Machine Learning: Natural Language Processing in Python (V2)

Hello friends!

Welcome to Machine Learning: Natural Language Processing in Python (Version 2).

This is a massive 4–in–1 course covering:

1) Vector models and text preprocessing methods

2) Probability models and Markov models

3) Machine learning methods

4) Deep learning and neural network methods

In part 1, which covers vector models and text preprocessing methods, you will learn about why vectors are so essential in data science and artificial intelligence. You will learn about various techniques for converting text into vectors, such as the CountVectorizer and TF–IDF, and you’ll learn the basics of neural embedding methods like word2vec, and GloVe.

You’ll then apply what you learned for various tasks, such as:

Text classification

Document retrieval / search engine

Text summarization

Along the way, you’ll also learn important text preprocessing steps, such as tokenization, stemming, and lemmatization.

You’ll be introduced briefly to classic NLP tasks such as parts–of–speech tagging.

In part 2, which covers probability models and Markov models, you’ll learn about one of the most important models in all of data science and machine learning in the past 100 years. It has been applied in many areas in addition to NLP, such as finance, bioinformatics, and reinforcement learning.