Why Learn Linear Algebra for Machine Learning?
Linear algebra is a field of mathematics that could be called the mathematics of data.

It is undeniably a pillar of the field of machine learning, and many recommend it as a prerequisite subject to study…

Learning SQL might not be as "sexy" as learning Python or R, but it's a fundamental skill for almost every data scientist and data analyst job. Here's why.

Overfitting is on of the primary problems, if not THE primary problem in machine learning. There are many aspects to it, but in a general sense, …

Like death and taxes, there are only two safe predictions about cybersecurity in 2018: There will be more spectacular data breaches and the EU General Data Protection Regulation (GDPR) will go into…

I call myself a statistician, because, well, I’m a statistics graduate student. However, the most important things I’ve learned are less formal, but have proven extremely useful when wo…

Get this Free eBook from O'Reilly Publishing on "Artificial Intelligence and Machine Learning in Industry -- Perspectives from Leading Practitioners" Talks about about the practical application of A.I. and Machine Learning in business.

It seems like every quarter a new McKinsey report predicts that this will be the year trillions of dollars of IoT potential is unlocked. But while the amount of data IoT produces has skyrocketed, we’re still waiting for that return on investment. The good…

Photo by Glenn Scott Today I got an email with a question I’ve heard many times – “How many images do I need to train my classifier?”. In the early days I would reply with t…

Guest post by Finnegan Pierson. Big data entails large volumes of data that are analyzed computationally to show trends and pattern. It comprises 3Vs; enormous volumes of data, the velocity of processing data, and the broad variety of data categories. Big…

We recently published a practical guide for data science management intended to help current and aspiring managers learn from the challenges and successes

This article provides an introductory guide to factorization machines (FM) and Field Aware Factorization (FFM) used for making predictions on huge datasets.

In this entry, I am going to talk about deep learning models exposure and serving via Tensorflow, while showcasing my setup for a flexible…

As the use of analytics proliferate, companies will need to be able to identify models that are breaking bad.

While useful across an array of industries and for a variety of purposes, one use case stands out above the rest: anomaly detection for IT and DevOps teams.

David Lyon is an alum from the Insight Data Engineering program in Silicon Valley, now working at Facebook as a Data Engineer. In this…

Have you ever received the same advertising, for the same products, over multiple communication sources? Or maybe the same company sends you multiple offers, for different products, within only a few days of each other? Have you ever been flooded with information…

Despite big data currently ranking among top business intelligence and data analytics trends, businesses continue to suffer from a lack of data-savvy talent. Research from BARC shows half of respondents reporting a lack of analytical or technical know-how…

I believe that analytics is now at least four times more important than ever before. Here’s why.

In this post, Don Miner covers how to identify, evaluate, prioritize, and pick which data science problems to work on next. Don is a cofounder of Miner & Kasch, an artificial intelligence solutions and data science consulting firm. Don specializes in large…

The O’Reilly Data Show Podcast: Mark Hammond on applications of reinforcement learning to manufacturing and industrial automation.