The Bias-Variance trade-off

Machine Learning models’ ultimate goal is making reliable predictions on new, unknown data. With this purpose in mind, we want our algorithm to capture relations in existing data and replicate them among new entries. At the same time, we do not want our algorithm to have, let’s say, prejudices because of the data it trained …

Streaming analysis with Kafka, InfluxDB and Grafana

If you are dealing with the streaming analysis of your data, there are some tools which can offer performing and easy-to-interpret results. First, we have Kafka, which is a distributed streaming platform which allows its users to send and receive live messages containing a bunch of data (you can read more about it here). We will …

Computer Vision: Feature Matching with OpenCV

Computer vision is a field of study which aims at gaining a deep understanding from digital images or videos. Combined with AI and ML techniques, today many industries are investing in researches and solutions of computer vision. Namely, think about the security procedures in the Airport: when you have to exhibit your passport, it is …

Twitter sentiment analysis with Tweepy

The world of social networks could be considered, today, one of the largest free data source available in the market. When you think about Big Data, probably the first example that comes to your mind is Twitter. Like many other social networks, Twitter allows its users to post, comment, like and follow, to express their …

Unsupervised Learning: PCA and K-means

Machine Learning algorithms can be categorized mainly into two bunches: supervised learning: we are provided with data which are already labeled, hence our aim will be finding, once provided with a new observation, its category (in case of a classification task) or its numerical value (in case of a regression task);unsupervised learning: in this case, …

Building your first chatbot with Python

Today, if you are about to order some foods on a restaurant’s website or you need assistance because your router is not working properly, you will probably get in touch with a chatbot. They appear to you like instant messaging chats, in one of the corners of the screen, and gently ask you whether you …

Deep learning for image recognition: Convolutional Neural Network with Tensorflow

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 …

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 …