Natural Language Processing with Real World Projects

• Verfasst von: Ltd., Geekshub [VerfasserIn]
• Titel: Natural Language Processing with Real World Projects
• Institutionen: Safari, an O’Reilly Media Company.
• Verf.angabe: Ltd., Geekshub
• Ausgabe: 1st edition
• Verlagsort: [Erscheinungsort nicht ermittelbar]
• Verlag: Packt Publishing
• Jahr: 2019
• Umfang: 1 online resource (1 video file, approximately 14 hr., 56 min.)
• Fussnoten: Online resource; Title from title screen (viewed July 24, 2019)
• ISBN: 978-1-83898-048-1
• ComputerInfo: Mode of access: World Wide Web.
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Electronic videos ; local
• K10plus-PPN: 1694861813
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m3624811495
• Lokale URL Inst.: Zum Volltext

Abstract

Become a Pro in Natural Language Processing About This Video To know how Apple Siri / Google Assistant Works To know how Machine can understand Human language Help Google find bad data from good data In Detail You will learn how machine can be trained to make sense of language humans use to interact. You will come across many NLP algorithms that teach the computational models about Lexical processing, basic syntactic processing. You will learn the mechanism Google translator uses, to understand the context of language and converts to a different language. You will build a chat-bot using an open-source tool Rasa, which is a text and voice-based conversations, understand messages, hold conversations, and connect to messaging channels and APIs. You will also learn to train the model you have created on NLU. The machine cannot be trained to understand or process data by traditional hand coded programs that rely heavily on very specific conditions. The moment there is a change in input, the hand coded program is rendered useless. So, rather than having to code possible conversations, we require a model that enables the system to make sense of context. By the end of the course you will be able to build NLP models that can summarize blocks of text to extract most important ideas, sentiment analysis to extract the sentiments from given block of text, identification of type entity extracted. All the projects included in this course are Real-World projects.