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Python Machine Learning Blueprints

Python Machine Learning Blueprints - Second Edition

By : Combs, Chhajed, Roman
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Python Machine Learning Blueprints

Python Machine Learning Blueprints

By: Combs, Chhajed, Roman

Overview of this book

Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you to put your knowledge and skills into practice and use the Python ecosystem to cover key domains in machine learning. This second edition covers a range of libraries from the Python ecosystem, including TensorFlow and Keras, to help you implement real-world machine learning projects. The book begins by giving you an overview of machine learning with Python. With the help of complex datasets and optimized techniques, you’ll go on to understand how to apply advanced concepts and popular machine learning algorithms to real-world projects. Next, you’ll cover projects from domains such as predictive analytics to analyze the stock market and recommendation systems for GitHub repositories. In addition to this, you’ll also work on projects from the NLP domain to create a custom news feed using frameworks such as scikit-learn, TensorFlow, and Keras. Following this, you’ll learn how to build an advanced chatbot, and scale things up using PySpark. In the concluding chapters, you can look forward to exciting insights into deep learning and you'll even create an application using computer vision and neural networks. By the end of this book, you’ll be able to analyze data seamlessly and make a powerful impact through your projects.
Table of Contents (13 chapters)
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Build an App to Find Cheap Airfares

Let's talk about mistakes. They're part of life; everyone makes them—even airlines.

In 2014, I happened to be reading my Twitter feed one afternoon when one of the accounts I follow tweeted that a major US airline had fares to Europe that were significantly below what would be expected. At the time, the cheapest fare from New York to Vienna was around $800, but the advertised fares for a select number of dates were between $350 and $450. This seemed too good to be true. But it wasn't. I had lucked upon what's known in the industry as a mistake fare.

In the super-secretive society of travel hackers and mileage junkies, it's well-known that airlines occasionally—and accidentally—post fares that exclude fuel surcharges. And remarkably, this isn't the only type of mistake they make. You might expect...

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Python Machine Learning Blueprints
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