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NLP Natural Language Processing Fundamentals in Python

NLP Natural Language Processing Fundamentals in Python

Learn the fundamentals of NLP and Text Mining by using NLTK, Word2Vec, Neural Networks and Sentiment Analysis

What you'll learn

  • 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 Sentiment Analysis Tool
  • Train a Machine Learning Model

Requirements

  • Internet Access
  • Computer with at least 4 GB of RAM

Description

Welcome to your first step into the Natural Language Processing and Text Mining world! This is your risk-free approach (30-day refund policy) to delve deep into the fundamentals which Google, Amazon and Microsoft base themselves on when working with text data.

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 beginners - 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 sentiment classifier (Sentiment Analysis);
  • Representing Sentences as Tabular Data;

After finishing the course you should able to build your own basic 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 strategies and techniques that most companies have used when they started their NLP applications.

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