Introduction to Natural Language Processing with Scikit-learn

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Andrea Giussani

37:35

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1. Introduction.mp4

03:23

2. Preprocessing Text Data.mp4

13:56

3. Term Frequency-Inverse Document Frequency (TF-IDF).mp4

10:02

4. Text Classification.mp4

09:02

5. Conclusion.mp4

01:12

Description

This lesson covers the basic techniques you need to know in order to fit a Natural Language Processing Machine Learning pipeline using scikit-learn, a machine learning library for Python.

Learning Objectives

Learn about the two main scikit-learn classes for natural language processing: CountVectorizer and TfidfVectorizer
Learn how to create Bag-of-Words (boW) representations and TF-IDF representations
Learn how to create a machine learning pipeline to classify BBC news articles into different categories

Intended Audience

This lesson is intended for anyone who wishes to understand how NLP works and, more particularly, how to implement it using scikit-learn.

Prerequisites

To get the most out of this lesson, you should already have an understanding of the Python programming language.

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Natural Language Processing

Andrea Giussani

Instructor's Courses

Andrea is a Data Scientist at Cloud Academy. He is passionate about statistical modeling and machine learning algorithms, especially for solving business tasks. He holds a PhD in Statistics, and he has published in several peer-reviewed academic journals. He is also the author of the book Applied Machine Learning with Python.

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