How to train, tune, and validate a machine learning model

How to Extract Features from Raw Data for Machine Learning

An explanation and implementation of the first step in solving problems with machine learning.

A General-Purpose Framework for Defining and Solving Meaningful Problems in 3 Steps.

Distributed feature engineering in Featuretools with Spark Apache Spark is one of the most popular technologies on the big data landscape. As a framework for distributed computing, it allows users to scale to massive datasets by running computations in parallel either on a single machine or on clusters of thousands

How to scale automated feature engineering using parallel processing When a computation is prohibitively slow, the most important question to ask is: “What is the bottleneck?” Once you know the answer, the logical next step is to figure out how to get around that bottleneck. Often, as we’ll see,

Automated feature engineering will save you time, build better predictive models, create meaningful features, and prevent data leakage There are few certainties in data science — libraries, tools, and algorithms constantly change as better methods are developed. However, one trend that is not going away is the move towards increased levels