• Introduction

• How To Start This Course (Must Watch)

• Introducing NLP

• Data Science In The Real World: Part 1

• Data Science In The Real World: Part 2

• NLP In The Real World

• An Overview of NLP Methods

• Text Preprocessing

• Text Normalization

• Word Embeddings

• Build a Model, Transfer Learning. Testing & Evaluating a Model

• Python Terminology: Scripts, Modules, Packages Libraries

• What Is A Module?

• Create A Module

• Iniroducing This Section - Why learn these topics?

• Language Syntax - Noun Phrases y

• Syntax Constituents - NP VP PP

• Context-Free Grammar Part of Speech Tagging - NLTK Practical

• Useful Applications of Parsers

• Part 2: Useful Applications of Parsers

• Overview of This Section

• What is Tokenization? Introduction to the Linguistic theory for tokenization.

• Linguistic theory for Word Segmentation.

• The Role of Citicisation & Contractions in Tokenization

• Tokenization with NLTK

• Use Contractions Library To Expand Clitics

• Top Programming Languages Used In Industry 2020 Top Programming Languages Used In Industry 2020 Part 2: PHP Python in Industry 2020 Python vs For Data Science & NLD

• Open A New Colab Notebook

• Open.IPYNB Files in Google Colab & Find The Resource Folders For This Course

• Colab Settings

• Download Resource Workbook For This Section

• What Are Variables And Lists?

• Create Variables

• Create Lists

• IF, ELIF, ELSE Statements

• Statements with Multiple Conditions

• Functions: Part 1

• Functions: Part 2

• Functions: Part 2

• Python Terminology: Scripts, Modules, Packages Libraries

• What Is A Module?

• Create A Module

• Introducing This Section - Why learn these topics? Language Syntax - Noun Phrases

• Syntax Constituents - NP, VP, PP

• context-Free Grammar

• Part of Speech Tagging - NLTK Practical Useful Applications of Parsers

• Part 2: Useful Applications of Parsers

• Overview of This Section

• What is Tokenization? Introduction to the Linguistic theory for tokenization.

• Linguistic theory for Word Segmentation.

• The Role of Criticisation & Contractions in Tokenization

• Tokenization with NLTK

• Use Contractions Library To Expand Clitics

• Introducing Regular Expressions

• Word Segmentation using Python's.split() Sentence Segmentation using Python's split

• ReGex Split Method re.split() Regular Expressions

• Regex Substitute Method re.sub Regular Expressions

• Search Method using Regex re search Regular Expressions

• Part 1: Find All Emails in Contact Details | Regular Expressions re.findallo)

• Part 2: Find All Emails in Contact Details | Regular Expressions refindallo

• Grammar Syntax

• Grammar Syntax Part 2

• How To Construct A Parse Tree

• A Parse Tree Example

• Part 1: Parse Tree Practical Project - Import Libraries Part 2: Part Of Speech & Parse Functions | Practical

• Part 3: Output Parse Tree Practical

• What is Stemming?

• Stemming with 3 NLTK Methods - Practical

• Comparing Stemming Methods: Porter. Lancaster & Snowball

• What is Lernmatization?

• Lemmatization with NLTK - Practical

• Wordnet Resource

• Part 2 Lemmatization with NLTK Part-of-Speech & Lemmatization Precision

• Introducing The Project: Preprocessing Tweets

• Part 1: Preprocess Tweets Practical Load & Examine Dataset

• Part 2: Extract Hashtags - Preprocess Tweets Practical

• Part 3: Remove Usernames, Links, Non-ASCII & Use lower - Tweets Practical

• Part 4: Try Non-ASCII & Lower Case Functions on Sample Text

• Part 5: Stopwords Removal

• Part 6: Remove Email Addresses

• Part 7: Remove Digits & Special Characters

• Part 8: Clean Tweets In Dataset

• Part 9: Translate Emoj & Extra Cleaning

• Why Question Answering Systems Need NER

• Why Chatbots Need NER

• Part1: Load Spacy Pipeline Model Part 2: Spa y NER Attributes

• Twint An Open Source Intelligence Tool

• Twint Part 1: Setup & Installs

• Part 2: Install Libraries Part 3: Configure Twint Part 4: Configure Twint for Pandas Part 6: Search For Covid Tweets & Disney Cashtags

• Part 8: Save Scraped Tweets To OSV File

• Part 7: Add Username Configuration Part 9: Search Within Geographic Coordinates

• Part 10: Output Geographic Coordinate Search Results

• Biological Neurons

• Biological Neuron Illustrated

• Comparing Biological & Artificial Neuron Structures Perceptron Model

• Image Sources

• Part 1 Steam Game Reviews Project Classifier for Sentiment Analysis

• Part 2: Steam Game Reviews Classifier | Explore Dataset

• Part 3: Build Classifier | Steam Game Reviews

• Part 4 Split & Format Training Data Steam Game Reviews |

• Part 5 | Prepare Training Data | Steam Game Reviews | Part 6 | Train the Model Steam Game Reviews|

• Part 7: Testing the Model Steam Game Reviews

• Section Overview

• Flask Project: Part 1. Design The Web App's Frontpage With HTML.

• Part 2: Load Data for Sentiment Analysis Model

• Part 3: Adjust Dataset

• Part 3: Train Test Split & TFilDF Vectorization for Sentiment Model

• Part 4: Random Forest Model

• Part 5: Sklearn Pipeline

• Introducing Markov Chains

• Build A Probability Distribution Diagram

• Create A State Diagram

• Part 2: Create A State Diagram

• Part 1: Markov Chain - Practical

• Part 2: Define Markov Function

• Part 3: Probability Matrix Markov Chain - Practical. Part 4: Define Markov Chain Function - Practical

• Part 5: Complete & Run Markov Chain Function - Practical.

• Introducing This Chapter

• One Hot Encoding

• One Hot Encoding Example

• Word Document Matrix

• Co-Occurence Matrix Concept

• Co-Occurence Matrix (Practical)

• Part 2:00-Occurence Matrix (Practical)

• BBC News NMF Part 1: Explore Dataset

• Part 2: TF-IDF Vectorization

• Part 3: Extract Topics with NMF Function

• BBC News NMF Part 4: Assign Topics

• BBC News NMF Part 5: Create Filtered Dataset, With Only The Articles Needed

• BBC News NMF Part 6: Wordcloud With Filtered Articles

• Part 1: Create Summarizer

• Part 2: Scrape Wikipedia With Beautiful Soup

• Part 3: Addition Assignment Of Scraped Data

• Part 4: Clean Scraped Wiki Data

• Part 5: Tokenize

• Part 6: The Key & Values Method

• Part 7: Weighted Frequency

• Part 3: Output The Summary

• Create 4 Bag Of Words Vector Representation

• Bag Of Words VS Word Embeddings

• Calculate Cosine Similarity: BoW vs Word Embedding (Practical)

• Part 2: Calculate Dosine Similarity: BoW vs Word Embedding

• Part 7: Remove Digits & Special Characters

• Part 8: Clean Tweets In Dataset

• Part 9: Translate Emoj & Extra Cleaning

• Why Question Answering Systems Need NER

• Why Chatbots Need NER

• Part1: Load Spacy Pipeline Model Part 2: Spa y NER Attributes

• Twint An Open Source Intelligence Tool

• Twint Part 1: Setup & Installs

• Part 2: Install Libraries Part 3: Configure Twint Part 4: Configure Twint for Pandas Part 6: Search For Covid Tweets & Disney Cashtags

• Part 8: Save Scraped Tweets To OSV File

• Part 7: Add Username Configuration Part 9: Search Within Geographic Coordinates

• Part 10: Output Geographic Coordinate Search Results

• Biological Neurons

• Biological Neuron Illustrated

• Comparing Biological & Artificial Neuron Structures

• Perceptron Model

• Image Sources

• Part 1 Steam Game Reviews Project Classifier for Sentiment Analysis

• Part 2: Steam Game Reviews Classifier Explore Dataset

• Part 3: Build Classifier | Steam Game Reviews

• Part 4 Split & Format Training Data Steam Game Reviews

• Part 5 | Prepare Training Data Steam Game Reviews | Part 6 | Train the Model Steam Game Reviews |

• Part 7: Testing the Model Steam Game Reviews

• Flask Project: Part 1. Design The Web App's Frontpage With HTML.

• Part 2: Load Data for Sentiment Analysis Model

• Part 3: Adjust Dataset

• Part 3: Train Test Split & TFilDF Vectorization for Sentiment

• Part 4: Random Forest Model

• Part 5: Sklearn Pipeline

• Introducing Markov Chains

• Build A Probability Distribution Diagram

• Create A State Diagram Part 2: Create A State Diagram

• Part 1: Markov Chain - Practical

• Part 2: Define Markov Function

• Part 3: Probability Matrix. Markov Chain - Practical.

• Part 4: Define Markov Chain Function - Practical.

• Part 5: Complete & Run Markov Chain Function - Practical.

• Part 1: Create Summarizer

• Part 2: Scrape Wikipedia With Beautiful Soup

• Part 6: The Values Method

• Part 7: Weighted Frequency

• Part 8: Output The Summary

• Create 4 Bag Of Words Vector Representation

• Bag Of Words VS Word Embeddings Calculate Cosina Similarity: BoW vs Word Embedding (Practical) Part 2: Calculate Dosine Similarity: BoW vs Word Embedding

• Part 2: Calculate Cosine Similarity: Bow vs Word Embedding

• Introducing This Chapter

• One Hot Encoding

• One Hot Encoding Example

• o Word Document Matrix

• Co-Occurence Matrix Concept

• Co-Occurence Matrix (Practical)

• Part 2: Co-Occurence Matrix (Practical)

• BBC News NMF Part 1: Explore Dataset Part 2: TF-IDF Vectorization

• Part 3: Extract Topics with NMF Function

• BBC News NMF Part 4: Assign Topics

• BBC News NMF Part 5: Create Filtered Dataset, With Only The Articles Needed

• BBC News NMF Part 6: Wordcloud With Filtered Articles

• Part 1: Netflix Recommendation Project: Data Exploration

• Part 2: Preprocessing | Netflix Recommendation Project

• Part 3: Pre-trained Data | Netflix Recommendation System

• Part 4: Examine Similarities with most_similar Function

• Part 5: Write Vectorized Function | Netflix Recommendation System

• Part 6: Make function to Get Most Similar Shows Neiflix Recommendation Project

• Part 7: Sorted Function

• Part 8: Final Recommendation Output

• FakeNews LSTM Part 1: Import Libraries, Load Dataset

• FakeNews LSTM Part 2 Remove Null Values

• FakeNews LSTM Part3: Preprocess Data

• FakeNews LSTM Part4: One-Hot Encoding

• Part 4: Pad_Sequences

• Part 5: Create Sequential Model With the Addo Method

• Chatbot #1: Part1 - Rule-Based For Hard-Coded Exact Matching

• Chatbot #1: Part 2 - Rule-Based For Hard-Coded Exact Matching

• Chatbot #2: Rule-Based Using Keywords

• Setting Up & Clone Repository Get SQUAD Training Data

• Train The ALBERT Model On SQUAD

• Q&Model Configurations

• Setting Up The Model & Tensor Attributes

• Adjust The Hugging Face Function: SquadExample

• Hugging Face Model Outputs

• Hugging Face Compute_Predictions_Logits Method

• Run Predictions Function

• Try Questions On Custom Text Resources For SQUAD

• Jetsons Cartoon, Google Assistant: NLP & Sound Recognition

• Convert Speech to Text - Load Resource File

• Part 1: Convert Speech to Text Part 2: Pecognise Speech & Convert to Text