Natural Language Processing: Machine Learning NLP In Python
What you'll 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
- 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
- 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.
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)
SQuAD - Stanford Question Answer Dataset. Train your Q&A Model on this awesome SQuAD dataset.
The topics outlined below are taught using practical Python projects!
Text Classification & Sentiment Analysis
Company Name Generator
Unsupervised Sentiment Analysis
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
Convert Speech to Text
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
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.
Nidia's specialities lie in war & conflict, data science and intelligence. She is a King's College Graduate and has a diverse background as her undergraduate studies include Computer Science and Civil & Environmental Engineering. She continued her postgraduate in Intelligence & Security. Her current research involves using NLP to analyse open-source data and opinion mining solutions. She is also a member of SHOC - the Strategic Hub for Organised Crime Research, as part of RUSI.
Hi I'm Rajeev, a Data Scientist, and Computer Vision Engineer.
I have a BSc in Computer & Electrical Engineering and an MSc in Artificial Intelligence from the University of Edinburgh where I gained extensive knowledge of machine learning, computer vision, and intelligent robotics.
I have published research on using data-driven methods for Probabilistic Stochastic Modeling for Public Transport and even was part of a group that won a robotics competition at the University of Edinburgh.
I launched my own computer vision startup that was based on using deep learning in education since then I've been contributing to 2 more startups in computer vision domains and one multinational company in Data Science.
Previously, I worked for 8 years at two of the Caribbean’s largest telecommunication operators where he gained experience in managing technical staff and deploying complex telecommunications projects.