At the end of the article, you will know how to structure a trading problem in a probabilistic way. Also, you will learn how to improve the feature engineering process by listening to the experts. In the end, we will train a Random Forest with real data to apply these concepts!. (Bonus track: Cool visualizations).

0) Our Motivation

This is the story: we are investors, and we have, let’s say, 1,199 USD$ (soon you will understand why I’m using 1,199 and not 1,000), and our goal is to make the most of them (could we want something else?). We decided to go into the crypto market because of its volatility. To maintain our partnership, we need to make consistent profits. We want to use machine learning without forgetting about the business understanding used by traders. Now, the only thing we need to know is HOW (Wow… I’m definitively using that in my next freestyle competition). Will we accomplish…

Because data also have something to say…

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Photo by Maxim Hopman on Unsplash

I doubt very much that by today you still don’t know anything about what happened the last few days with GameStop. But, let’s say you don’t. That’s no problem, besides some headlines, I didn’t either. Like any other disinformed person, I decided to read some articles to understand what was going on. After further investigation, I was shocked by the phenomenon’s name. A new term has arisen: “Meme Stock”.

I don’t want to go into very much detail about what happened, because there is a lot of information out there, and you will find better explanations from other great and…


Data scientist is not one…

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Photo by Yoal Desurmont on Unsplash

The irruption of cloud computing accelerated the adoption of artificial intelligence. Expectations were created and companies thought that hiring data scientists and giving them a bunch of data, was enough to solve every problem inside the company. But, like with every beginning field, time is needed before value is properly addressed. And for the data science teams, it wasn’t any different.

Happily for us, the scenario is changing quickly. Every year there’s more knowledge and experience about what “data science” actually means. …

Because you don’t learn it until you build it

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Photo by Nina PhotoLab on Unsplash

What about starting with a quote?.

“In the past, I’ve tried to teach machine learning using […] different programming languages […], and what I found is that students were able to learn the most productively […] using a relatively high level language like Octave.”, Andrew NG.

Building something from scratch was the method used by Andrew NG to teach his famous Coursera’s machine learning course (in plain Octave 😂), with one of the greatest ratings on the platform. Like Andrew, I truly believe that building things is the best way to learn because it forces us to understand every step…

A Step by Step Guide

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Photo by Omar Ram on Unsplash

If you always wanted to learn decision trees, just by reading this, you’ve received a beautiful opportunity, a stroke of luck, I might say. But as entrepreneurs declare, “it’s not enough to be in the right place; you also need to take the opportunity when it comes”. So, consider giving yourself some time and get comfortable, because what’s coming it’s not the shortest guide, but it might be the best.

Decision trees are the foundation of the most well performing algorithms on Kaggle competitions and real-life. An indicator of this is that you are certainly going to collide with a…

Hands-on Tutorials

Manage infrastructure, train your models & deploy them.

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Photo by Callum Wale on Unsplash

If it sounds like the dream for you, let me tell you that it sounded like the dream for product developers too. The result: Azure ML SDK.

Today we will be exploring how to deal with infrastructure, environments, and deployments in the cloud. We will dig deeper into concepts and the underlying structures required for you to master these skills.

I know that concepts by themselves might feel like castles in the air. But don’t worry, there will be enough code for you to feel that the castles are actually on the ground.

Context, please

If we talk about cloud providers, there…

Without even knowing how to create Dockerfiles

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Image by Author.


A few years ago, Docker started gaining popularity. Everyone claimed that this tool was saving them incredible amounts of time. The only thing that you needed for solving the messy process of building, deploying, and managing apps was Docker. It was like heaven.

But… such a wonderful thing could be real? Well, it turns out that everything was true. A little part of paradise might mistakenly have fallen on earth?. …

After you read this, you will understand how Azure ML deals with data, how MLOps is related, and learn how to do it by yourself

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Photo by Scott Graham on Unsplash

One of the most significant concerns in this data science era is operationalizing artificial intelligence’s full lifecycle. As you might know, the foundation for machine learning is data. If you want to be sure that your project has full traceability, you can’t forget about the less sexy component.

This article aims to visit why data versioning is essential and how to do it on Azure Machine Learning.

Just a glimpse on MLOps

But first, let’s dive a little bit into what MLOps actually is. An excellent way to understand this is by looking at the infographic created by Microsoft. …

After you read this, you will have a more profound understanding about cloud certifications and the DP-100

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Flaticon credits

0.a) Is this certification worth it?

Before you start judging me and worrying thinking that I might be suffering from the “confirmation bias syndrome” (I took the exam and pass, so I could be tempted to find every single argument to say that this is the best certificate ever), I want to tell you that you don’t need to!

Here you won’t encounter statements like “study hard, become the best, like me!” or “It was incredibly hard, only experienced data scientist will pass.” …

Using Plotly’s line_polar will show us an exciting approach for clustering interpretation. Also, we will see that algorithms are just a little part of the process of the business value that can be obtained from clustering.

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Photo by Aditya Chinchure on Unsplash

Our Case

Imagine that you get a great job as the head of the data science team in a new E-commerce mainly focused on selling men’s clothes. You noticed that the questions were heavily focused on clustering in the interview process, and now you get why: the CEO of the company it’s pressuring the marketing area to elaborate campaigns targetting the most representative groups. The marketing team knows that you are the best fit to help them! So they set up a meeting to figure this out, after a short introduction of the situation, they quickly got to the point. “We are…

Mauricio Letelier

Solving Business Problems | Quant Finance | Azure Data Scientist Associate |

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