- Main
- Computers - Computer Science
- Applied Text Analysis with Python:...
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning
Benjamin Bengfort, Tony Ojeda, Rebecca BilbroQuanto ti piace questo libro?
Qual è la qualità del file?
Scarica il libro per la valutazione della qualità
Qual è la qualità dei file scaricati?
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 scientist’s 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
Categorie:
Anno:
2018
Edizione:
1
Casa editrice:
O’Reilly Media
Lingua:
english
Pagine:
332
ISBN 10:
1491963042
ISBN 13:
9781491963043
File:
PDF, 13.97 MB
I tuoi tag:
IPFS:
CID , CID Blake2b
english, 2018
Leggi Online
- Scaricare
- pdf 13.97 MB Current page
- Checking other formats...
- Convertire a
- Sbloccare file di conversione di dimensioni maggiori di 8 MB Premium
Il file verrà inviato al tuo indirizzo email. Ci vogliono fino a 1-5 minuti prima di riceverlo.
Entro 1-5 minuti il file verrà consegnato al tuo account Telegram.
Attenzione: assicurati di aver collegato il tuo account al bot Z-Library Telegram.
Entro 1-5 minuti il file verrà consegnato al tuo dispositivo Kindle.
Nota: devi verificare ogni libro che desideri inviare al tuo Kindle. Controlla la tua casella di posta per l'e-mail di verifica da Amazon Kindle Support.
La conversione in è in corso
La conversione in non è riuscita
Vantaggi dello status Premium
- Inviare a lettori di e-book
- Limite aumentato di download
- Converti i file
- Più risultati di ricerca
- Altri vantaggi