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A Solution to Missing Data: Imputation Using R
Handling missing values is one of the worst nightmares a data analyst dreams of. In situations, a wise analyst ‘imputes’ the missing values instead of dropping them from the data.
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Evaluating Data Science Projects: A Case Study Critique
It’s not necessary to understand the inner workings of a machine learning project, but you should understand whether the right things have been measured and whether the results are suited to the business problem. You need to know whether to believe what data scientists are telling you.
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Machine Learning Translation and the Google Translate Algorithm
Today, we’ve decided to explore machine translators and explain how the Google Translate algorithm works.
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K-Nearest Neighbors – the Laziest Machine Learning Technique
K-Nearest Neighbors (K-NN) is one of the simplest machine learning algorithms. When a new situation occurs, it scans through all past experiences and looks up the k closest experiences. Those experiences (or: data points) are what we call the k nearest neighbors.
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Top 10 Machine Learning Use Cases: Part 2
This post is the second in a series whose aim is to shake up our intuitions about what machine learning is making possible in specific sectors — to look beyond the set of use cases that always come to mind.
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How Booking.com’s data scientist uses predictive analytics – PAW interview
Lukas Vermeer will speak at the upcoming Predictive Analytics World for Business, 11-12 October in London. His Keynote will focus on new perspectives on the Data Science challenges we face today.
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 Putting the “Science” Back in Data Science
The scientific method to approach a problem, in my point of view, is the best way to tackle a problem and offer the best solution. If you start your data analysis by simply stating hypotheses and applying Machine Learning algorithms, this is the wrong way.
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Visualizing Cross-validation Code
Cross-validation helps to improve your prediction using the K-Fold strategy. What is K-Fold you asked? Check out this post for a visualized explanation.
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Detecting Facial Features Using Deep Learning
A challenging task in the past was detection of faces and their features like eyes, nose, mouth and even deriving emotions from their shapes. This task can be now “magically” solved by deep learning and any talented teenager can do it in a few hours.
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Next Generation Data Manipulation with R and dplyr
The idea behind the dplyr package is to do one thing at a time. dplyr has separate functions for every task which make its implementation crisp and easy to understand.
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Putting the “Science” Back in Data Science