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NLP - Natural Language Processing with Python

Name: NLP - Natural Language Processing with Python
Rating: 4.4 (19938 reviews)

Learn to use Machine Learning, Spacy, NLTK, SciKit-Learn, Deep Learning, and more to conduct Natural Language Processing

Created byJose Portilla, Pierian Training

Last updated 4/2023

English

What you'll 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

Course content

9 sections • 80 lectures • 11h 24m total length

Course Overview - DO NOT SKIP THIS LECTURE PLEASE. IMPORTANT INFO HERE!4:07
Quick Check
Curriculum Overview3:07
Navigate the NLP with Python curriculum from text basics to deep learning, covering spaCy, NLTK, tokenization, stemming, lemmatization, stop words, part of speech tagging, and text classification.
Installation and Setup Lecture11:51
FAQ - Frequently Asked Questions2:52

Introduction to Python Text Basics0:36
Explore python text basics by reading text and PDFs with built-in libraries and no external dependencies. Learn basic regular expressions to find patterns in text and complete an assessment.
Working with Text Files with Python - Part One11:00
Working with Text Files with Python - Part Two20:39
Working with PDFs12:35
Learn to read and extract text from PDFs using PyPDF2, and handle limitations with scanned or non-extractable PDFs. Create and append pages to new PDFs with Python.
Regular Expressions Part One15:18
Regular Expressions Part Two10:35
Python Text Basics - Assessment Overview2:24
Explore the NLP assessment in Python text basics, including f-string printing, file creation and reading, PDF text extraction with PyPDF2, appending to Contacts.txt, and regex email extraction.
Python Text Basics - Assessment Solutions6:54

Introduction to Natural Language Processing0:41
We set up spaCy and the required language library, explore tokenization, stemming, lemmatization, and stop words, and compare NLTK and spaCy while outlining what natural language processing is.
Spacy Setup and Overview7:23
Explore spaCy, the open source python NLP library designed for speed, and compare it to NLTK. Learn to install spaCy, download the English language model, and load it for use.
What is Natural Language Processing?3:00
Explore what natural language processing is and how computers turn raw English text into structured data using spaCy and NLTK, with examples like spam detection and sentiment analysis.
Spacy Basics19:09
Tokenization - Part One15:37
Tokenization - Part Two6:13
Stemming9:23
Explore stemming in natural language processing using Porter's and Snowball stemmers in Python with NLTK, and compare their effects on words like run, runs, and fairly.
Lemmatization6:32
Stop Words4:55
Learn to filter stop words with spaCy, view the default 305 English stop words, and add or remove custom stop words like btw to tailor NLP preprocessing.
Phrase Matching and Vocabulary - Part One14:27
Phrase Matching and Vocabulary - Part Two6:42
Explore phrase matching and vocabulary techniques in Python NLP, using a phrase matcher on a Reaganomics text to extract terms like supply-side and trickle-down economics with context.
NLP Basics Assessment Overview2:44
NLP Basics Assessment Solution7:40

Introduction to Section on POS and NER0:32
Part of Speech Tagging16:50
Visualizing Part of Speech6:05
Visualizes parts of speech with displaCy in spaCy, showing POS tags and syntactic dependencies via render or serve, with options for distance, compact, color, background, and fonts.
Named Entity Recognition - Part One9:58
Named Entity Recognition - Part Two9:20
Learn to add multiple named entities with a spacey phrase matcher, creating product entities like vacuum space cleaner and vacuum dash cleaner, and count entities by label such as money.
Visualizing Named Entity Recognition7:11
Sentence Segmentation15:59
Part Of Speech Assessment2:19
Part Of Speech Assessment - Solutions8:22
Demonstrate a spaCy driven parts-of-speech assessment workflow: load English, build a doc from Peter Rabbit, analyze tokens, POS tags, dependency parse, noun frequency, and named entities.

Introduction to Text Classification0:50
Machine Learning Overview9:53
Classification Metrics11:55
Explore how classification metrics evaluate NLP models, focusing on accuracy, recall, precision, and F1 score through a binary spam vs ham example, using vectorization and a confusion matrix.
Confusion Matrix9:49
Scikit-Learn Primer - How to Use SciKit-Learn4:18
Scikit-Learn Primer - Code Along Part One15:22
Scikit-Learn Primer - Code Along Part Two8:46
Text Feature Extraction Overview6:21
Text Feature Extraction - Code Along Implementations14:29
Explore manual vocabulary construction and feature extraction from text, then vectorize across documents with scikit-learn using count vectorizer and tf-idf, and build pipelines for text classification.
Text Feature Extraction - Code Along - Part Two10:59
Text Classification Code Along Project10:56
Text Classification Assessment Overview1:07
Complete a text classification assessment by loading moviereviews2.tsv into a pandas data frame, building a vectorization pipeline, training a model, and evaluating with the confusion matrix, classification report, and accuracy.
Text Classification Assessment Solutions6:35

Introduction to Semantics and Sentiment Analysis0:27
Explore semantics and sentiment analysis using spaCy and Python. Learn the general syntax for sentiment analysis and apply Vader with NLTK for text classification.
Overview of Semantics and Word Vectors7:30
Semantics and Word Vectors with Spacy17:11
Explore spaCy word vectors and document vectors in Python, learn vector similarities and king minus man plus woman as a practical semantic demonstration.
Sentiment Analysis Overview4:45
Sentiment Analysis with NLTK13:04
Learn to perform sentiment analysis with Vader in Python using NLTK. Create a sentiment intensity analyzer, interpret negative, neutral, positive, and compound scores, and apply it to Amazon reviews.
Sentiment Analysis Code Along Movie Review Project7:38
Sentiment Analysis Project Assessment2:41
Sentiment Analysis Project Assessment - Solutions11:03

Introduction to Topic Modeling Section0:39
Overview of Topic Modeling2:03
Latent Dirichlet Allocation Overview10:44
Latent Dirichlet Allocation with Python - Part One8:55
Latent Dirichlet Allocation with Python - Part Two16:33
Non-negative Matrix Factorization Overview6:54
Non-negative Matrix Factorization with Python11:42
Topic Modeling Project - Overview3:42
Explore the topic modeling assessment project with over 400,000 Quora questions, applying tf-idf vectorization and non-negative matrix factorization to extract 20 topics. Use random state 42 to align topic results.
Topic Modeling Project - Solutions6:38

Introduction to Deep Learning for NLP0:53
Explore the basics of deep learning for natural language processing, study RNNs and LSTMs to generate text from a source corpus like Moby-Dick, and build Q&A chatbots in Python.
The Basic Perceptron Model5:12
Introduction to Neural Networks6:35
Connect multiple perceptrons into a multi-layer neural network with input, hidden, and output layers, then compare sigmoid, tanh, and ReLU activations, using z = w x + b and Keras.
Keras Basics - Part One13:44
Keras Basics - Part Two5:21
Recurrent Neural Network Overview7:47
LSTMs, GRU, and Text Generation10:11
Explore how LSTM and GRU cells manage long-term memory for text generation, detailing forget and input gates, cell state updates, and peepholes, using Keras for practical RNN data preparation.
Text Generation with LSTMs with Keras and Python - Part One16:23
Text Generation with LSTMs with Keras and Python - Part Two12:28
Text Generation with LSTMS with Keras - Part Three13:56
Chat Bots Overview7:18
Creating Chat Bots with Python - Part One10:29
Creating Chat Bots with Python - Part Two12:58
Learn to vectorize the dataset with Python by tokenizing text, building word indices, and padding sequences for stories, questions, and answers, using a reusable vectorize_stories function.
Creating Chat Bots with Python - Part Three16:49
Creating Chat Bots with Python - Part Four18:10
Create and train a Python chat bot, load the chatbot_10.h5 file after 100 epochs, evaluate on the test set, plot training history, and craft custom stories and questions.

Requirements

Understand general Python
Have permissions to install python packages onto computer
Internet connection

Description

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

Who this course is for:

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

NLP - Natural Language Processing with Python

What you'll learn

Explore related topics

Course content

Introduction4 lectures • 22min

Python Text Basics8 lectures • 1hr 20min

Natural Language Processing Basics13 lectures • 1hr 44min

Part of Speech Tagging and Named Entity Recognition9 lectures • 1hr 17min

Text Classification13 lectures • 1hr 51min

Semantics and Sentiment Analysis8 lectures • 1hr 4min

Topic Modeling9 lectures • 1hr 8min

Deep Learning for NLP15 lectures • 2hr 38min

BONUS SECTION: THANK YOU!1 lecture • 1min

Requirements

Description

Who this course is for: