Professional data scientists know that data must be prepared before feeding any model with it. Data pre-processing is probably the most important part of a machine learning pipeline and its importance is sometimes underestimated.
The bootstrap. The Swiss army knife of any data scientist
Every measure must be followed by an error estimate. There’s no chance to avoid this. If I tell you “I’m 1,93 …
The most used probability distributions in Data Science
Statistics is a must-have skill in a Data Scientist’s CV, so there are concepts and topics that must be known in advance if somebody wants to work with data and machine learning models. Probability distributions are a must-have tool. Let’s see the most important ones to know for a Data Scientist.
How to build your own AutoML library in Python from scratch
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.
An example of overfitting and how to avoid it
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.
Visualize multidimensional datasets with MDS
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?
What is Data Science?
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?
Why Artificial Intelligence is not a technological revolution
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?
Will AutoML software replace Data Scientists?
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?
An introduction to linear models
Linear models are some of the simplest models in machine learning. They are very powerful and, sometimes, they are really …