Feature Engineering for NLP in Python

via

DataCamp

Course Features

Duration

4 hours

Delivery Method

Online

Available on

Limited Access

Accessibility

Mobile, Desktop, Laptop

Language

English

Subtitles

English

Level

Intermediate

Teaching Type

Self Paced

Video Content

4 hours

Course Description

This course will show you how to extract useful information from text and transform it into a format suitable for ML modeling. Named entity recognition and POS tagging will be covered. You will also learn how to compare documents. Additionally, you will learn how to predict movie reviews as well as create Ted Talk recommendations. Learn how to extract key features from any text, and how to solve the most challenging problems in data science.

Course Overview

Virtual Labs

International Faculty

Post Course Interactions

Hands-On Training,Instructor-Moderated Discussions

Skills You Will Gain

Prerequisites/Requirements

Introduction to Natural Language Processing in Python

Supervised Learning with scikit-learn

Data Manipulation with pandas

What You Will Learn

Following the course, you will be able to engineer critical features out of any text and solve some of the most challenging problems in data science!

In the process, you will predict the sentiment of movie reviews and build movie and Ted Talk recommenders

Learn techniques to extract useful information from text and process them into a format suitable for machine learning

Course Content

Expand all sections

Module 1: Basic features and readability scores

Module 2: Text preprocessing, POS tagging and NER

Module 3: N-Gram models

Module 4: TF-IDF and similarity scores

Course Instructors

Rounak Banik

Data Scientist at Fractal Analytics

Rounak is a Young India Fellow and the author of the book, Hands-on Recommendation Systems with Python. He currently works as a Data Science Fellow with the QuantumBlack division of McKinsey and Comp...