This section deals with ingesting a TXT file.

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