This event has been UPDATED since it was first published. View the UPDATED & Detailed Natural Language Processing(NLP) Training course for beginners Information here.

16 Hours Natural Language Processing (NLP)Training is being delivered from April 27, 2021 - May 20, 2021 US Pacific Time over 4 weeks, 8 sessions, 2 sessions per week, 2 hours per session.

• Instructor-led and guided training
• Practical Hands-On, Highly Interactive training
• This course will be taught in English language
• All Published Ticket Prices are in US Dollars

16 Hours Natural Language Processing (NLP) Training Course Schedule

• April 27, 2021 - May 20, 2021 US Pacific time
• 4 Weeks | 2 Hours on Tuesdays, 2 Hours on Thursdays every week US Pacific time
• 8:30 AM - 10:30 AM US Pacific time each of those days
• Please click here to add your city name and check your local date and time for the first session to be held on April 27, 2021 at 8:30 AM US Pacific Time.

Features and Benefits

• 4 Weeks, 8 sessions, 16 hours of total Instructor-led and guided training
• Training material, instructor handouts and access to useful resources on the cloud provided
• Practical Hands-on Lab exercises provided
• Real-life Scenarios

Course Objectives

This course provides introduction to the field of Natural Language Processing. It includes relevant background material in Linguistics, Mathematics, Probabilities, and Computer Science. Some of the topics covered in the class are Text Similarity, Part of Speech Tagging, Parsing, Semantics, Question Answering, Sentiment Analysis, and Text Summarization.

Who can take this course

Anyone who wants to learn natural language processing for any purpose.

Prerequisites

• Data Structures
• Probability
• Statistics

Course Outline

• Introduction
• NLP Tasks and Text Similarity
• Syntax and Parsing
• Formal language
• Language Modeling and Word Sense Disambiguation
• Part of Speech Tagging and Information Extraction
• Question Answering
• Text Summarization
• Collocations and Information Retrieval
• Sentiment Analysis and Semantics
• Discourse, Machine Translation, and Generation
• Statistical methods
• hidden Markov models
• language understanding
• conversational agents