Analytics Vidhya
Published in

Analytics Vidhya

Machine Learning Must Know — From Raw to Training Data

This topic seems too rudimentary, yet I found most machine learning books do not cover. Most machine learning books cover the techniques to split the modeling data randomly into training, test and validation datasets, then the topics quickly turn into k-fold cross-validation. But wait, how do we prepare the modeling data? The number of transactions of a credit card company can be billions, but…




Analytics Vidhya is a community of Analytics and Data Science professionals. We are building the next-gen data science ecosystem

Recommended from Medium

The plight of the data scientist

[OSINT/GEOINT] Using shadows and optics to geolocate a photo in a US military base

No organization is an island — can we build an open and integrated labor data ecosystem?

Machine Learning on Graphs, Part 1

Yum! Brands and McDonald’s Corporation Loyalty Program Value

Mcdonald’s Cafe

#66DaysOfData — Days 15 to 17: Building an OCEAN — Attrition Formula

Data Visualization in Practice 101

New York MTA Challenge: A Data Science Story

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Chris Kuo/Dr. Dataman

Chris Kuo/Dr. Dataman

The Dataman articles are my reflections on data science and teaching notes at Columbia University

More from Medium

What is Cosine Similarity? How to Compare Text and Images in Python

Pipeline in Sklearn: an efficient method to assemble multiply steps and configure parameters

In Depth: Naive Bayes Classification

K-Means: The maths behind it, how it works and an example