Natural Language Processing & Deep Learning: Zero to Hero

What you'll learn
Libraries: Tensorflow, Pytorch, NLTK, SpaCy, Sci-kit Learn, Twint
Linguistics Foundation To Help Learn NLP Concepts
Deep Learning: Neural Networks, RNN, LSTM Theory & Practical Projects
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
Scrape Unlimited Tweets Using An Open Source Intelligence Tool
Speech Recognition
LSTM Fake News Detector
Context-Free Grammar Syntax
Scrape Wikipedia & Create An Article Summarizer

Description
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

Libraries:

NLTK

Sci-kit Learn

Tensorflow

Pytorch

SpaCy

DeepPavlov

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

Open Domain Question Answering (like asking Google)

Closed Domain Question Answering (Like asking a Restaurant-Finder bot)

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

Who this course is for:
Anyone who is curious about data science & NLP
Those who are in the Business & Marketing world - learn use NLP to gain insight into customers & products. Can help at interviews & job promotions.
If you intend to enrol in an NLP/Data Science course but are a total newbie, complete this course before to avoid being lost in class since it can seem overwhelming if classmates already have a foundation in Python or Datascience.

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