Natural Language Processing: Machine Learning NLP In Python

Natural Language Processing: Machine Learning NLP In Python

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 19h 44m | 10.9 GB

A Complete Beginner NLP Syllabus. Practicals: Linguistics, Sentiment, Scrape Tweets, RNNs, Chatbot, Hugging Face & more!

This course takes you from a beginner level to being able to understand NLP concepts, linguistic theory, and then practice these basic theories using Python – with very simple examples as you code along with me.

Get experience doing a full real-world workflow from Collecting your own Data to NLP Sentiment Analysis using Big Datasets of over 50,000 Tweets.

Data collection: Scrape Twitter using: OSINT – Open Source Intelligence Tools: Gather text data using real-world techniques. In the real world, in many instances you would have to create your own data set; i.e source your data instead of downloading a clean, ready-made file online

Use Python to search relevant tweets for your study and NLP to analyze sentiment.

Language Syntax: Most NLP courses ignore the core domain of Linguistics. This course explains the fundamentals of Language Syntax & Parse trees – the foundation of how a machine can interpret the structure of s sentence.

New to Python: If you are new to Python or any computer programming, the course instructions make it easy for you to code together with me. I explain code line by line.

No Installs, we go straight to coding – Code using Google Colab – to be up-to-date with what’s being used in the Data Science world 2021!

The gentle pace takes you gradually from these basics of NLP foundation to being able to understand Mathematical & Linguistic (English-Language-based, Non-Mathematical) theories of Deep Learning.

Natural Language Processing Foundation

  • Linguistics & Semantics – study the background theory on natural language to better understand the Computer Science applications
  • Pre-processing Data (cleaning)
  • Regex, Tokenization, Stemming, Lemmatization
  • Name Entity Recognition (NER)
  • Part-of-Speech Tagging


SQuAD – Stanford Question Answer Dataset. Train your Q&A Model on this awesome SQuAD dataset.


  • NLTK
  • Sci-kit Learn
  • Hugging Face
  • Tensorflow
  • Pytorch
  • SpaCy
  • Twint

The topics outlined below are taught using practical Python projects!

  • Parse Tree
  • Markov Chain
  • Text Classification & Sentiment Analysis
  • Company Name Generator
  • Unsupervised Sentiment Analysis
  • Topic Modelling
  • Word Embedding with Deep Learning Models
  • Closed Domain Question Answering (Like asking questions on many different topics, from Beyonce to Iranian Cuisine)
  • LSTM using TensorFlow, Keras Sequence Model
  • Speech Recognition
  • Convert Speech to Text

Neural Networks

  • This is taught from first principles – comparing Biological Neurons in the Human Brain to Artificial Neurons.
  • Practical project: Sentiment Analysis of Steam Reviews

Word Embedding: This topic is covered in detail, similar to an undergraduate course structure that includes the theory & practical examples of:

  • TF-IDF
  • Word2Vec
  • One Hot Encoding
  • gloVe

Deep Learning

  • Recurrent Neural Networks
  • LSTMs
  • Get introduced to Long short-term memory and the recurrent neural network architecture used in the field of deep learning.
  • Build models using LSTMs

What you’ll learn

  • Libraries: Hugging Face, NLTK, SpaCy, Keras, Sci-kit Learn, Tensorflow, Pytorch, Twint
  • Linguistics Foundation To Help Learn NLP Concepts
  • Deep Learning: Neural Networks, RNN, LSTM Theory & Practical Projects
  • Scrape Unlimited Tweets Using An Open Source Intelligence Tool
  • Machine Reading Comprehension: Create A Question Answering System with SQuAD
  • No Tedious Anaconda or Jupyter Installs: Use Modern Google Colab Cloud-Based Notebooks for using Python
  • How To Build Generative AI Chatbots
  • Create A Netflix Recommendation System With Word2Vec
  • Perform Sentiment Analysis on Steam Game Reviews
  • Convert Speech To Text
  • Machine Learning Modelling Techniques
  • Markov Property – Theory & Practical
  • Optional Python For Beginners Section
  • Cosine-Similarity & Vectors
  • Word Embeddings: My Favourite Topic Taught In Depth
  • Speech Recognition
  • LSTM Fake News Detector
  • Context-Free Grammar Syntax
  • Scrape Wikipedia & Create An Article Summarizer
Table of Contents

1 Introduction
2 How To Start This Course (Must Watch)

Intro NLP, Data Science & Machine Learning – Are they different
3 Introducing NLP
4 Data Science In The Real World Part 1
5 Data Science In The Real World Part 2
6 NLP In The Real World

NLP Pipeline
7 An Overview of NLP Methods
8 Text Preprocessing
9 Text Normalization
10 Word Embeddings
11 Build a Model, Transfer Learning, Testing & Evaluating a Model

Why Learn Python for NLP & Data Science
12 Top Programming Languages Used In Industry 2020
13 Top Programming Languages Used In Industry 2020 Part 2 PHP
14 Python in Industry 2020
15 Python vs R For Data Science & NLP

Google Colab – Setting Up
16 Open A New Colab Notebook
17 Open .IPYNB Files in Google Colab & Find The Resource Folders For This Course
18 Colab Settings

Python A Beginner’s Guide Part 1 (Optional)
19 Download Resource Workbook For This Section
20 What Are Variables And Lists
21 Create Variables
22 Create Lists
23 IF, ELIF, ELSE Statements
24 IF Statements with Multiple Conditions
25 Functions Part 1
26 Functions Part 2
27 Python Terminology Scripts, Modules, Packages Libraries
28 What Is A Module
29 Create A Module

Part Of Speech & Syntactic Parsing
30 Introducing This Section – Why learn these topics
31 Language Syntax – Noun Phrases
32 Syntax Constituents – NP, VP, PP
33 Context-Free Grammar
34 Part of Speech Tagging – NLTK Practical
35 Useful Applications of Parsers
36 Part 2 Useful Applications of Parsers

Tokenization & Regular Expressions
37 Overview of This Section
38 What is Tokenization Introduction to the Linguistic theory for tokenization
39 Linguistic theory for Word Segmentation
40 The Role of Cliticisation & Contractions in Tokenization
41 Tokenization with NLTK
42 Use Contractions Library To Expand Clitics
43 Introducing Regular Expressions
44 Word Segmentation using Python’s .split()
45 Sentence Segmentation using Python’s .split
46 ReGex Split Method re.split() Regular Expressions
47 Regex Substitute Method re.sub Regular Expressions
48 Search Method using Regex Regular Expressions
49 Part 1 Find All Emails in Contact Details Regular Expressions re.findall()
50 Part 2 Find All Emails in Contact Details Regular Expressions re.findall()

Grammar Syntax Rules & Parse Trees
51 Grammar Syntax
52 Grammar Syntax Part 2
53 How To Construct A Parse Tree
54 A Parse Tree Example
55 Part 1 Parse Tree Practical Project – Import Libraries
56 Part 2 Part Of Speech & Parse Functions Practical
57 Part 3 Output Parse Tree Practical

Stemming & Lemmatization
58 What is Stemming
59 Stemming with 3 NLTK Methods – Practical
60 Comparing Stemming Methods Porter, Lancaster & Snowball
61 What is Lemmatization
62 Lemmatization with NLTK – Practical
63 Wordnet Resource
64 Part 2 Lemmatization with NLTK
65 Part-of-Speech & Lemmatization Precision

Text Preprocessing Detailed Step-By-Step Practical Examples
66 Introducing The Project Preprocessing Tweets
67 Part 1 Preprocess Tweets Practical Load & Examine Dataset
68 Part 2 Extract Hashtags – Preprocess Tweets Practical
69 Part 3 Remove Usernames, Links, Non-ASCII & Use lower() – Tweets Practical
70 Part 4 Try Non-ASCII & Lower Case Functions on Sample Text
71 Part 5 Stopwords Removal
72 Part 6 Remove Email Addresses
73 Part 7 Remove Digits & Special Characters
74 Part 8 Clean Tweets In Dataset
75 Part 9 Translate Emoji & Extra Cleaning

Name Entity Recognition With SpaCy (NER)
76 Why Question Answering Systems Need NER
77 Why Chatbots Need NER
78 Part1 Load Spacy Pipeline Model
79 Part 2 SpaCy NER Attributes

Scrape Twitter Get Unlimited Tweets
80 Twint An Open Source Intelligence Tool
81 Twint Part 1 Setup & Installs
82 Part 2 Install Libraries
83 Part 3 Configure Twint
84 Part 4 Configure Twint for Pandas
85 Part 6 Search For Covid Tweets & Disney Cashtags
86 Part 8 Save Scraped Tweets To CSV File
87 Part 7 Add Username Configuration
88 Part 9 Search Within Geographic Coordinates
89 Part 10 Output Geographic Coordinate Search Results

Neural Networks
90 Biological Neurons
91 Biological Neuron Illustrated
92 Comparing Biological & Artificial Neuron Structures
93 Perceptron Model
94 Image Sources

Text Classification Used For Sentiment Analysis
95 Part 1 Steam Game Reviews Project Classifier for Sentiment Analysis
96 Part 2 Steam Game Reviews Classifier Explore Dataset
97 Part 3 Build Classifier Steam Game Reviews
98 Part 4 Split & Format Training Data Steam Game Reviews
99 Part 5 Prepare Training Data Steam Game Reviews
100 Part 6 Train the Model Steam Game Reviews
101 Part 7 Testing the Model Steam Game Reviews

Create A Web App To Deploy A Sentiment Analysis Model With Flask
102 Section Overview
103 Flask Project Part 1. Design The Web App’s Frontpage With HTML
104 Part 2 Load Data for Sentiment Analysis Model
105 Part 3 Adjust Dataset
106 Part 3 Train Test Split & TFiIDF Vectorization for Sentiment Model
107 Part 4 Random Forest Model
108 Part 5 Sklearn Pipeline
109 frontend

Hidden Markov Model
110 Introducing Markov Chains
111 Build A Probability Distribution Diagram
112 Create A State Diagram
113 Part 2 Create A State Diagram
114 Part 1 Markov Chain – Practical
115 Part 2 Define Markov Function
116 Part 3 Probability Matrix. Markov Chain – Practical
117 Part 4 Define Markov Chain Function – Practical
118 Part 5 Complete & Run Markov Chain Function – Practical

Extraction-Based Summarisation
119 Part 1 Create Summarizer
120 Part 2 Scrape Wikipedia With Beautiful Soup
121 Part 3 Addition Assignment Of Scraped Data
122 Part 4 Clean Scraped Wiki Data
123 Part 5 Tokenize
124 Part 6 The Key & Values Method
125 Part 7 Weighted Frequency
126 Part 8 Output The Summary

Bag Of Words VS Word Embeddings
127 Create A Bag Of Words Vector Representation
128 Bag Of Words VS Word Embeddings
129 Calculate Cosine Similarity BoW vs Word Embedding (Practical)
130 Part 2 Calculate Cosine Similarity BoW vs Word Embedding

Word Embedding Fundamentals
131 Introducing This Chapter
132 One Hot Encoding
133 One Hot Encoding Example
134 Word Document Matrix
135 Co-Occurence Matrix Concept
136 Co-Occurence Matrix (Practical)
137 Part 2 Co-Occurence Matrix (Practical)

Topic Modelling With Sklearn BBC News
138 BBC News NMF Part 1 Explore Dataset
139 Part 2 TF-IDF Vectorization
140 Part 3 Extract Topics with NMF Function
141 BBC News NMF Part 4 Assign Topics
142 BBC News NMF Part 5 Create Filtered Dataset, With Only The Articles Needed
143 BBC News NMF Part 6 Wordcloud With Filtered Articles

Create A Netflix Recommendation System
144 Part 1 Netflix Recommendation Project Data Exploration
145 Part 2 Preprocessing Netflix Recommendation Project
146 Part 3 Pre-trained Data Netflix Recommendation System
147 Part 4 Examine Similarities with most similar Function
148 Part 5 Write Vectorize() Function Netflix Recommendation System
149 Part 6 Make function to Get Most Similar Shows Netflix Recommendation Project
150 Part 7 Sorted() Function
151 Part 8 Final Recommendation Output

Fake News Detection Deep Learning With LSTM
152 FakeNews LSTM Part 1 Import Libraries, Load Dataset
153 FakeNews LSTM Part 2 Remove Null Values
154 FakeNews LSTM Part3 Preprocess Data
155 FakeNews LSTM Part4 One-Hot Encoding
156 Part 4 Pad Sequences
157 Part 5 Create Sequential Model With the Add() Method
158 Part 6 Calculate LSTM & Dense Layer Parameters

Rule-Based Chatbot for Banking Customer Service
159 Chatbot #1 Part1 – Rule-Based For Hard-Coded Exact Matching
160 Chatbot #1 Part 2 – Rule-Based For Hard-Coded Exact Matching
161 Chatbot #2 Rule-Based Using Keywords

Question-Answering System With ALBERT On SQuAD
162 Section Overview
163 Setting Up & Clone Repository
164 Get SQuAD Training Data
165 Train The ALBERT Model On SQuAD
166 Q&A Model Configurations
167 Setting Up The Model & Tensor Attributes
168 Adjust The Hugging Face Function SquadExample
169 Hugging Face Model Outputs
170 Hugging Face Compute Predictions Logits Method
171 Run Predictions Function
172 Try Questions On Custom Text
173 Resources For SQuAD

Speech Recognition Practical
174 Jetsons Cartoon, Google Assistant NLP & Sound Recognition
175 Convert Speech to Text – Load Resource File
176 Part 1 Convert Speech to Text
177 Part 2 Recognise Speech & Convert to Text