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Development Data Science Natural Language Processing

Natural Language Processing with Python: 3-in-1

Build solutions to get up and speed with new trends in NLP. Three complete courses in one comprehensive training program
Rating: 3.5 out of 53.5 (21 ratings)
126 students
Created by Packt Publishing
Last updated 8/2018
English
English [Auto]
30-Day Money-Back Guarantee

What you'll 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

Course content

3 sections • 72 lectures • 4h 29m total length

  • Preview03:35
  • Installing and Setting Up NLTK
    06:31
  • Implementing Simple NLP Tasks and Exploring NLTK Libraries
    09:05
  • Part-Of-Speech Tagging
    08:38
  • Stemming and Lemmatization
    09:32
  • Named Entity Recognition
    07:30
  • Frequency Distribution with NLTK
    04:56
  • Frequency Distribution on Your Text with NLTK
    06:13
  • Concordance Function in NLTK
    04:06
  • Similar Function in NLTK
    03:33
  • Dispersion Plot Function in NLTK
    04:15
  • Count Function in NLTK
    04:44
  • Introduction to Recurrent Neural Network and Long Short Term Memory
    03:54
  • Programming Your Own Sentiment Classifier Using NLTK
    04:04
  • Perform Sentiment Classification on a Movie Rating Dataset
    06:46
  • Starting with Latent Semantic Analysis
    05:54
  • Programming Example of Principal Component Analysis
    06:32
  • Programming Example of Singular Value Decomposition
    07:25

  • Preview03:22
  • Accessing In-Built Corpora
    04:07
  • Downloading an External Corpus
    03:33
  • Counting All the wh-words
    03:43
  • Frequency Distribution Operations
    02:40
  • WordNet
    03:09
  • The Concepts of Hyponyms and Hypernyms Using WordNet
    03:39
  • Compute the Average Polysemy According to WordNet
    03:28
  • The Importance of String Operations
    03:09
  • Getting Deeper with String Operations
    02:58
  • Reading a PDF File in Python
    02:54
  • Reading Word Documents in Python
    03:56
  • Creating a User-Defined Corpus
    04:30
  • Reading Contents from an RSS Feed
    02:49
  • HTML Parsing Using BeautifulSoup
    03:50
  • Tokenization – Learning to Use the Inbuilt Tokenizers of NLTK
    02:51
  • Stemming – Learning to Use the Inbuilt Stemmers of NLTK
    02:28
  • Lemmatization – Learning to Use the WordNetLemmatizer of NLTK
    02:20
  • Stopwords – Learning to Use the Stopwords Corpus
    03:14
  • Edit Distance – Writing Your Own Algorithm to Find Edit Distance Between Two Str
    02:48
  • Processing Two Short Stories and Extracting the Common Vocabulary
    02:38
  • Regular Expression – Learning to Use *, +, and ?
    03:23
  • Regular Expression – Learning to Use Non-Start and Non-End of Word
    03:20
  • Searching Multiple Literal Strings and Substrings Occurrences
    01:53
  • Creating Date Regex
    02:40
  • Making Abbreviations
    01:19
  • Learning to Write Your Own Regex Tokenizer
    01:21
  • Learning to Write Your Own Regex Stemmer
    02:14

  • Preview03:25
  • Exploring the In-Built Tagger
    02:08
  • Writing Your Own Tagger
    05:42
  • Training Your Own Tagger
    03:05
  • Learning to Write Your Own Grammar
    01:55
  • Writing a Probabilistic CFG
    02:28
  • Writing a Recursive CFG
    02:10
  • Using the Built-In Chunker
    02:02
  • Writing Your Own Simple Chunker
    02:13
  • Training a Chunker
    02:23
  • Parsing Recursive Descent
    01:40
  • Parsing Shift-Reduce
    01:42
  • Parsing Dependency Grammar and Projective Dependency
    01:38
  • Parsing a Chart
    02:56
  • Using Inbuilt NERs
    02:09
  • Creating, Inversing, and Using Dictionaries
    03:44
  • Choosing the Feature Set
    03:48
  • Segmenting Sentences Using Classification
    02:31
  • Writing a POS Tagger with Context
    02:28
  • Creating an NLP Pipeline
    07:03
  • Solving the Text Similarity Problem
    04:02
  • Resolving Anaphora
    03:35
  • Disambiguating Word Sense
    02:44
  • Performing Sentiment Analysis
    03:01
  • Exploring Advanced Sentiment Analysis
    03:04
  • Creating a Conversational Assistant or Chatbot
    03:57

Requirements

  • Good knowledge of Python is a must

Description

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.

Who this course is for:

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

Instructor

Packt Publishing
Tech Knowledge in Motion
Packt Publishing
  • 3.9 Instructor Rating
  • 57,914 Reviews
  • 349,049 Students
  • 1,420 Courses

Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

With an extensive library of content - more than 4000 books and video courses -Packt's mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what's important to them now.

From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Packt Udemy courses continue this tradition, bringing you comprehensive yet concise video courses straight from the experts.



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