About the course
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!
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.
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
Curtis Miller is Associate Instructor at the University of Utah, and an MSTAT student. He is currently involved in research on data analysis from statistical and computer science perspectives. Curtis has published research on policy and economic issues.
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