Deep learning is a subset of Machine Learning (that is, again, a subset of Artificial Intelligence) whose algorithms are based on the layers used in artificial neural networks. It has a variety of applications, among which image recognition, that is what we are going to discuss in this article. To show how to build, train [...]
Apache Kafka: platform architecture and streaming analysis
Kafka is a distributed streaming platform which allows its users to send and receive live messages containing a bunch of data. This article will dwell on the architecture of Kafka, which is pivotal to understand how to properly set your streaming analysis environment. Later on, I will provide an example of real-time data analysis by [...]
Natural Language Processing with TextBlob
Natural Language Processing (NPL) is a field of Artificial Intelligence whose purpose is finding computational methods to interpret human language as it is spoken or written. The idea of NLP goes beyond a mere classification task which could be carried on by ML algorithms or Deep Learning NNs. Indeed, NLP is about interpretation: you want to [...]
Mapping and building machine learning algorithms on geodata with R
Sometimes the very representation method of data, by itself, can provide a huge amount of information and might direct you towards a good analysis. In this article, I will dwell on some interesting plotting methods, provided by R, which are pivotal if you are facing geodata. I will use the famous NYC Taxi Dataset, which [...]
How to set and deploy your machine learning experiment with R
The aim of this article is providing a foretaste of the potentiality of machine learning algorithms using R, following step-by-step a standard procedure that, once got familiar, could be a good starting point to design customized models. The idea behind each model, indeed, is the same. In a nutshell, it consists of finding an algorithm [...]
