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 this? …


Opinion — As a Data Scientist —

Could we have expected something different?

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Photo by Nik Shuliahin on Unsplash

MLOps, Deep Learning, Machine Learning, AutoML, Advanced Analytics, and so on. If you have heard these ones in the last months, you’re certainly part of the buzzword phenomenon.

A couple of days ago, I saw the following post, and it really made me ponder. What’s the role that buzzwords have been playing, and how will they affect our data ecosystem?


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 are three leading players: AWS, GCP, and Azure. When it comes to cloud computing, it’s not rocket science to figure out that one of their main customers are data scientists. Maybe not directly them, but, surprise! Companies are — still — made up of people. …


Without even knowing how to create Dockerfiles

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Image by Author.

Docker

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 experts defining well-suited campaigns when we know the people we are trying to reach, but we need to first identify the most representative group of clients with a description of their behavior. …


Is our target static?

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Photo by Clem Onojeghuo on Unsplash

0.a) Motivation and … Darts (?)

Yup, you read correctly, Optimizing the Target Variable. You might be thinking, “but, that’s like cheating” or “can you change the past?”. Stop worrying! I’m neither a liar nor a time traveler who attempts to change the past just to improve his machine learning models (There are plenty of more exciting things to do with that power).

If you went for your first dataset, learning about Titanic, where you tried everything to predict if the passenger survived, or the MNIST database, where there were numbers that even you couldn’t recognize, you might be a little confused. …


At the end of the article, you will understand how to grab a business concept from scratch and materialize it into a variable for a machine learning model with an applied case.

Data Science Project: Cryptocurrencies Part 1—Motivation —

Data Science Project: Cryptocurrencies Part 2 — Volume and Data Source —

Data Science Project: Cryptocurrencies Part 3 — Becoming a Trader Data Scientist —

Intro

Today I’m going to introduce you to one of the variables that we will be using in our models. I will describe to you the whole process from retrieving a trading concept to, in the end, create an insightful variable. You will understand how to transform your business insights into real machine learning material.

Our data

The horizon that I would like to evaluate is 5-minute data aggregation. This means that I will collect data for 5 minutes intervals, with the standard OHLCV information. I met a little problem with Cryptocompare. I just find out that for gathering more than a week for minute data, we need an enterprise account. …


We will be exploring and understanding concepts from both worlds, data science, and trading, obtaining our first approach to mix them in one.

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Becoming a Trader Data Scientist: Transforming Bollinger Bands (Part 4)

Coinmonks

Hi, darlings, last week I was reached by Gaurav Agrawal, who works as an editor on Coinmonks. He invited me to publish there, adding me as a writer, so you can find my content there. Coinmonks describe itself as “A Non-profit Crypto Educational Publication,” and they’re pretty much that; there are a lot of great publishers and thousands of interesting articles. The link embedded earlier is for you to check them out (the fact that I’ve been invited has no relation to this invitation). …

About

Mauricio Letelier

Machine Learning Consultant | Solving Business Problems | Finance | https://www.linkedin.com/in/maletelier/ | Azure Data Scientist Associate

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