Book Image

Python: Real-World Data Science

By : Fabrizio Romano, Dusty Phillips, Phuong Vo.T.H, Martin Czygan, Robert Layton, Sebastian Raschka
Book Image

Python: Real-World Data Science

By: Fabrizio Romano, Dusty Phillips, Phuong Vo.T.H, Martin Czygan, Robert Layton, Sebastian Raschka

Overview of this book

The Python: Real-World Data Science course will take you on a journey to become an efficient data science practitioner by thoroughly understanding the key concepts of Python. This learning path is divided into four modules and each module are a mini course in their own right, and as you complete each one, you’ll have gained key skills and be ready for the material in the next module. The course begins with getting your Python fundamentals nailed down. After getting familiar with Python core concepts, it’s time that you dive into the field of data science. In the second module, you'll learn how to perform data analysis using Python in a practical and example-driven way. The third module will teach you how to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis to more complex data types including text, images, and graphs. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. In the final module, we'll discuss the necessary details regarding machine learning concepts, offering intuitive yet informative explanations on how machine learning algorithms work, how to use them, and most importantly, how to avoid the common pitfalls.
Table of Contents (12 chapters)
Free Chapter
Table of Contents
Python: Real-World Data Science
Meet Your Course Guide
What's so cool about Data Science?
Course Structure
Course Journey
The Course Roadmap and Timeline

Chapter 8. Beating CAPTCHAs with Neural Networks

Interpreting information contained in images has long been a difficult problem in data mining, but it is one that is really starting to be addressed. The latest research is providing algorithms to detect and understand images to the point where automated commercial surveillance systems are now being used—in real-world scenarios—by major vendors. These systems are capable of understanding and recognizing objects and people in video footage.

It is difficult to extract information from images. There is lots of raw data in an image, and the standard method for encoding images—pixels—isn't that informative by itself. Images—particularly photos—can be blurry, too close to the targets, too dark, too light, scaled, cropped, skewed, or any other of a variety of problems that cause havoc for a computer system trying to extract useful information.

In this chapter, we look at extracting text from...