Applications of Statistical Learning with Python [Video]

Applications of Statistical Learning with Python [Video]

Curtis Miller

Turn practical hands-on projects such as language processing, computer vision, sentiment analysis, and text processing into useful application in Python to take your skills to another level!
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Video Description

Scientists have been increasingly using Python for data analysis tasks such as natural language processing and computer vision, and a new wave of modules and packages, make programming these tasks easier than ever. In this course, you’ll dive into Natural Language Processing and get familiar with the NLTK package. This video course is filled with real-world, practical examples that show you Python’s true power as a programming language for data analysis.

You’ll learn to read text in documents using different models, and employ sentiment analysis to predict the author’s intent. You’ll also see how to employ Python to read images and for computer vision. Once you’ve learned to employ specific Python packages and syntax for these tasks, you’ll explore case studies that put forth solid real-world examples on spam filtering and analyzing human emotions through a dictionary of images.

The code bundle for this video course is available at : https://github.com/PacktPublishing/Applications-of-Statistical-Learning-with-Python

Style and Approach

This course contains in-depth content balanced with tutorials that put theory into practice. This course will give you both a theoretical understanding and practical exp with examples that will allow you indulge in the art of statistical modeling and analysis using the Python programming language.

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What You Will Learn

  • Look for specific signs and intents using natural language processing
  • Find specific points and figures within images using computer vision
  • Detect spam by analyzing data within emails
  • Detect emotion by reading patterns within images
  • Employ different filters and transforms to analyze and manipulate data

Authors

Video Details

ISBN 139781788295499
Course Length1 hour 44 minutes
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