4.31 out of 5
4.31
235 reviews on Udemy

Natural Language Processing: Machine Learning NLP In Python

A Complete Beginner NLP Syllabus. Practicals: Linguistics, Flask,Sentiment, Scrape Tweets, Chatbot, Hugging Face & more!
Instructor:
Nidia Sahjara
2,008 students enrolled
English [Auto]
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
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

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

You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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Working hours

Monday 9:30 am - 6.00 pm
Tuesday 9:30 am - 6.00 pm
Wednesday 9:30 am - 6.00 pm
Thursday 9:30 am - 6.00 pm
Friday 9:30 am - 5.00 pm
Saturday Closed
Sunday Closed