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NLP-Natural Language Processing in Python(Theory & Projects)

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NLP-Natural Language Processing in Python(Theory & Projects), available at $54.99, has an average rating of 4.35, with 259 lectures, based on 100 reviews, and has 1077 subscribers.

You will learn about • The importance of Natural Language Processing (NLP) in Data Science. • The reasons to move from classical sequence models to deep learning-based sequence models. • The essential concepts from the absolute beginning with complete unraveling with examples in Python. • Details of deep learning models for NLP with examples. • A summary of the concepts of Deep Learning theory. • Practical description and live coding with Python. • Deep PyTorch (Deep learning framework by Facebook). • The use and applications of state-of-the-art NLP models. • Building your own applications for automatic text generation and language translators. • And much more… This course is ideal for individuals who are • Complete beginners to Natural Language Processing. or • People who want to upgrade their Python programming skills for NLP. or • Individuals who are passionate about data science and machine learning. or • Data Scientists. or • Data Analysts. or • Machine Learning Practitioners. It is particularly useful for • Complete beginners to Natural Language Processing. or • People who want to upgrade their Python programming skills for NLP. or • Individuals who are passionate about data science and machine learning. or • Data Scientists. or • Data Analysts. or • Machine Learning Practitioners.

Enroll now: NLP-Natural Language Processing in Python(Theory & Projects)

Summary

Title: NLP-Natural Language Processing in Python(Theory & Projects)

Price: $54.99

Average Rating: 4.35

Number of Lectures: 259

Number of Published Lectures: 258

Number of Curriculum Items: 259

Number of Published Curriculum Objects: 258

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • • The importance of Natural Language Processing (NLP) in Data Science.
  • • The reasons to move from classical sequence models to deep learning-based sequence models.
  • • The essential concepts from the absolute beginning with complete unraveling with examples in Python.
  • • Details of deep learning models for NLP with examples.
  • • A summary of the concepts of Deep Learning theory.
  • • Practical description and live coding with Python.
  • • Deep PyTorch (Deep learning framework by Facebook).
  • • The use and applications of state-of-the-art NLP models.
  • • Building your own applications for automatic text generation and language translators.
  • • And much more…

Who Should Attend

  • • Complete beginners to Natural Language Processing.
  • • People who want to upgrade their Python programming skills for NLP.
  • • Individuals who are passionate about data science and machine learning.
  • • Data Scientists.
  • • Data Analysts.
  • • Machine Learning Practitioners.

Target Audiences

  • • Complete beginners to Natural Language Processing.
  • • People who want to upgrade their Python programming skills for NLP.
  • • Individuals who are passionate about data science and machine learning.
  • • Data Scientists.
  • • Data Analysts.
  • • Machine Learning Practitioners.

Master Natural Language Processing (NLP): Unleash the Power of AI in Language Understanding and Text Analysis

Are you ready to embark on an exciting journey into the world of Natural Language Processing (NLP)? This comprehensive course is your gateway to mastering the art of understanding human language and harnessing the incredible capabilities of AI for text analysis and language understanding. Whether you’re a novice or an aspiring NLP practitioner, this course offers an extensive exploration of NLP theory and hands-on practice using Python.

Course Highlights:

In this enlightening course, you will:

1. Explore NLP Foundations: Gain a solid understanding of NLP concepts, its importance, and its applications in fields like speech recognition, sentiment analysis, language translation, and chatbots.

2. Harness Python’s Power: Leverage Python’s extensive libraries and tools for text analysis, text preprocessing, and data extraction. Python’s versatility makes it the ideal language for NLP.

3. Master Text Preprocessing: Dive into the nitty-gritty of text preprocessing, including regular expressions, text normalization, tokenization, and more. Learn how to prepare text data for analysis effectively.

4. Decode Word Embeddings: Unlock the potential of word embeddings, from traditional methods like one-hot vectors to advanced techniques like Word2Vec, GloVe, and BERT. Understand how words are represented in vectors and their applications.

5. Grasp Deep Learning for NLP: Explore neural networks, recurrent neural networks (RNNs), their types (one to one, one to many, many to one, many to many), bi-directional RNNs, deep RNNs, and more. Understand how deep learning is revolutionizing NLP.

6. Real-World Projects: Apply your NLP skills to practical projects, including building a Neural Machine/Language Translator and developing a Chatbot. These projects will challenge you and reinforce your learning.

7. Extensive Learning Material: Access high-quality video lectures, assessments, course notes, and handouts to enhance your understanding. We provide comprehensive resources to support your learning journey.

8. Supportive Community: Reach out to our friendly team for prompt assistance with any course-related queries. We are here to help you succeed.

Course Modules:

Here’s a glimpse of what you’ll explore throughout this comprehensive course:

  • Introduction to NLP: Understand the essence of NLP, its significance, and its applications in various domains. Get an overview of essential software tools used in NLP.

  • Text Preprocessing: Dive into text preprocessing techniques, including regular expressions, text normalization, tokenization, and string matching. Learn how to clean and prepare text data for analysis.

  • Word Embeddings: Explore language models, vocabulary, N-Grams, one-hot vectors, and advanced word embeddings like Word2Vec, GloVe, and BERT. Understand the mathematical foundations and applications of word embeddings.

  • NLP with Deep Learning: Master neural networks, different RNN architectures (one to one, one to many, many to one, many to many), advanced RNN models for NLP (encoder-decoder models, attention mechanisms), and deep learning techniques. Discover how deep learning has transformed NLP.

  • Projects: Apply your newfound knowledge to real-world projects. Build a Neural Machine/Language Translator and create a Chatbot. These hands-on projects will allow you to demonstrate your skills and creativity in solving practical NLP problems.

Who Should Enroll:

This course is designed to cater to a wide audience, making it suitable for:

  • Beginners who are eager to venture into the fascinating world of Natural Language Processing

  • Python enthusiasts looking to enhance their programming skills for NLP applications

  • Data Scientists, Data Analysts, and Machine Learning Practitioners aiming to add NLP expertise to their skill set

Upon successful completion of this course, you’ll be equipped with the knowledge and hands-on experience to confidently tackle NLP challenges, create AI-powered language understanding systems, and embark on exciting career opportunities in the field of Natural Language Processing.

Unlock the Potential of NLP and Transform Your Skill Set. Enroll Now and Harness the Power of AI in Language Understanding and Text Analysis!

Keywords:

  • Natural Language Processing (NLP)

  • Artificial Intelligence (AI)

  • Text Analysis

  • Language Understanding

  • Python Programming

  • Text Preprocessing

  • Word Embeddings

  • Word Vectors

  • Deep Learning for NLP

  • Neural Networks

  • Recurrent Neural Networks (RNNs)

  • Word2Vec

  • GloVe

  • BERT

  • Language Models

  • Chatbots

  • Sentiment Analysis

  • Speech Recognition

  • Machine Translation

  • Text Data Processing

  • Text Normalization

  • Tokenization

  • Regular Expressions

  • Data Extraction

  • Text Mining

  • NLP Applications

  • Natural Language Understanding

  • Language Processing Tools

  • NLP Projects

  • AI-powered Language Systems

  • Career Opportunities in NLP

  • NLP Certification

  • Master NLP with Python

  • Learn Text Analysis with NLP

  • Python for Natural Language Processing

  • Dive into Word Embeddings

  • Deep Learning Techniques for NLP

  • Hands-on NLP Projects

  • Build AI-driven Chatbots

  • Sentiment Analysis in Python

  • NLP Career Advancement

  • Language Understanding Systems

  • Natural Language Processing Course

  • NLP Training and Certification

  • AI in Text Data Analysis

  • Harnessing NLP in Python

  • Unlock the Power of NLP

  • Real-world NLP Applications

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to Course

Lecture 2: Introduction to Instructor

Lecture 3: Introduction to Co-Instructor

Lecture 4: Course Introduction

Lecture 5: Request for Your Honest Review

Lecture 6: Links for the Course's Materials and Codes

Chapter 2: Introduction(Regular Expressions)

Lecture 1: Links for the Course's Materials and Codes

Lecture 2: What Is Regular Expression

Lecture 3: Why Regular Expression

Lecture 4: ELIZA Chatbot

Lecture 5: Python Regular Expression Package

Chapter 3: Meta Characters(Regular Expressions)

Lecture 1: Links for the Course's Materials and Codes

Lecture 2: Meta Characters

Lecture 3: Meta Characters Bigbrackets Exercise

Lecture 4: Meta Characters Bigbrackets Exercise Solution

Lecture 5: Meta Characters Bigbrackets Exercise 2

Lecture 6: Meta Characters Bigbrackets Exercise 2 Solution

Lecture 7: Meta Characters Cap

Lecture 8: Meta Characters Cap Exercise 3

Lecture 9: Meta Characters Cap Exercise 3 Solution

Lecture 10: Backslash

Lecture 11: Backslash Continued

Lecture 12: Backslash Continued 01

Lecture 13: Backslash Squared Brackets Exercise

Lecture 14: Backslash Squared Brackets Exercise Solution

Lecture 15: Backslash Squared Brackets Exercise Another Solution

Lecture 16: Backslash Exercise

Lecture 17: Backslash Exercise Solution And Special Sequences Exercise

Lecture 18: Solution And Special Sequences Exercise Solution

Lecture 19: Meta Character Asterisk

Lecture 20: Meta Character Asterisk Exercise

Lecture 21: Meta Character Asterisk Exercise Solution

Lecture 22: Meta Character Asterisk Homework

Lecture 23: Meta Character Asterisk Greedymatching

Lecture 24: Meta Character Plus And Questionmark

Lecture 25: Meta Character Curly Brackets Exercise

Lecture 26: Meta Character Curly Brackets Exercise Solution

Chapter 4: Pattern Objects(Regular Expressions)

Lecture 1: Links for the Course's Materials and Codes

Lecture 2: Pattern Objects

Lecture 3: Pattern Objects Match Method Exersize

Lecture 4: Pattern Objects Match Method Exersize Solution

Lecture 5: Pattern Objects Match Method Vs Search Method

Lecture 6: Pattern Objects Finditer Method

Lecture 7: Pattern Objects Finditer Method Exersize Solution

Chapter 5: More Meta Characters(Regular Expressions)

Lecture 1: Links for the Course's Materials and Codes

Lecture 2: Meta Characters Logical Or

Lecture 3: Meta Characters Beginning And End Patterns

Lecture 4: Meta Characters Paranthesis

Chapter 6: String Modification(Regular Expressions)

Lecture 1: Links for the Course's Materials and Codes

Lecture 2: String Modification

Lecture 3: Word Tokenizer Using Split Method

Lecture 4: Sub Method Exercise

Lecture 5: Sub Method Exercise Solution

Chapter 7: Words and Tokens(Text Preprocessing)

Lecture 1: Links for the Course's Materials and Codes

Lecture 2: What Is A Word

Lecture 3: Definition Of Word Is Task Dependent

Lecture 4: Vocabulary And Corpus

Lecture 5: Tokens

Lecture 6: Tokenization In Spacy

Chapter 8: Sentiment Classification(Text Preprocessing)

Lecture 1: Links for the Course's Materials and Codes

Lecture 2: Yelp Reviews Classification Mini Project Introduction

Lecture 3: Yelp Reviews Classification Mini Project Vocabulary Initialization

Lecture 4: Yelp Reviews Classification Mini Project Adding Tokens To Vocabulary

Lecture 5: Yelp Reviews Classification Mini Project Look Up Functions In Vocabulary

Lecture 6: Yelp Reviews Classification Mini Project Building Vocabulary From Data

Lecture 7: Yelp Reviews Classification Mini Project One Hot Encoding

Lecture 8: Yelp Reviews Classification Mini Project One Hot Encoding Implementation

Lecture 9: Yelp Reviews Classification Mini Project Encoding Documents

Lecture 10: Yelp Reviews Classification Mini Project Encoding Documents Implementation

Lecture 11: Yelp Reviews Classification Mini Project Train Test Splits

Lecture 12: Yelp Reviews Classification Mini Project Featurecomputation

Lecture 13: Yelp Reviews Classification Mini Project Classification

Chapter 9: Language Independent Tokenization(Text Preprocessing)

Lecture 1: Links for the Course's Materials and Codes

Lecture 2: Tokenization In Detial Introduction

Lecture 3: Tokenization Is Hard

Lecture 4: Tokenization Byte Pair Encoding

Lecture 5: Tokenization Byte Pair Encoding Example

Lecture 6: Tokenization Byte Pair Encoding On Test Data

Lecture 7: Tokenization Byte Pair Encoding Implementation Getpaircounts

Lecture 8: Tokenization Byte Pair Encoding Implementation Mergeincorpus

Lecture 9: Tokenization Byte Pair Encoding Implementation BFE Training

Lecture 10: Tokenization Byte Pair Encoding Implementation BFE Encoding

Lecture 11: Tokenization Byte Pair Encoding Implementation BFE Encoding One Pair

Lecture 12: Tokenization Byte Pair Encoding Implementation BFE Encoding One Pair 1

Chapter 10: Text Nomalization(Text Preprocessing)

Lecture 1: Links for the Course's Materials and Codes

Lecture 2: Word Normalization Case Folding

Lecture 3: Word Normalization Lematization

Lecture 4: Word Normalization Stemming

Lecture 5: Word Normalization Sentence Segmentation

Chapter 11: String Matching and Spelling Correction(Text Preprocessing)

Instructors

Rating Distribution

  • 1 stars: 4 votes
  • 2 stars: 3 votes
  • 3 stars: 11 votes
  • 4 stars: 23 votes
  • 5 stars: 59 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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