Natural Language Processing: Machine Learning NLP In Python




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

A Complete Beginner NLP Syllabus. Practicals: Linguistics, Flask,Sentiment, Scrape Tweets, Chatbot, Hugging Face & more!

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What you will learn
  • Use Flask to Deploy A Sentiment Analysis Model To A Web Interface
  • Libraries: Hugging Face, NLTK, SpaCy, Keras, Sci-kit Learn, Tensorflow, Pytorch, Twint
  • Linguistics Foundation To Help Learn NLP Concepts

Rating: 4.37037

Level: Beginner Level

Duration: 19.5 hours

Instructor: Nidia Sahjara


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