4.40 (65 reviews)
☑ 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
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)
SQuAD - Stanford Question Answer Dataset. Train your Q&A Model on this awesome SQuAD dataset.
The topics outlined below are taught using practical Python projects!
Text Classification & Sentiment Analysis
Company Name Generator
Unsupervised Sentiment Analysis
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
Convert Speech to Text
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:
One Hot Encoding
Recurrent Neural Networks
Get introduced to Long short-term memory and the recurrent neural network architecture used in the field of deep learning.
Build models using LSTMs
Intro: NLP, Data Science & Machine Learning - Are they different?
Data Science In The Real World: Part 1
Data Science In The Real World: Part 2
NLP In The Real World
NLP Pipeline *Must Watch Section
An Overview of NLP Methods
Build a Model, Transfer Learning, Testing & Evaluating a Model
Why Learn Python for NLP & Data Science?
Top Programming Languages Used In Industry 2020
Top Programming Languages Used In Industry 2020 Part 2: PHP
Python in Industry 2020
Python vs R For Data Science & NLP
Google Colab - Setting Up
Open A New Colab Notebook
Open .IPYNB Files in Google Colab & Find The Resource Folders For This Course
Python: A Beginner's Guide Part 1 (Optional)
Download Resource Workbook For This Section
What Are Variables And Lists?
IF, ELIF, ELSE Statements
IF Statements with Multiple Conditions
Functions: Part 2
Python Terminology: Scripts, Modules, Packages Libraries
What Is A Module?
Create A Module
Part Of Speech & Syntactic Parsing
Introducing This Section - Why learn these topics?
Language Syntax - Noun Phrases
Syntax Constituents - NP, VP, PP
Part of Speech Tagging - NLTK Practical
Useful Applications of Parsers
Part 2: Useful Applications of Parsers
Tokenization & Regular Expressions
What is Tokenization? Introduction to the Linguistic theory for tokenization.
Linguistic theory for Word Segmentation.
How To Open The .IPYNB file For The Next Lecture (Optional)
Tokenization with NLTK
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.findall()
Part 2: Find All Emails in Contact Details | Regular Expressions re.findall()
Grammar Syntax Rules & Parse Trees
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
Stemming & Lemmatization
What is a Stemming?
Stemming with 3 NLTK Methods - Practical
Comparing Stemming Methods: Porter, Lancaster & Snowball
What is Lemmatization?
Lemmatization with NLTK - Practical
Part 2 Lemmatization with NLTK
Part-of-Speech & Lemmatization Precision
Text Preprocessing: Detailed Step-By-Step Practical Examples
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
Name Entity Recognition With SpaCy (NER)
Why Question Answering Systems Need NER
Why Chatbots Need NER
Part1: Load Spacy Pipeline Model
Part 2: SpaCy NER Attributes
Scrape Twitter: Get Unlimited Tweets
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 5: Cashtags
Part 6: Search For Covid Tweets & Disney Cashtags
Part 7: Add Username Configuration
Part 8: Save Scraped Tweets To CSV File
Part 9: Search Within Geographic Coordinates
Part 10: Output Geographic Coordinate Search Results
Biological Neuron Illustrated
Comparing Biological & Artificial Neuron Structures
Text Classification Used For Sentiment Analysis
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
Hidden Markov Model
Introducing Markov Chains
Build A Probability Distribution Diagram
Create A State Diagram
Part 2: Create A State Diagram
Markov Chain - Practical
Part 2: Probability Matrix. Markov Chain - Practical.
Part 3: Define Markov Chain Function - Practical.
Part 4: Complete & Run Markov Chain Function - Practical.
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 8: Output The Summary
Bag Of Words VS Word Embeddings
Create A Bag Of Words Vector Representation
Bag Of Words VS Word Embeddings
Calculate Cosine Similarity: BoW vs Word Embedding (Practical)
Part 2: Calculate Cosine Similarity: BoW vs Word Embedding
Word Embedding Fundamentals
Introducing This Chapter
One Hot Encoding
One Hot Encoding Example
Word Document Matrix
Co-Occurence Matrix Concept
Co-Occurence Matrix (Practical)
Part 2: Co-Occurence Matrix (Practical)
Topic Modelling With Sklearn: BBC News
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
Create A Netflix Recommendation System
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 Vectorize() Function | Netflix Recommendation System
Part 6: Make function to Get Most Similar Shows | Netflix Recommendation Project
Part 7: Sorted() Function
Part 8: Final Recommendation Output
Fake News Detection: Deep Learning With LSTM
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 Add() Method
Rule-Based Chatbot for Banking Customer Service
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
Question-Answering System With ALBERT On SQuAD
Setting Up & Clone Repository
Get SQuAD Training Data
Train The ALBERT Model On SQuAD
Q&A 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
Speech Recognition Practical
Jetsons Cartoon, Google Assistant: NLP & Sound Recognition
Convert Speech to Text - Load Resource File
Part 1: Convert Speech to Text
Part 2: Recognise Speech & Convert to Text
It is a very basic and introductory course. Most of the time when coding along, the teacher write code without explaining why we are coding this or that.
Fantastic course! So many neat little miscellaneous tricks taught along the way, in addition to the NLP content. Will definitely be taking other courses by Nidia and Rajeev :)
This course was definitely a great match for me and was impeccable.It is very well put together, easily understandable, and easy to follow therefore, learning and understanding for a beginner is a success. Firstly, The course enables different types of learners such as visual learners to grasp the infortantion as Nidia uses different teaching approaches such as directing and discussing style of teaching. This not only keeps you engrossed and attentive but keeps you interested in the content being presented as the layout feels very light and fun yet clear and concise , thus comfortable to follow and not difficult to keep up with. Secondly, in addition to this, it has been a great experience as Nidia is a very thoughtful in ensuring students get the best experince possible as it is shown that she puts a lot of thought into how she presents the material. The course was very engaging, useful, and motivating in that she encouraged me to have a better positive view on NLP. The course enables you to learn at your own pace and to have a better appreciation for NLP. It gave me a better perspective and was such a life changing experience. It has helped in generating my vision and goals and can be beneficial in the business world for me by developing new stratgies for problem solving, strengthening leadership capabilities and stepping over my own mindset, managing change and even be a better influence on employees or persons around me. I think it can have a substantial positive impact on persons and can also be such a life changing experience for others. When choosing a course on NLP, it is important to note that you should be comfortable and motivated to learn and get a positive outcome from it, and this definitely does that. I Definitely reccommend!
The explanation is very clear. Using simple illustrations like stars, buckets, etc makes the course more understandable.
Learning so many valuable things in this course, it's been great so far, highly recommend it for beginners to NLP!
Absolutely loving how comprehensive and well taught this course is. It's the only course I've found that actually teaches you the core grammatical foundation of NLP which helps so much in making you understand more complicated NLP work! Great course!
The course gets into the linguistic stuff quite well . As a newbie I got this course for the chat bot and the Twitter scraping but I’m eagerly looking forward to learning linguistics . Haven’t found other courses with this stuff before. Very knowledgeable passionate instructor , superb clarity and content .