Natural Language Processing: Machine Learning NLP In Python-0daytown

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

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

Requirements
No Tedious Installs
No previous programming knowledge necessary. The lectures slowly explain the python syntax as you code alone with me.
New to Python: you get explanations of the code as you code along with me but not only that – theory slides explain concepts to help you understand what’s going on behind the code.
No data science knowledge required: lectures teach how to work with data and key modelling concepts.
No NLP knowledge required. Linguistic concepts are taught to give a strong foundation of NLP even before you get into practical coding. This helps you to grasp NLP modelling techniques and cleaning concepts better.

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

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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Natural Language Processing: Machine Learning NLP In Python

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