NLP Natural Language Processing with Python

NLP – Natural Language Processing with Python, available at $99.99, has an average rating of 4.47, with 80 lectures, 1 quizzes, based on 17555 reviews, and has 92982 subscribers.

You will learn about Learn to work with Text Files with Python Learn how to work with PDF files in Python Utilize Regular Expressions for pattern searching in text Use Spacy for ultra fast tokenization Learn about Stemming and Lemmatization Understand Vocabulary Matching with Spacy Use Part of Speech Tagging to automatically process raw text files Understand Named Entity Recognition Visualize POS and NER with Spacy Use SciKit-Learn for Text Classification Use Latent Dirichlet Allocation for Topic Modelling Learn about Non-negative Matrix Factorization Use the Word2Vec algorithm Use NLTK for Sentiment Analysis Use Deep Learning to build out your own chat bot This course is ideal for individuals who are Python developers interested in learning how to use Natural Language Processing. It is particularly useful for Python developers interested in learning how to use Natural Language Processing.

Enroll now: NLP – Natural Language Processing with Python

Summary

Title: NLP – Natural Language Processing with Python

Price: $99.99

Average Rating: 4.47

Number of Lectures: 80

Number of Quizzes: 1

Number of Published Lectures: 80

Number of Published Quizzes: 1

Number of Curriculum Items: 81

Number of Published Curriculum Objects: 81

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Learn to work with Text Files with Python

Learn how to work with PDF files in Python

Utilize Regular Expressions for pattern searching in text

Use Spacy for ultra fast tokenization

Learn about Stemming and Lemmatization

Understand Vocabulary Matching with Spacy

Use Part of Speech Tagging to automatically process raw text files

Understand Named Entity Recognition

Visualize POS and NER with Spacy

Use SciKit-Learn for Text Classification

Use Latent Dirichlet Allocation for Topic Modelling

Learn about Non-negative Matrix Factorization

Use the Word2Vec algorithm

Use NLTK for Sentiment Analysis

Use Deep Learning to build out your own chat bot

Who Should Attend

Python developers interested in learning how to use Natural Language Processing.

Target Audiences

Python developers interested in learning how to use Natural Language Processing.

Welcome to the best Natural Language Processing course on the internet! This course is designed to be your complete online resource for learning how to use Natural Language Processing with the Python programming language.

In the course we will cover everything you need to learn in order to become a world class practitioner of NLP with Python.

We’ll start off with the basics, learning how to open and work with text and?PDF files with Python, as well as learning how to use regular expressions to search for custom patterns inside of text files.

Afterwards we will begin with the basics of Natural Language Processing, utilizing the Natural Language Toolkit library for Python, as well as the state of the art Spacy library for ultra fast tokenization, parsing, entity recognition, and lemmatization of text.

We’ll understand fundamental NLP concepts such as stemming, lemmatization, stop words, phrase matching, tokenization and more!

Next we will cover Part-of-Speech tagging, where your Python scripts will be able to automatically assign words in text to their appropriate part of speech, such as nouns, verbs and adjectives, an essential part of building intelligent language systems.

We’ll also learn about named entity recognition, allowing your code to automatically understand concepts like money, time, companies, products, and more simply by supplying the text information.

Through state of the art visualization libraries we will be able view these relationships in real time.

Then we will move on to understanding machine learning with Scikit-Learn to conduct text classification, such as automatically building machine learning systems that can determine positive versus negative movie reviews, or spam versus legitimate email messages.

We will expand this knowledge to more complex unsupervised learning methods for natural language processing, such as topic modelling, where our machine learning models will detect topics and major concepts from raw text files.

This course even covers advanced topics, such as sentiment analysis of text with the NLTK?library, and creating semantic word vectors with the Word2Vec algorithm.

Included in this course is an entire section devoted to state of the art advanced topics, such as using deep learning to build out our own chat bots!

Not only do you get fantastic technical content with this course, but you will also get access to both our course related Question and Answer forums, as well as our live student chat channel, so you can team up with other students for projects, or get help on the course content from myself and the course teaching assistants.

All of this comes with a 30 day money back garuantee, so you can try the course risk free.

What are you waiting for? Become an expert in natural language processing today!

I will see you inside the course,

Jose

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Overview – DO NOT SKIP THIS LECTURE PLEASE. IMPORTANT INFO HERE!

Lecture 2: Curriculum Overview

Lecture 3: Installation and Setup Lecture

Lecture 4: FAQ – Frequently Asked Questions

Chapter 2: Python Text Basics

Lecture 1: Introduction to Python Text Basics

Lecture 2: Working with Text Files with Python – Part One

Lecture 3: Working with Text Files with Python – Part Two

Lecture 4: Working with PDFs

Lecture 5: Regular Expressions Part One

Lecture 6: Regular Expressions Part Two

Lecture 7: Python Text Basics – Assessment Overview

Lecture 8: Python Text Basics – Assessment Solutions

Chapter 3: Natural Language Processing Basics

Lecture 1: Introduction to Natural Language Processing

Lecture 2: Spacy Setup and Overview

Lecture 3: What is Natural Language Processing?

Lecture 4: Spacy Basics

Lecture 5: Tokenization – Part One

Lecture 6: Tokenization – Part Two

Lecture 7: Stemming

Lecture 8: Lemmatization

Lecture 9: Stop Words

Lecture 10: Phrase Matching and Vocabulary – Part One

Lecture 11: Phrase Matching and Vocabulary – Part Two

Lecture 12: NLP Basics Assessment Overview

Lecture 13: NLP Basics Assessment Solution

Chapter 4: Part of Speech Tagging and Named Entity Recognition

Lecture 1: Introduction to Section on POS and NER

Lecture 2: Part of Speech Tagging

Lecture 3: Visualizing Part of Speech

Lecture 4: Named Entity Recognition – Part One

Lecture 5: Named Entity Recognition – Part Two

Lecture 6: Visualizing Named Entity Recognition

Lecture 7: Sentence Segmentation

Lecture 8: Part Of Speech Assessment

Lecture 9: Part Of Speech Assessment – Solutions

Chapter 5: Text Classification

Lecture 1: Introduction to Text Classification

Lecture 2: Machine Learning Overview

Lecture 3: Classification Metrics

Lecture 4: Confusion Matrix

Lecture 5: Scikit-Learn Primer – How to Use SciKit-Learn

Lecture 6: Scikit-Learn Primer – Code Along Part One

Lecture 7: Scikit-Learn Primer – Code Along Part Two

Lecture 8: Text Feature Extraction Overview

Lecture 9: Text Feature Extraction – Code Along Implementations

Lecture 10: Text Feature Extraction – Code Along – Part Two

Lecture 11: Text Classification Code Along Project

Lecture 12: Text Classification Assessment Overview

Lecture 13: Text Classification Assessment Solutions

Chapter 6: Semantics and Sentiment Analysis

Lecture 1: Introduction to Semantics and Sentiment Analysis

Lecture 2: Overview of Semantics and Word Vectors

Lecture 3: Semantics and Word Vectors with Spacy

Lecture 4: Sentiment Analysis Overview

Lecture 5: Sentiment Analysis with NLTK

Lecture 6: Sentiment Analysis Code Along Movie Review Project

Lecture 7: Sentiment Analysis Project Assessment

Lecture 8: Sentiment Analysis Project Assessment – Solutions

Chapter 7: Topic Modeling

Lecture 1: Introduction to Topic Modeling Section

Lecture 2: Overview of Topic Modeling

Lecture 3: Latent Dirichlet Allocation Overview

Lecture 4: Latent Dirichlet Allocation with Python – Part One

Lecture 5: Latent Dirichlet Allocation with Python – Part Two

Lecture 6: Non-negative Matrix Factorization Overview

Lecture 7: Non-negative Matrix Factorization with Python

Lecture 8: Topic Modeling Project – Overview

Lecture 9: Topic Modeling Project – Solutions

Chapter 8: Deep Learning for NLP

Lecture 1: Introduction to Deep Learning for NLP

Lecture 2: The Basic Perceptron Model

Lecture 3: Introduction to Neural Networks

Lecture 4: Keras Basics – Part One

Lecture 5: Keras Basics – Part Two

Lecture 6: Recurrent Neural Network Overview

Lecture 7: LSTMs, GRU, and Text Generation

Lecture 8: Text Generation with LSTMs with Keras and Python – Part One

Lecture 9: Text Generation with LSTMs with Keras and Python – Part Two

Lecture 10: Text Generation with LSTMS with Keras – Part Three

Lecture 11: Chat Bots Overview

Lecture 12: Creating Chat Bots with Python – Part One

Lecture 13: Creating Chat Bots with Python – Part Two

Lecture 14: Creating Chat Bots with Python – Part Three

Lecture 15: Creating Chat Bots with Python – Part Four

Chapter 9: BONUS SECTION: THANK YOU!

Lecture 1: BONUS LECTURE

Instructors

Jose Portilla
Head of Data Science at Pierian Training

Pierian Training
Data Science and Machine Learning Training

Rating Distribution

1 stars: 98 votes

2 stars: 141 votes

3 stars: 1337 votes

4 stars: 6257 votes

5 stars: 9721 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!