Hi There👋!

In this article, I'll cover the essential topics and also some resources that are required to get started in NLP (Natural Language Processing). The choice of language for this article is Python. If Python is not your cup of tea, you can still follow this article, but replace the exercises with a language of your choice.

At the end of this article, you'll also find the best articles, newsletters and open-source libraries to follow to keep yourself updated in this ever-changing world! 💯

This article doesn't cover what NLP is about. It provides you with a constructive roadmap along with resources to get you started.

First off, please don't pay for courses online. Instead, just follow the free-resources that have been mentioned in the article's roadmap.

Let's Start! 🥁

• NLP, in theory- To understand what NLP is about and its applications - Start with this awesome blog at MonkeyLearn
  • MonkeyLearn - Definitive Guide to NLP
• Text Preprocessing in NLP - To understand the nuances of pre-processing that is required to start working with data.
  • Kavita-Ganesan - Theory - Text Preprocessing for Machine Learning & NLP
  • Kavita-Ganesan - Practice in Python - Practice Examples mentioned in the above article
• Word Embedding - To convert words into vectors, which can then be used to feed as input for mathematical models (Machine Learning/Deep Learning Models)
  • Educative - CountVectorizer - CountVectorizer in Python
  • TowardsDataScience - TF IDF - TFIDF Python Example
  • Stack Abuse - Optional - Implementing Word2Vec with Gensim Library in Python
• Text Classifier - Time to build a something interesting and put into practice all you have learned so far.
  • Kavita Ganesan - Build Your First Text Classifier in Python with Logistic Regression
• Sentiment Analysis - Let's see if we can use NLP to classify a review as positive or negative polarized.
  • Real Python - Use Sentiment Analysis With Python to Classify Movie Reviews
• Topic Modelling - We'll now find various topics from a corpus of text. Trust me it's pretty simple.
  • TowardsDataScience - LDA Model -Topic Modeling and Latent Dirichlet Allocation (LDA) in Python
• NER - Named Entity Recognition. Let' see how we can find various entities (Person, Organization etc) using spaCy in Python
  • TowardsDataScience - Named Entity Recognition with NLTK and SpaCy
• Some optional concepts like POS, Neural Co-referencing and Dependency Parser.
  • Parts of Speech using spaCy
  • Neural Co-reference using spaCy
  • Dependency Parser using spaCy

That brings an end to the list of resources to get started with NLP for this article. The resources above take you from beginner to directly intermediate level.

Looking for more?🧐

• Deep Learning for NLP: An Overview of Recent Trends
• Deep Learning Notes on NLP

Open-source Libraries ❤️

• spaCy
• NLTK
• Gensim
• TextBlob
• CoreNLP

Blogs to follow 📚

• Detailed Blogs about - MonkeyLearn
• Community for ML- Sub-reddit for ML
• Research Oriented Updates
  • Weights & Biases
  • Distill

Newsletters🗞

• Data Elixir - Data Science and Visualization updates
• O’Reilly Data & AI Newsletter - Tutorials, updates and Research Papers
• PyCoder's Weekly - Updates, Projects, Jobs in the field of Python.

YouTube Channels🎥

• Two Minutes Paper - Research papers explained in two minutes.
• Siraj Raval - Concepts explained in a simpler way
• Data School - Concepts and Implementations.

If you have reached so far reading, and like what you've read. Please spread the love ❤️ , react👍 , and comment💬! It encourages me to write more blogs on the lines of Machine Learning, Cloud Development, Python Development and Deep Learning!

Until next time! Good day and Cheers 😎