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Technical Deep Dive
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…
Easily Access Pre-trained Word Embeddings with Gensim
What are pre-trained embeddings and why? Pre-trained word embeddings are vector representation of words trained on a large dataset. With pre-trained embeddings, you will essentially be using the weights and vocabulary from the end result of the…
How to Use Tfidftransformer & Tfidfvectorizer?
Scikit-learn’s Tfidftransformer and Tfidfvectorizer aim to do the same thing, which is to convert a collection of raw documents to a matrix of TF-IDF features. The differences between the two modules can be quite confusing and it’s…
The Business Side of AI
3 Painful Mistakes Leaders Can Avoid When Buying AI Solutions
85% of global executives believe that AI can become their competitive advantage. So, the rush to AI adoption is understandable. Unfortunately, implementing AI from scratch takes time,…
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…
Machine Learning Consulting Rates
One thing that is top of mind for companies looking to implement machine learning and data science solutions is cost. You want solutions and strategies that deliver…
How to Prevent Machine Learning Models from Failing in Practice?
Have you seen machine learning solutions fall flat in practice? Well, I have. Several times. I get occasional panic calls from teams about their 98% accurate models…
5 Examples of Text Classification in Practice
AI is transforming nearly every industry, and text analysis is a key area of interest. That’s because there’s been an explosion in unstructured text data—nearly 80% of…
Before AI, Invest in A Big Data Strategy
Big data describes the volumes of data that your company generates, every single day. Both structured and unstructured. Analysts at Gartner estimate that more than 80 percent of enterprise data is unstructured….