English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 11.5 Hours | 4.51 GB
Learn to use Machine Learning, Spacy, NLTK, SciKit-Learn, Deep Learning, and more to conduct 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!
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
Table of Contents
Introduction
1 Course Overview – DO NOT SKIP THIS LECTURE PLEASE. IMPORTANT INFO HERE!
2 Curriculum Overview
3 Installation and Setup Lecture
4 FAQ – Frequently Asked Questions
Python Text Basics
5 Introduction to Python Text Basics
6 Working with Text Files with Python – Part One
7 Working with Text Files with Python – Part Two
8 Working with PDFs
9 Regular Expressions Part One
10 Regular Expressions Part Two
11 Python Text Basics – Assessment Overview
12 Python Text Basics – Assessment Solutions
Natural Language Processing Basics
13 Introduction to Natural Language Processing
14 Phrase Matching and Vocabulary – Part One
15 Phrase Matching and Vocabulary – Part Two
16 NLP Basics Assessment Overview
17 NLP Basics Assessment Solution
18 Spacy Setup and Overview
19 What is Natural Language Processing
20 Spacy Basics
21 Tokenization – Part One
22 Tokenization – Part Two
23 Stemming
24 Lemmatization
25 Stop Words
Part of Speech Tagging and Named Entity Recognition
26 Introduction to Section on POS and NER
27 Part of Speech Tagging
28 Visualizing Part of Speech
29 Named Entity Recognition – Part One
30 Named Entity Recognition – Part Two
31 Visualizing Named Entity Recognition
32 Sentence Segmentation
33 Part Of Speech Assessment
34 Part Of Speech Assessment – Solutions
Text Classification
35 Introduction to Text Classification
36 Text Feature Extraction – Code Along – Part Two
37 Text Classification Code Along Project
38 Text Classification Assessment Overview
39 Text Classification Assessment Solutions
40 Machine Learning Overview
41 Classification Metrics
42 Confusion Matrix
43 Scikit-Learn Primer – How to Use SciKit-Learn
44 Scikit-Learn Primer – Code Along Part One
45 Scikit-Learn Primer – Code Along Part Two
46 Text Feature Extraction Overview
47 Text Feature Extraction – Code Along Implementations
Semantics and Sentiment Analysis
48 Introduction to Semantics and Sentiment Analysis
49 Overview of Semantics and Word Vectors
50 Semantics and Word Vectors with Spacy
51 Sentiment Analysis Overview
52 Sentiment Analysis with NLTK
53 Sentiment Analysis Code Along Movie Review Project
54 Sentiment Analysis Project Assessment
55 Sentiment Analysis Project Assessment – Solutions
Topic Modeling
56 Introduction to Topic Modeling Section
57 Overview of Topic Modeling
58 Latent Dirichlet Allocation Overview
59 Latent Dirichlet Allocation with Python – Part One
60 Latent Dirichlet Allocation with Python – Part Two
61 Non-negative Matrix Factorization Overview
62 Non-negative Matrix Factorization with Python
63 Topic Modeling Project – Overview
64 Topic Modeling Project – Solutions
Deep Learning for NLP
65 Introduction to Deep Learning for NLP
66 Text Generation with LSTMS with Keras – Part Three
67 Chat Bots Overview
68 Creating Chat Bots with Python – Part One
69 Creating Chat Bots with Python – Part Two
70 Creating Chat Bots with Python – Part Three
71 Creating Chat Bots with Python – Part Four
72 The Basic Perceptron Model
73 Introduction to Neural Networks
74 Keras Basics – Part One
75 Keras Basics – Part Two
76 Recurrent Neural Network Overview
77 LSTMs, GRU, and Text Generation
78 Text Generation with LSTMs with Keras and Python – Part One
79 Text Generation with LSTMs with Keras and Python – Part Two
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