Book Image

Hands-on Scikit-learn for Machine Learning [Video]

By : Farhan Nazar Zaidi
5 (1)
Book Image

Hands-on Scikit-learn for Machine Learning [Video]

5 (1)
By: Farhan Nazar Zaidi

Overview of this book

Scikit-learn is arguably the most popular Python library for Machine Learning today. Thousands of Data Scientists and Machine Learning practitioners use it for day to day tasks throughout a Machine Learning project’s life cycle. Due to its popularity and coverage of a wide variety of ML models and built-in utilities, jobs for Scikit-learn are in high demand, both in industry and academia. If you’re an aspiring machine learning engineer ready to take real-world projects head-on, Hands-on Scikit-Learn for Machine Learning will walk you through the most commonly used models, libraries, and utilities offered by Scikit-learn. By the end of the course, you will have a set of ML problem-solving tools in the form of code modules and utility functions based on Scikit-learn in one place, instead of spread over several books and courses, which you can easily use on real-world projects and data sets. All the code and supporting files for this course are available on Github at: https://github.com/PacktPublishing/Hands-on-Scikit-learn-for-Machine-Learning-V-
Table of Contents (8 chapters)
Chapter 8
Handling Text Data with Scikit-learn
Content Locked
Section 2
Using Stop-Words and TF-IDF for Sentiment Analysis
This video you will learn how to use stop-words and TF-IDF for sentiment - Remove stop-words from dataset representation - Applying TF-IDF scaling on IMDB dataset - Apply different ML models for sentiment analysis on IMDB dataset