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3 Reasons Why AutoML Won’t Replace Data Scientists Yet
We dispel the myth that AutoML is replacing Data Scientists jobs by highlighting three factors in Data Science development that AutoML can’t solve.
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On Building Effective Data Science Teams
We take a look at the qualities that make a successful data team in order to help business leaders and executives create better AI strategies.
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 Asking Great Questions as a Data Scientist
We outline the importance of asking yourself the questions you need to ask to effectively produce something that the business wants. Once you start asking questions, it’ll become second nature and you’ll immediately see the value and find yourself asking even more questions as you gain more experience.
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Word Embeddings in NLP and its Applications
Word embeddings such as Word2Vec is a key AI method that bridges the human understanding of language to that of a machine and is essential to solving many NLP problems. Here we discuss applications of Word2Vec to Survey responses, comment analysis, recommendation engines, and more.
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PDF Data Extraction: What You Need to Know
In our free guide, we show you how and where you can use extracted data from PDFs, and explain the necessary qualities you should be looking for when evaluating extraction tools.
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Automatic Machine Learning is broken
We take a look at the arguments against implementing a machine learning solution, and the occasions when the problems faced are not ML problems and can perhaps be solved using optimization, exploratory data analysis tasks or problems that can be solved with simple statistics.
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Deep Multi-Task Learning – 3 Lessons Learned
We share specific points to consider when implementing multi-task learning in a Neural Network (NN) and present TensorFlow solutions to these issues.
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The Analytics Engineer – new role in the data team
In a constantly changing landscape and with many companies, the roles and responsibilities of data engineers, analysts, and data scientists are changing, forcing the introduction of a new role: The Analytics Engineer.
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How AI can help solve some of humanity’s greatest challenges – and why we might fail
AI represents a step change in humanity’s ability to rise to its greatest challenges. We explore three areas in which AI can contribute to the UN’s Global Goals - and why we could fall short.
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A Quick Guide to Feature Engineering
Feature engineering plays a key role in machine learning, data mining, and data analytics. This article provides a general definition for feature engineering, together with an overview of the major issues, approaches, and challenges of the field.
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Asking Great Questions as a Data Scientist