Data pre-processing is an important part of every machine learning project. A very useful transformation to be applied to data is normalization. Some models require it as mandatory to work properly. Let’s see some of them.
Model explanation is an essential task in supervised machine learning. Explaining how a model can represent the information is crucial to understanding the dynamics that rule our data. Let’s see some models that are easy to interpret.
Online marketing and startup growth are better if you can continuously test different ideas. The statistic comes into help when we have to perform A/B tests. The results you may achieve with the proper analysis can give your project a great boost.
Data scientists usually split a dataset into training and test sets. Their model is trained on the former and then its performance is checked in the latter. But, if these sets are sampled wrongly, model performance may be affected by biases.
Measuring the predictive power of some feature in a supervised machine learning problem is always a hard task to accomplish. Before using any correlation metrics, it’s important to visualize wether a feature is informative or not. In this article, we’re going to apply data visualization to a classification problem.
Outliers are a great problem for a data scientist. They are “strange points” in a dataset that must be checked in order to verify whether they are errors or real phenomena.
Data Science mixes different skills and there are some old skills that are still useful. One of such skills is SQL.
We’ve seen strong growth of Deep Learning techniques in the past few years. Thanks to technologies like Tensorflow and Keras, neural networks have become accessible by anybody in the world. But is Deep Learning really useful for you?
I have a Master’s Degree cum laude in Theoretical Physics. When I started my journey into data science, I figured out how useful it is for this kind of job.