Natural Language Processing (NLP) Using NLTK in Python

Natural Language Processing (NLP) Using NLTK in Python, available at $39.99, has an average rating of 3.9, with 54 lectures, 2 quizzes, based on 5 reviews, and has 44 subscribers.

You will learn about Attain a strong foundation in Python for deep learning and NLP Build applications with Python, using the Natural Language Toolkit via NLP Get to grips on various NLP techniques to build an intelligent Chatbot Classify text and speech using the Naive Bayes Algorithm Use various tools and algorithms to build real-world applications Build solutions such as text similarity, summarization, sentiment analysis and anaphora resolution to get up to speed with new trends in NLP Write your own POS taggers and grammars so that any syntactic analyses can be performed easily Use the inbuilt chunker and create your own chunker to evaluate trained models Create your own named entities using dictionaries to use inbuilt text classification algorithms This course is ideal for individuals who are This course is for data science professionals who would like to expand their knowledge from traditional NLP techniques to state-of-the-art techniques in the application of NLP. It is particularly useful for This course is for data science professionals who would like to expand their knowledge from traditional NLP techniques to state-of-the-art techniques in the application of NLP.

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Summary

Title: Natural Language Processing (NLP) Using NLTK in Python

Price: $39.99

Average Rating: 3.9

Number of Lectures: 54

Number of Quizzes: 2

Number of Published Lectures: 54

Number of Published Quizzes: 2

Number of Curriculum Items: 56

Number of Published Curriculum Objects: 56

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Attain a strong foundation in Python for deep learning and NLP
Build applications with Python, using the Natural Language Toolkit via NLP
Get to grips on various NLP techniques to build an intelligent Chatbot
Classify text and speech using the Naive Bayes Algorithm
Use various tools and algorithms to build real-world applications
Build solutions such as text similarity, summarization, sentiment analysis and anaphora resolution to get up to speed with new trends in NLP
Write your own POS taggers and grammars so that any syntactic analyses can be performed easily
Use the inbuilt chunker and create your own chunker to evaluate trained models
Create your own named entities using dictionaries to use inbuilt text classification algorithms

Who Should Attend

This course is for data science professionals who would like to expand their knowledge from traditional NLP techniques to state-of-the-art techniques in the application of NLP.

Target Audiences

This course is for data science professionals who would like to expand their knowledge from traditional NLP techniques to state-of-the-art techniques in the application of NLP.

Natural Language Processing (NLP) is the most interesting subfield of data science. It offers powerful ways to interpret and act on spoken and written language. It’s used to help deal with customer support enquiries, analyse how customers feel about a product, and provide intuitive user interfaces. If you wish to build high performing day-to-day apps by leveraging NLP, then go for this course.

This course teaches you to write applications using one of the popular data science concepts, NLP. You will begin with learning various concepts of natural language understanding, Natural Language Processing, and syntactic analysis. You will learn how to implement text classification, identify parts of speech, tag words, and more. You will also learn how to analyze sentence structures and master syntactic and semantic analysis. You will learn all of these through practical demonstrations, clear explanations, and interesting real-world examples. This course will give you a versatile range of NLP skills, which you will put to work in your own applications.

Contents and Overview

This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Natural Language Processing in Practice, will help you gain NLP skills by practical demonstrations, clear explanations, and interesting real-world examples. It will give you a versatile range of deep learning and NLP skills that you can put to work in your own applications.

The second course, Developing NLP Applications Using NLTK in Python, course is designed with advanced solutions that will take you from newbie to pro in performing natural language processing with NLTK. You will come across various concepts covering natural language understanding, natural language processing, and syntactic analysis. It consists of everything you need to efficiently use NLTK to implement text classification, identify parts of speech, tag words, and more. You will also learn how to analyze sentence structures and master syntactic and semantic analysis.

By the end of this course, you will be all ready to bring deep learning and NLP techniques to build intelligent systems using NLTK in Python.
Meet Your Expert(s):

We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:

Smail Oubaalla is a talented Software Engineer with an interest in building the most effective, beautiful, and correct piece of software possible. He has helped companies build excellent programs. He also manages projects and has experience in designing and managing new ones. When not on the job, he loves hanging out with friends, hiking, and playing sports (football, basketball, rugby, and more). He also loves working his way through every recipe he can find in the family cookbook or elsewhere, and indulging his love for seeing new places.
Krishna Bhavsar has spent around 10 years working on natural language processing, social media analytics, and text mining in various industry domains such as hospitality, banking, healthcare, and more. He has worked on many different NLP libraries such as Stanford CoreNLP, IBM’s SystemText and BigInsights, GATE, and NLTK to solve industry problems related to textual analysis. He has also worked on analyzing social media responses for popular television shows and popular retail brands and products. He has also published a paper on sentiment analysis augmentation techniques in 2010 NAACL. he recently created an NLP pipeline/toolset and open sourced it for public use. Apart from academics and technology, Krishna has a passion for motorcycles and football. In his free time, he likes to travel and explore. He has gone on pan-India road trips on his motorcycle and backpacking trips across most of the countries in South East Asia and Europe.
Naresh Kumar has more than a decade of professional experience in designing, implementing, and running very-large-scale Internet applications in Fortune Top 500 companies. He is a full-stack architect with hands-on experience in domains such as ecommerce, web hosting, healthcare, big data and analytics, data streaming, advertising, and databases. He believes in open source and contributes to it actively. Naresh keeps himself up-to-date with emerging technologies, from Linux systems internals to frontend technologies. He studied in BITS-Pilani, Rajasthan with dual degree in computer science and economics.
Pratap Dangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, in its research and innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master’s degree from IIT Bombay in its industrial engineering and operations research program. Pratap is an artificial intelligence enthusiast. When not working, he likes to read about nextgen technologies and innovative methodologies. He is also the author of the book Statistics for Machine Learning by Packt.

Course Curriculum

Chapter 1: Natural Language Processing in Practice

Lecture 1: Course Overview

Lecture 2: Setup and Installation

Lecture 3: Understanding NLP and Its Benefits

Lecture 4: Exploring NLP Tools and Libraries

Lecture 5: Tokenization

Lecture 6: Stop Words

Lecture 7: Part of Speech Tagging

Lecture 8: Stemming and Lemmatization

Lecture 9: Named Entity Recognition

Lecture 10: TF-IDF

Lecture 11: Introduction to Sentiment Analysis

Lecture 12: Pre-Processing the Dataset

Lecture 13: Word Embeddings

Lecture 14: Build the Network

Lecture 15: Train the Model

Lecture 16: Test the Model

Lecture 17: Apply to a Single Input

Lecture 18: Machine Learning

Lecture 19: Classification

Lecture 20: Pre-Processing the Dataset

Lecture 21: Naïve Bayes and SVM

Lecture 22: Train the Classifier

Lecture 23: Test the Classifier

Lecture 24: Chatbots

Lecture 25: Simple NLTK Bot

Lecture 26: Create a ChatterBot

Lecture 27: Enhancing the Chabot

Lecture 28: Training the Chabot

Chapter 2: Developing NLP Applications Using NLTK in Python

Lecture 1: The Course Overview

Lecture 2: Exploring the In-Built Tagger

Lecture 3: Writing Your Own Tagger

Lecture 4: Training Your Own Tagger

Lecture 5: Learning to Write Your Own Grammar

Lecture 6: Writing a Probabilistic CFG

Lecture 7: Writing a Recursive CFG

Lecture 8: Using the Built-In Chunker

Lecture 9: Writing Your Own Simple Chunker

Lecture 10: Training a Chunker

Lecture 11: Parsing Recursive Descent

Lecture 12: Parsing Shift-Reduce

Lecture 13: Parsing Dependency Grammar and Projective Dependency

Lecture 14: Parsing a Chart

Lecture 15: Using Inbuilt NERs

Lecture 16: Creating, Inversing, and Using Dictionaries

Lecture 17: Choosing the Feature Set

Lecture 18: Segmenting Sentences Using Classification

Lecture 19: Writing a POS Tagger with Context

Lecture 20: Creating an NLP Pipeline

Lecture 21: Solving the Text Similarity Problem

Lecture 22: Resolving Anaphora

Lecture 23: Disambiguating Word Sense

Lecture 24: Performing Sentiment Analysis

Lecture 25: Exploring Advanced Sentiment Analysis

Lecture 26: Creating a Conversational Assistant or Chatbot

Instructors

Packt Publishing
Tech Knowledge in Motion

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