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Figure 1: Data Science Job Market Trends | All images are from the author(s) unless stated otherwise.

Data Science, Editorial, News

Author(s): Sujan Shirol, Roberto Iriondo

Disclaimer: This article is only for educational purposes. We do not encourage anyone to scrape websites, especially those web properties that may have terms and conditions against such actions.

Are you preparing for a data science job interview in 2021? We have analyzed the hiring trends from more than 3000+ data science job postings across several online career portals. Hopefully, these insights will help you get ready for an interview by analyzing the expectations of employers and the overall market demand.

Data science and machine learning opportunities in the US are getting better every year…

Source: Image by the Author, created with Canva

Statistics, Editorial, Programming

Author(s): Pratik Shukla, Roberto Iriondo

Last updated on July 19, 2021

Data science and machine learning are scientific disciplines that are ruled by programming and mathematics. Nowadays, most corporations globally generate immense amounts of data that can be further analyzed and visualized by experts to understand trends and forecast predictions. For instance, we can only perform accurate data visualization if our data is clear and understandable.

However, organizations’ data is (frequently) too messy to tinker with — therefore, finding structures and important patterns in data is a crucial task for data science. Statistics provides the methods and tools to find…

An image showing Superb AI’s logo and the space behind it, Superb AI offers state-of-the-Art data Labeling with a true AI-powered data management platform.
An image showing Superb AI’s logo and the space behind it, Superb AI offers state-of-the-Art data Labeling with a true AI-powered data management platform.
This tutorial has been brought to you thanks to Superb AI, the AI-powered enterprise-grade training data platform. | Source: Derivative from original by NASA on Unsplash

Data Science, Machine Learning, Tutorial

Author(s): Roberto Iriondo

Data labeling is an essential part of the machine learning workflow, particularly data preprocessing, where both input and output data are labeled for classification to present a learning base for planned data processing.

We use data labeling to identify raw data, such as objects in images, videos, text, and so on. It works by affixing one or more significant and informative labels to produce context so that a model can learn from it [2]. …


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Applied natural language processing (Applied NLP) is becoming a very hot topic in the machine learning community, and for good reasons. It has emerged as an essential subdiscipline in the field of artificial intelligence (AI), and the main reason can be summed up in one sentence: We are drowning in unstructured data — texts, documents, articles, blog posts, emails, and more — and most of them are written in natural language. NLP’s objective is to build computer systems that can understand and derive meaning from human language…

Source: Photo by Annie Spratt on Unsplash

Artificial Intelligence, Editorial, News

Once you start exploring the field of artificial intelligence and machine learning, you will realize that there is a lot to learn. There is a variety of online groups and communities where experts share their insights on AI algorithms, problems in AI, ML, computer vision, and so on. These discussions lead to developing new ideas and solutions, which will eventually help the AI community grow more robust. This article will look at some actionable tips that help identify good online groups and communities.

If you are an enthusiast of artificial intelligence, then it is very likely that you might be…

Join Determined AI’s third lunch-and-learn session and learn how to scale the training of HuggingFace Transformers with Determined.


Training complex state-of-the-art natural language processing (NLP) models is now a breeze, thanks to HuggingFace — making it an essential open-source go-to for data scientists and machine learning engineers to implement Transformers models and configure them as state-of-the-art NLP models with straightforward library calls. As a result, the library has become crucial for training NLP models, like in Baidu or Alibaba, and has contributed to state-of-the-art results in several NLP tasks.

Our friends at Determined AI are hosting an exciting lunch-and-learn covering training HuggingFace Transformers at scale using Determined! …

News, Newsletter

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Game theory is a mathematical method modeled after the mechanics of game balancing and structure. It’s used to create interactions between multiple parties to help them achieve optimal results without extreme compromises or deviations from normality. It helped Google create an AI that learned how to master Starcraft II in just four hours, and its understanding of risk, strategy, and other critical variables makes it an invaluable tool when teaching AI systems to be flexible and adaptable thinkers. …

Source: Free image by Freepik, this cover has been designed using resources from

Data Science, Editorial, Machine Learning

Author(s): Saniya Parveez

Machine learning (ML) is the process of building models that learn from data. It has a diverse set of algorithms that are brought to life with data. However, a traditional classical ML approach can also solve problems with a minimal set of variables by using explicit rules. But, the trick is that it gets complicated when the number of variables increases. Regardless of how they are represented visually, machine learning models are mathematical functions and can consequently be implemented from scratch using numerical software frameworks and packages [1].

Source: Image by Jr Korpa on Unsplash

Natural Language Processing, Editorial, Programming

Author(s): Daksh Trehan, Roberto Iriondo

We live in a world that is becoming increasingly dependent on machines. Whether it is Siri, Alexa, or Google, they can all understand human language (mostly). But how do they do that? Today we will be exploring how some of the latest developments in NLP (Natural Language Processing) can make it easier for us to process and analyze text.

Can computers understand and respond to human language? One of the most fundamental questions in Computer Science. …


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The open-source AI community is seeing rapid growth. Open-source projects like TensorFlow, Theano, Caffe, BERT, MXNet, PyTorch, Gluon, and PyStanfordNLP are among the ones helping make AI accessible to everyone. To get you even more excited about scalable AI, check out this excellent and free-to-access event, presented by Anyscale:

[Free] Ray Summit June 22–24: The best way to scale AI

Ray Summit brings together developers, data scientists, architects, and product managers to build scalable AI using Ray, the dominant framework for distributed computing. Topics include top AI trends, ML in production, MLOps, reinforcement learning, cloud computing, serverless &…

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