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Natural Language Processing: Machine Learning NLP In Python


Natural Language Processing: Machine Learning NLP In Python - 
Fundamental Theory & Beginner Python Projects: Sentiment, Scrape Tweets, RNN/LSTM, Chatbot, Audio To Text, Deep Learning
  • Bestseller
  • Created by Nidia Sahjara, Rajeev D. Ratan
  • English [Auto]

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What you'll learn

  • 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)
Part-of-Speech Tagging
SQuAD - Stanford Question Answer Dataset. Train your Q&A Model on this awesome SQuAD dataset.
Sci-kit Learn
Hugging Face
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:
One Hot Encoding
Deep Learning
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
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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