Natural Language Processing: Machine Learning NLP In Python

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

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

Libraries:

• 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