Gianluca Malato

AutoML libraries and services have already entered the world of machine learning. They are very useful tools for a Data Scientist, but sometimes they must be adapted to fit the needs of the business context a Data Scientist work in. That’s why you would need to build your own AutoML library.

Overfitting is a tremendous enemy for a data scientist trying to train a supervised model. It will affect performances in a dramatic way and the results can be very dangerous in a production environment.

Data visualization is one of the most fascinating fields in Data Science. Sometimes, using a good plot or graphical representation can make us better understand the information hidden inside data. How can we do it with more than 2 dimensions?

Data Science is considered as one of the most modern and fascinating jobs of our time. It can be funny and can give you satisfaction, but is it really as it’s described?

Now we are facing this new character in the stage of evolution, that is Artificial Intelligence. Where do we have to put this card in the puzzle of human history?

In the last years, a lot of automated machine learning pieces of software have been introduced. They can automate some tasks that a Data Scientist has usually to perform manually. They have reached a very remarkable level of complexity and effectiveness. Are they a threat to Data Scientist’s job or are they an opportunity?

Linear models are some of the simplest models in machine learning. They are very powerful and, sometimes, they are really …

Data is everything and models are, sometimes, lazy in front of data they don’t understand. That’s why we need to …

I’ve recently launched my online course about Supervised Machine Learning in Python. In this post, I’ll explain to you 5 …