Combs Alexander T. - Python machine learning blueprints: put your machine learning concepts to the test by developing real-world smart projects

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First published: July 2016
Second edition: January 2019

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Alexander Combs is an experienced data scientist, strategist, and developer with a background in financial data extraction, natural language processing and generation, and quantitative and statistical modeling. He currently lives and works in New York City .

Michael Roman is a data scientist at The Atlantic, where he designs, tests, analyzes, and productionizes machine learning models to address a range of business topics. Prior to this he was an associate instructor at a full-time data science immersive program in New York City. His interests include computer vision, propensity modeling, natural language processing, and entrepreneurship.

Saurabh Chhajed is a machine learning and big data engineer with 9 years of professional experience in the enterprise application development life cycle using the latest frameworks, tools, and design patterns. He has experience of designing and implementing some of the most widely used and scalable customer-facing recommendation systems with extensive usage of the big data ecosystem the batch, real-time, and machine learning pipeline. He has also worked for some of the largest investment banks, credit card companies, and manufacturing companies around the world, implementing a range of robust and scalable product suites.

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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 the 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 learn how to apply advanced concepts and popular machine learning algorithms to real-world projects. Next, you'll cover projects in 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 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.

This book is for machine learning practitioners, data scientists, and deep learning enthusiasts who want to take their machine learning skills to the next level by building real-world projects. This intermediate-level guide will help you to implement libraries from the Python ecosystem to build a variety of projects addressing various machine learning domains.

, The Python Machine Learning Ecosystem