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Natural Language Processing: Machine Learning NLP In Python

Natural Language Processing: Machine Learning NLP In Python - A Complete Beginner NLP Syllabus. Practicals: Linguistics, Sentiment, Scrape Tweets, RNNs, Chatbot, Hugging Face & more!

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Bestseller, Created by Nidia Sahjara, Rajeev D. Ratan, English [Auto]

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

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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