The decision tree is one of the oldest and most intuitive classification algorithms in existence. This post provides a straightforward technical overview of this brand of classifiers.
This is an overview (with links) to a 5-part series on introductory machine learning. The set of tutorials is comprehensive, yet succinct, covering many important topics in the field (and beyond).
This is a write-up of an experiment employing a machine learning model to identify malicious URLs. The author provides a link to the code for you to try yourself.
The path to success and happiness of the data science team working with big data project is not always clear from the beginning. It depends on maturity of underlying platform, their cross skills and devops process around their day-to-day operations.
This post provides a technical overview of frequent pattern mining algorithms (also known by a variety of other names), along with its most famous implementation, the Apriori algorithm.
Learn more about Academic Torrents, a platform for researchers to share data consisting of a site where users can search for datasets, and a BitTorrent backbone which makes sharing data scalable and fast.
A carefully-curated list of 5 free ebooks to help you better understand the various aspects of what machine learning, and skills necessary for a career in the field.
This post outlines setting up a neural network in Python using Scikit-learn, the latest version of which now has built in support for Neural Network models.
Getting started with Data Science or need a refresher? Clustering is among the most used tools of Data Scientists. Check out these 10 Clustering-related terms and their concise definitions.
We interview LinkedIn about their recently published LinkedIn Knowledge Graph which connects their many millions of members, jobs, companies, and more.
MLDB is an openÂsource database designed for machine learning. Send it commands over a RESTful API to store data, explore it using SQL, then train machine learning models and expose them as APIs.
Learning and the future are the key topics in the recent Youtube videos on Data Science. The main questions revolve around: “how to become a Data Scientist”, “what is a data scientist”, and “where data science is going”. But why there is so little explanation of data science to the masses?
This article is meant to explain the concepts of AI, deep learning, and neural networks at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.
Welcome to the R graph gallery, a collection of R graph examples, organized by chart type, searchable by R function, with reproducible code and explanation.
From data driven strategies to decision making, the true worth of Big Data has been realized, and has led to opening up of amazing career choices. Check out these 12 interesting careers to explore in Big Data.
The use cases for big data are clear when it comes to areas like marketing, healthcare, and retail, but IT’s use of big data is a little less clear. Recently, however, some IT departments are finding ways to use big data to improve their individual operations along with that of the entire organization.
This post proposes and outlines adversarial validation, a method for selecting training examples most similar to test examples and using them as a validation set, and provides a practical scenario for its usefulness.
Google Research announces the Open Images dataset; Canadian Government Deep Learning Research grant; DeepMind: WaveNet - A Generative Model for Raw Audio; Machine Learning in a Year - From total noob to using it at work; Phd-level machine learning courses; xkcd: Linear Regression
First came Drew Conway's data science Venn diagram. Then came all the rest. Read this comparative overview of data science Venn diagrams for both the insight into the profession and the humor that comes along for free.
This is an interview with the authors of the recent winning KDnuggets Automated Data Science and Machine Learning blog contest entry, which provided an overview of the Auto-sklearn project. Learn more about the authors, the project, and automated data science.
Machine Learning: A Complete and Detailed Overview
Big Data Science: Expectation vs. Reality
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