Benjamin Bengfort is a computer scientist who specializes in distributed systems, machine learning, and other techniques. Rebecca Bilbro is a data scientist and Python programmer whose work explores visual diagnostics for the machine learning workflow. Tony Ojeda is the founder and CEO of District Data Labs, where he focuses on applied analytics for business strategy, optimization, forecasting, and curricula using open source tools.
Applied Text Analysis with Python. Enabling Language Aware Data Products with Machine Learning
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- Livraison à domicile ou en point Mondial Relay estimée à partir du 28 septembreCet article sera commandé chez un fournisseur et vous sera envoyé 25 jours après la date de votre commande.
- Retrait Click and Collect en magasin gratuit
- Nombre de pages310
- FormatGrand Format
- PrésentationBroché
- Poids0.6 kg
- Dimensions17,7 cm × 23,3 cm × 2,5 cm
- ISBN978-1-4919-6304-3
- EAN9781491963043
- Date de parution01/08/2018
- ÉditeurO'Reilly
Résumé
From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context ; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientists approach to building language-aware products with applied machine learning.
You'll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you'll be equipped with practical methods to solve any number of complex real-world problems. Preprocess and vectorize text into high-dimensional feature representations.
Perform document classification and topic modeling. Steer the model selection process with visual diagnostics. Extract key phrases, named entities, and graph structures to reason about data in text. Build a dialog framework to enable chatbots and language-driven interaction. Use Spark to scale processing power, and neural networks to scale model complexity.
You'll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you'll be equipped with practical methods to solve any number of complex real-world problems. Preprocess and vectorize text into high-dimensional feature representations.
Perform document classification and topic modeling. Steer the model selection process with visual diagnostics. Extract key phrases, named entities, and graph structures to reason about data in text. Build a dialog framework to enable chatbots and language-driven interaction. Use Spark to scale processing power, and neural networks to scale model complexity.