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The Unintended Consequences of Machine Learning
But with great power comes great responsibility. Let me tell you a story about the unintended consequences of well-meaning machine learning research.
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How Feature Engineering Can Help You Do Well in a Kaggle Competition – Part I
As I scroll through the leaderboard page, I found my name in the 19th position, which was the top 2% from nearly 1,000 competitors. Not bad for the first Kaggle competition I had decided to put a real effort in!
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Your Checklist to Get Data Science Implemented in Production
For over a year we surveyed thousands of companies from all types of industries and data science advancement on how they managed to overcome these difficulties and analyzed the results. Here are the key things to keep in mind when you're working on your design-to-production pipeline.
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 6 Interesting Things You Can Do with Python on Facebook Data
Facebook has a huge amount of data that is available for you to explore, you can do many things with this data. I will be sharing my experience with you on how you can use the Facebook Graph API for analysis with Python.
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K-means Clustering with R: Call Detail Record Analysis
Call Detail Record (CDR) is the information captured by the telecom companies during Call, SMS, and Internet activity of a customer. This information provides greater insights about the customer’s needs when used with customer demographics.
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TPOT Automated Machine Learning Competition: Can AutoML beat humans on Kaggle?
Over the next couple months, we’re going to challenge you to apply TPOT to any data science problem you find interesting on Kaggle. If your entry ranks in the top 25% of the leaderboard on a Kaggle problem, we want to see how TPOT helped you accomplish that.
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 Deep Learning 101: Demystifying Tensors
Many deep-learning systems available today are based on tensor algebra, but tensor algebra isn’t tied to deep-learning. It isn’t hard to get started with tensor abuse but can be hard to stop.
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Why Does Deep Learning Not Have a Local Minimum?
"As I understand, the chance of having a derivative zero in each of the thousands of direction is low. Is there some other reason besides this?"
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 Which Machine Learning Algorithm Should I Use?
A typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is "which algorithm should I use?” The answer to the question varies depending on many factors, including the size, quality, and nature of data, the available computational time, and more.
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The Artificial ‘Artificial Intelligence’ Bubble and the Future of Cybersecurity
What’s going on now in the field of ‘AI’ resembles a soap bubble. And we all know what happens to soap bubbles eventually if they keep getting blown up by the circus clowns (no pun intended!): they burst.
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6 Interesting Things You Can Do with Python on Facebook Data
Deep Learning 101: Demystifying Tensors
Which Machine Learning Algorithm Should I Use?