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Natural Language Processing (NLP) in Python for Beginners

 
Natural language toolkit (NLTK) is the most popular library for natural language processing (NLP) which was written in Python and has a big community behind it. NLTK also is very easy to learn, actually, it's the easiest natural language processing (NLP) library that you'll use.
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What you'll learn
  • Learn complete text processing with Python
  • Learn how to extract text from PDF files
  • Use Regular Expressions for search in text
  • Use SpaCy and NLTK to extract complete text features from raw text
  • Use Latent Dirichlet Allocation for Topic Modelling
  • Use Scikit-Learn and Deep Learning for Text Classification
  • Learn Multi-Class and Multi-Label Text Classification
  • Use Spacy and NLTK for Sentiment Analysis
  • Understand and Build word2vec and GloVe based ML models
  • Use Gensim to obtain pretrained word vectors and compute similarities and analogies
  • Learn Text Summarization and Text Generation using LSTM and GRU
Description

Welcome to KGP Talkie's Natural Language Processing course. It is designed to give you a complete understanding of Text Processing and Mining with the use of State-of-the-Art NLP algorithms in Python.

We Learn Spacy and NLTK in details and we will also explore the uses of NLP in real-life. This course covers the basics of NLP to advance topics like BERT, DistilBERT, and FastText.

In this course, we will start from level 0 to the advanced level.

We will start with basics like what is machine learning and how it works. Thereafter I will take you to Python, Numpy, and Pandas crash course. If you have prior experience you can skip these sections. The real game of NLP will start with Spacy Introduction where I will take you through various steps of NLP preprocessing. We will be using Spacy and NLTK mostly for the text data preprocessing.

In the next section, we will learn about working with Files for storing and loading the text data. This section is the foundation of another section on Complete Text Preprocessing. I will show you many ways of text preprocessing using Spacy and Regular Expressions. Finally, I will show you how you can create your own python package on preprocessing. It will help us to improve our code writing skills. We will be able to reuse our code systemwide without writing codes for preprocessing every time. This section is the most important section.

Then, we will start the Machine learning theory section and a walkthrough of the Scikit-Learn Python package where we will learn how to write clean ML code. Thereafter, we will develop our first text classifier for SPAM and HAM message classification. I will be also showing you various types of word embeddings used in NLP like Bag of Words, Term Frequency, IDF, and TF-IDF. I will show you how you can estimate these features from scratch as well as with the help of the Scikit-Learn package.

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