Data Science at Home

Artificial Intelligence allow machines to learn patterns from data. The way humans learn however is different and more efficient. With Lifelong Machine Learning, machines can learn the way human beings do, faster, and more efficiently

Talking about security of communication and privacy is never enough, especially when political instabilities are driving leaders towards decisions that will affect people on a global scale

We strongly believe 2017 will be a very interesting year for data science and artificial intelligence. Let me tell you what I expect and why.

Is the market really predictable? How do stock prices increase? What is their dynamics? Here is what I think about the magics and the reality of predictions applied to markets and the stock exchange.

Why the job of the data scientist can disappear soon. What is required by a data scientist to survive inflation.

Data science is making the difference also in fraud detection. In this episode I have a conversation with an expert in the field, Engineer Eyad Sibai, who works at iZettle, a fraud detection company

Extracting knowledge from large datasets with large number of variables is always tricky. Dimensionality reduction helps in analyzing high dimensional data, still maintaining most of the information hidden behind complexity. Here are some methods that you must try before further analysis (Part 1).

How would you perform accurate classification on a very large dataset by just looking at a sample of it

What is deep learning?If you have no patience, deep learning is the result of training many layers of non-linear processing units for feature extraction and data transformation e.g. from pixel, to edges, to shapes, to object classification, to scene description, captioning, etc.

At some point, statistical problems need sampling. Sampling consists in generating observations from a specific distribution.

There are statisticians and data scientists... Among statisticians, there are some who just count. Some others who… think differently. In this show we explore the old time dilemma between frequentists and bayesians.Given a statistical problem, who’s going to be right?

In this show I interview Sebastian Raschka, data scientist and author of Python Machine Learning.In addition to the fun we had offline, there are great elements about machine learning, data science, current and future trends, to keep an ear on. Moreover, it is the conversation of two data scientists who contribute and operate in the field, on a daily basis.

In this episode, we tell you how to become data scientist and join an amazing community that is changing the world with data analytics.

Should data scientists follow the old good practices of software engineering? Data scientists make software after all.

It’s time to experiment with Data Science at home. Since we are still dealing with our hosting service, consider the first episode purely experimental, even though the content might be of your interest, no matter what.