Natural Language Processing (NLP) is a hot topic into the Machine Learning field.

This course isfocused in practical approach with many examples and developing functional applications.This course starts explaining you, how to get the basic tools for codingand also making a review of the main machine learning concepts and algorithms. After that this course offers you a complete explanation of the main tools in NLP such as: Text Data Assemble, Text Data Preprocessing, TextData Visualization, Model Building and finally developing NLP applications.

Hot topics on NLPthat Iwill cover with practical applications on this course are:

- Regular expressions - Scrapping the web

- Textract library for extracting text content

- Sentence splitter and tokenization

- Stemming and Lemmatization

- Stop and rare word removal

- Part of Speech (POS) tagging

- Chunking

- N-grams

- Bag of Words: TfidfVectorizer

- Frequency Chart

- Co-occurence matrix

- Word cloud library

- Text similarity

- Text clustering

- Latent Semantic Analysis

- Topic Modeling

- Text Classification

- Sentiment Analysis

- Word2Vec library

- Recommender Systems: Collaborative Filtering

- Spam detector app

- Social Media Mining on Twitter

and much more!...

In this course you will find a concise review of the theory with graphical explanations and for coding ituses Python language and NLTK library.

Finally this courseoffers you many datasets and other resources for your practice andstudy.

The student has the opportunity to get a feedback from the instructor through Q&A forums, by email: [email protected] or by Twitter: @AILearningCQ