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dask parallel computing in python

Dask – How to handle large dataframes in python using parallel computing

Dask provides efficient parallelization for data analytics in python. Dask Dataframes allows you to work with large datasets for both data manipulation and building ML models with only minimal code changes. It is open source and works well with python libraries like NumPy, scikit-learn, etc. Let’s understand how to use Dask with hands-on examples. Dask …

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Text Summarization Approaches

Text Summarization Approaches for NLP – Practical Guide with Generative Examples

Text summarization in NLP is the process of summarizing the information in large texts for quicker consumption. In this article, I will walk you through the traditional extractive as well as the advanced generative methods to implement Text Summarization in Python. Contents 1. Introduction 2. Types of Text Summarization 3. Text Summarization using Gensim 4. …

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Complete Guide to Natural Language Processing (NLP) – with Practical Examples

Natural language processing (NLP) is the technique by which computers understand the human language. NLP allows you to perform a wide range of tasks such as classification, summarization, text-generation, translation and more. NLP has advanced so much in recent times that AI can write its own movie scripts, create poetry, summarize text and answer questions …

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spacy custom text classification

SpaCy Text Classification – How to Train Text Classification Model in spaCy (Solved Example)?

Text Classification is the process categorizing texts into different groups. SpaCy makes custom text classification structured and convenient through the textcat component. Text classification is often used in situations like segregating movie reviews, hotel reviews, news data, primary topic of the text, classifying customer support emails based on complaint type etc. For many real-life cases, …

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101 NLP Exercises (using modern libraries)

I hope you found this useful. For more such posts, stay tuned to our page ! Desired Output: #> [(‘incredible’, 0.90), #> (‘awesome’, 0.82), #> (‘unbelievable’, 0.82), #> (‘fantastic’, 0.77), #> (‘phenomenal’, 0.76), #> (‘astounding’, 0.73), #> (‘wonderful’, 0.72), #> (‘unbelieveable’, 0.71), #> (‘remarkable’, 0.70), #> (‘marvelous’, 0.70)] Difficulty Level : L2 22. How to …

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Training Custom NER models in SpaCy to auto-detect named entities [Complete Guide]

Named-entity recognition (NER) is the process of automatically identifying the entities discussed in a text and classifying them into pre-defined categories. Categories could be entities like ‘person’, ‘organization’, ‘location’ and so on. The spaCy library allows you to train NER models by both updating an existing spacy model to suit the specific context of your …

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Machine Learning A-Z™: Hands-On Python & R In Data Science

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Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z™: Hands-On Python & R In Data Science