Natural Language Processing (NLP) in Python

Learn NLP and Text Mining by creating word vectors and do sennt analysis using Word2Vec, NLTK and Neural Networks

Dealing with Strings in Python

Working with the Natural Language Toolkit Library

Understanding the Intuition behind Word Vectors

Pre-Processing Text for Analytics

Understanding Text Vectorization

Train a Neural Network to generate Word Embeddings

Obtain Text Data from Web Pages

Read Files with Textual Data

Developing a Sennt Analysis Tool

Train a Machine Learning Model

Internet Access

Computer with at least 4 GB of RAM

Natural Language Processing is one of the most exciting fields in Data Science and Analytics nowadays. The ability to make a computer understand words and phrases is a technological innovation that brought a huge transformation to tasks such as Information Retrieval, Translation or Text Classification.

In this course we are going to learn the fundamentals of working with Text data in Python and discuss the most important techniques that you should know to start your journey in Natural Language Processing. This course was designed for absolute bners - meaning that everything regarding NLP that we are going to speak in the course will be explained during the lectures, assuming that the student does not have any prior knowledge in the subject.

Don't worry if you don't know Python code by heart - this course also contains a Python crash course that will help you to get familiar with the language and support the rest of the use cases that we will develop with Python throughout the lectures. In this course we are going to approach the following concepts:

Working with the raw material of Natural Language Processing - strings - in Python;

Tokenizing Sentences and Documents;

Stemming and Lemmatizing words;

Training machine learning models using text;

Extracting the Part-of-Speech Tag from words in a sentence;

Extracting Text Data from a Web Page;

Training a Neural Network to extract Word Embeddings;

Developing your own sennt classifier (Sennt Analysis);

Representing Sentences as Tabular Data;

After finishing the course you should able to build your own NLP applications and also understand most of the fundamental concepts that are the base of most NLP algorithms. This will give you the flexibility to study more advanced Natural Language Processing concepts and also enable you to get familiar with the strats and techniques that most companies have used when they started their NLP applications.

Join me in this exciting NLP journey and I'm looking forward to see you in the course!

Bner Python Developers

Experienced Python Developers Interested in learning NLP

Data Eeers

Data Scientists

Business Analysts



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