Applications of Statistical Learning with Python [Video]

More Information
  • 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

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 :

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.

  • Solid examples to help you wrap your head around the application of statistical modeling using Python
  • Filled with real-world, practical examples that show you how to jump in and start building effective prediction models
  • Covers the important concepts such sentiment analysis and test processing with the help of Python
Course Length 1 hour 44 minutes
ISBN 9781788295499
Date Of Publication 28 May 2018


Curtis Miller

Curtis Miller is a doctoral candidate at the University of Utah studying mathematical statistics. He writes software for both research and personal interest, including the R package (CPAT) available on the Comprehensive R Archive Network (CRAN). Among Curtis Miller's publications are academic papers along with books and video courses all published by Packt Publishing. Curtis Miller's video courses include Unpacking NumPy and Pandas, Data Acquisition and Manipulation with Python, Training Your Systems with Python Statistical Modelling, and Applications of Statistical Learning with Python. His books include Hands-On Data Analysis with NumPy and Pandas.