Author of the future

  • MLOps: DevOps applied to Machine Learning projects

    In a previous article, we presented the DevOps philosophy, and how this new approach enables faster value delivery for businesses through the unification of development (Dev) and operations (Ops) teams, which previously worked in silos. In this article, we’ll look at the application of this approach to Machine Learning problems: we’re talking about MLOps.

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  • SEO (Search Engine Optimisation): What is it? What is it used for?

    A successful business is very often a visible business. In the age of the Internet, this visibility is achieved through search engines such as Google, Bing and Yahoo. But competition there is fierce. To make your mark, it’s vital to adopt the right strategy, namely SEO. In this article, we’ll explain what SEO is all about, as well as the upheavals brought about by Artificial Intelligence.

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  • Microservices: Definition, operation, benefits

    Microservices architecture is an innovative approach to software development that has gained in popularity in recent years. This solution involves dividing an application into several small, simple and independent services.

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  • Quantitative Analyst: Discover the best courses

    A Quantitative Analyst course equips you with all the skills needed to become a Quantitative Analyst. Explore how to pursue training for this highly sought-after career in the finance sector. In the world of finance, luck has no place. Companies now turn to Quantitative Analysts: professionals capable of developing complex mathematical models to minimize risks, enhance security, and make investment decisions.

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  • Python Programming for Beginners – Episode 3

    Python Programming for Beginners – Table of Contents:

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  • Text Mining: What you didn’t know

    Text mining involves using Machine Learning to analyse text. Find out everything you need to know: definition, how it works, techniques, benefits, use cases, etc.

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  • How to become a Data Scientist? The the best-kept secrets!

    Regarded as the “sexiest job of the 21st century” by Harvard Business School, the role of a Data Scientist holds promising professional prospects. Have you ever wondered how to become a Data Scientist? We’re here to outline the essential skills you need to acquire for your training! To become a Data Scientist, it’s crucial to undergo specialized Data Science training that equips you to be effective and influential from the start. However, these training programs do require certain prerequisite skills and knowledge, such as mathematics and programming.

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  • Data Mining: Everything you need to know about data mining

    Data Mining, also known as data foraging, involves analyzing vast volumes of data to uncover trends and correlations. Discover everything you need to know about it: definition, operation, use cases, careers, and training…

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  • How do I create a Power BI Dashboard?

    Among Microsoft products and services, Power BI is one of the most powerful tools for professional use. Here’s how to create a dashboard. This means being able to read more easily an extract of the most important data from the report being produced.

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  • Folium: Discover the open source Python library

    Folium is one of the many open-source Python libraries that extend its capabilities. In the case of Folium, it’s a powerful visualization tool because it enables the creation of interactive maps.

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  • A/B Testing: Principles, steps, use

    AB testing is a statistical comparison method used to compare different variants of a basic version of a website page. The aim is to identify the most effective of these, according to the objectives defined. A/B testing can also be used to evaluate the performance of mobile applications and even conversion channels, including e-mail marketing or landing pages.

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  • Overfitting: what is it? How can I avoid it?

    In this article, we present one of the pitfalls of supervised learning algorithms: overfitting. What is overfitting? Overfitting in Machine Learning is the risk of a model learning the training data “by heart”. In this way, it runs the risk of not being able to generalize to unknown data. For example, a model that returns […]

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