Natural Language Processing with Python- 3-in-1

Natural Language Processing with Python: 3-in-1, available at $19.99, has an average rating of 3.6, with 72 lectures, based on 22 reviews, and has 133 subscribers.

You will learn about Discover how to create frequency distributions on your text with NLTK Build your own movie review sentiment application in Python Import, access external corpus & explore frequency distribution of the text in corpus file Perform tokenization, stemming, lemmatization, spelling corrections, stop words removals, and more Build solutions such as text similarity, summarization, sentiment analysis and anaphora resolution to get up to speed with new trends in NLP Use dictionaries to create your own named entities using this easy-to-follow guide This course is ideal for individuals who are Python developers who wish to master Natural Language Processing and want to make their applications smarter by implementing NLP It is particularly useful for Python developers who wish to master Natural Language Processing and want to make their applications smarter by implementing NLP.

Enroll now: Natural Language Processing with Python: 3-in-1

Summary

Title: Natural Language Processing with Python: 3-in-1

Price: $19.99

Average Rating: 3.6

Number of Lectures: 72

Number of Published Lectures: 72

Number of Curriculum Items: 72

Number of Published Curriculum Objects: 72

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Discover how to create frequency distributions on your text with NLTK

Build your own movie review sentiment application in Python

Import, access external corpus & explore frequency distribution of the text in corpus file

Perform tokenization, stemming, lemmatization, spelling corrections, stop words removals, and more

Build solutions such as text similarity, summarization, sentiment analysis and anaphora resolution to get up to speed with new trends in NLP

Use dictionaries to create your own named entities using this easy-to-follow guide

Who Should Attend

Python developers who wish to master Natural Language Processing and want to make their applications smarter by implementing NLP

Target Audiences

Python developers who wish to master Natural Language Processing and want to make their applications smarter by implementing NLP

Natural Language Processing is a part of Artificial Intelligence that deals with the interactions between human (natural) languages and computers.?

This comprehensive 3-in-1 training course includes unique videos that will teach you various aspects of performing Natural Language Processing with NLTK—the leading Python platform for the task. Go through various topics in Natural Language Processing, ranging from an introduction to the relevant Python libraries to applying specific linguistics concepts while exploring text datasets with the help of real-word examples.

About the Author

Tyler Edwards is a senior engineer and software developer with over a decade of experience creating analysis tools in the space, defense, and nuclear industries. Tyler is experienced using a variety of programming languages (Python, C++, and more), and his research areas include machine learning, artificial intelligence, engineering analysis, and business analytics. Tyler holds a Master of Science degree in Mechanical Engineering from Ohio University. Looking forward, Tyler hopes to mentor students in applied mathematics, and demonstrate how data collection, analysis, and post-processing can be used to solve difficult problems and improve decision making.?

Krishna Bhavsar has spent around 10 years working on natural language processing, social media analytics, and text mining. He has worked on many different NLP libraries such as Stanford Core NLP, IBM’s System Text and Big Insights, GATE, and NLTK to solve industry problems related to textual analysis. He has also published a paper on sentiment analysis augmentation techniques in 2010 NAACL. Apart from academics, he has a passion for motorcycles and football. In his free time, he likes to travel and explore.

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 e-commerce, 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 Next-gen 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 with Python

Lecture 1: The Course Overview

Lecture 2: Installing and Setting Up NLTK

Lecture 3: Implementing Simple NLP Tasks and Exploring NLTK Libraries

Lecture 4: Part-Of-Speech Tagging

Lecture 5: Stemming and Lemmatization

Lecture 6: Named Entity Recognition

Lecture 7: Frequency Distribution with NLTK

Lecture 8: Frequency Distribution on Your Text with NLTK

Lecture 9: Concordance Function in NLTK

Lecture 10: Similar Function in NLTK

Lecture 11: Dispersion Plot Function in NLTK

Lecture 12: Count Function in NLTK

Lecture 13: Introduction to Recurrent Neural Network and Long Short Term Memory

Lecture 14: Programming Your Own Sentiment Classifier Using NLTK

Lecture 15: Perform Sentiment Classification on a Movie Rating Dataset

Lecture 16: Starting with Latent Semantic Analysis

Lecture 17: Programming Example of Principal Component Analysis

Lecture 18: Programming Example of Singular Value Decomposition

Chapter 2: Text Processing Using NLTK in Python

Lecture 1: The Course Overview

Lecture 2: Accessing In-Built Corpora

Lecture 3: Downloading an External Corpus

Lecture 4: Counting All the wh-words

Lecture 5: Frequency Distribution Operations

Lecture 6: WordNet

Lecture 7: The Concepts of Hyponyms and Hypernyms Using WordNet

Lecture 8: Compute the Average Polysemy According to WordNet

Lecture 9: The Importance of String Operations

Lecture 10: Getting Deeper with String Operations

Lecture 11: Reading a PDF File in Python

Lecture 12: Reading Word Documents in Python

Lecture 13: Creating a User-Defined Corpus

Lecture 14: Reading Contents from an RSS Feed

Lecture 15: HTML Parsing Using BeautifulSoup

Lecture 16: Tokenization – Learning to Use the Inbuilt Tokenizers of NLTK

Lecture 17: Stemming – Learning to Use the Inbuilt Stemmers of NLTK

Lecture 18: Lemmatization – Learning to Use the WordNetLemmatizer of NLTK

Lecture 19: Stopwords – Learning to Use the Stopwords Corpus

Lecture 20: Edit Distance – Writing Your Own Algorithm to Find Edit Distance Between Two Str

Lecture 21: Processing Two Short Stories and Extracting the Common Vocabulary

Lecture 22: Regular Expression – Learning to Use *, +, and ?

Lecture 23: Regular Expression – Learning to Use Non-Start and Non-End of Word

Lecture 24: Searching Multiple Literal Strings and Substrings Occurrences

Lecture 25: Creating Date Regex

Lecture 26: Making Abbreviations

Lecture 27: Learning to Write Your Own Regex Tokenizer

Lecture 28: Learning to Write Your Own Regex Stemmer

Chapter 3: 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

Rating Distribution

1 stars: 0 votes

2 stars: 3 votes

3 stars: 6 votes

4 stars: 7 votes

5 stars: 6 votes

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