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AI for Business

4 Business AI Predictions for 2022-2023
AI as a field, especially in the context of real-world applications, has been progressing at a rapid pace. This has been further accelerated by the onset of the COVID-19 pandemic. In fact, AI was found to be…

Will AI Kill Your Job?
Will AI *really* steal our jobs? This article performs some reality checks on this topic.
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AI in Manufacturing: 4 Real-World Examples
Explore 4 real-world AI in manufacturing examples. From defect detection to predictive maintenance.
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How To Generate Quality Training Data For Your Machine Learning Projects
Learn 5 strategies for generating high-quality machine learning training data. Warning: some of these approaches have been tried and tested.
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3 Painful Mistakes Leaders Can Avoid When Buying AI Solutions
Buying AI solutions requires the right strategy. In this article, we explore 3 mistakes to avoid when buying AI.
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7 Natural Language Processing Applications for Business Problems
Did you know, that several years ago, NLP was heavily an academic discipline? Today, natural language processing or NLP has become critical to business applications. This can partly be attributed to the growth of big data, consisting…
Technical Deep Dive for Practitioners

A Gentle Introduction to Deep Neural Networks with Python
This article examines the parts that make up neural networks and deep neural networks, as well as the fundamental different types of models (e.g. regression), their constituent parts (and how they contribute to model accuracy), and which…

FastText vs. Word2vec: A Quick Comparison
One of the questions that often comes up is what’s the difference between fastText and Word2Vec? Aren’t they both the same? Yes and no. They are conceptually the same, but there is a minor difference—fastText operates at a character…

Word2Vec: A Comparison Between CBOW, SkipGram & SkipGramSI
Word2Vec is a widely used word representation technique that uses neural networks under the hood. The resulting word representation or embeddings can be used to infer semantic similarity between words and phrases, expand queries, surface related concepts…

HashingVectorizer vs. CountVectorizer
Previously, we learned how to use CountVectorizer for text processing. In place of CountVectorizer, you also have the option of using HashingVectorizer. In this tutorial, we will learn how HashingVectorizer differs from CountVectorizer and when to use…

10+ Examples for Using CountVectorizer
Scikit-learn’s CountVectorizer is used to transform a corpora of text to a vector of term / token counts. It also provides the capability to preprocess your text data prior to generating the vector representation making it a…

Build Your First Text Classifier in Python with Logistic Regression
Text classification is the automatic process of predicting one or more categories given a piece of text. For example, predicting if an email is legit or spammy. Thanks to Gmail’s spam classifier, I don’t see or hear…