ALL ARTICLES FOR Machine learning

Since it’s 2021, it’s probably no surprise to you that heart rate can be measured using different gadgets like smartphones or smartwatches.


For some reason, it is quite natural for people to argue with each other all the time. Wives argue with husbands. Children argue with their parents. Facebook users argue with other Facebook users. United fans argue with City fans. And it goes without saying that … Data Scientists argue with other Data Scientists!



Nowadays, no one needs to be convinced of the power and usefulness of deep neural networks. AI solutions based on neural networks have revolutionised almost every area of ​​technology, business, medicine, science and military applications. After the breakthrough win of Geoffrey Hinton's group in the ImageNet competition in 2012, neural networks have become the most popular machine learning algorithm. Since then, 21st century technology has come to increasingly rely on AI applications. We encounter AI solutions in almost every step of our daily lives - in cutting-age technologies, entertainment systems, business solutions, protective systems, the medical domain and many more areas. In many of these areas, AI solutions work in a way which is self-sufficient and under only little or no human supervision.


According to some sources, over 40% of all Internet traffic is made up of bot traffic. And we know that malicious bots are a significant proportion of current bot traffic. This article describes a number of strategies (Machine Learning, user authentication using simple input devices, and behavioral biometrics) which you can use to distinguish automatically between humans using the Internet (on the one hand), and bots (on the other hand).


The main focus in machine learning projects is to optimize metrics  like accuracy, precision, recall, etc. We put effort into hyper-parameter tuning or designing good data pre-processing. What if these efforts don’t seem to work? 

If I was to point out one most common mistake of a rookie Data Scientist, it’s their focus on the model, not on the data.

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