Python Machine Learning Cookbook pdf epub mobi txt 电子书下载 2025

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Prateek Joshi is an Artificial Intelligence researcher and a published author. He has over eight years of experience in this field with a primary focus on content-based analysis and deep learning. He has written two books on Computer Vision and Machine Learning. His work in this field has resulted in multiple patents, tech demos, and research papers at major IEEE conferences. People from all over the world visit his blog, and he has received more than a million page views from over 200 countries. He has been featured as a guest author in prominent tech magazines. He enjoys blogging about topics, such as Artificial Intelligence, Python programming, abstract mathematics, and cryptography. You can visit his blog at www.prateekvjoshi.com. He has won many hackathons utilizing a wide variety of technologies. He is an avid coder who is passionate about building game-changing products. He graduated from University of Southern California, and he has worked at companies such as Nvidia, Microsoft Research, Qualcomm, and a couple of early stage start-ups in Silicon Valley. You can learn more about him on his personal website at www.prateekj.com.

出版者:Packt Publishing - ebooks Account

作者:Prateek Joshi

出品人:

页数:392

译者:

出版时间:2016-9-6

价格:USD 59.99

装帧:Paperback

isbn号码:9781786464477

丛书系列:

图书标签:

Python
机器学习
人工智能
CS
美國
機器學習
印度
Programming

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Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more.

With this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you'll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.

You'll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.