- Paperback: 400 pages
- Publisher: O′Reilly; 1 edition (15 March 2019)
- Language: English
- ISBN-10: 1492035645
- ISBN-13: 978-1492035640
- Product Dimensions: 17.8 x 1.9 x 23.3 cm
- Boxed-product Weight: 576 g
- Average Customer Review: Be the first to review this item
- Amazon Bestsellers Rank: 37,223 in Books (See Top 100 in Books)
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Hands–On Unsupervised Learning Using Python Paperback – 5 May 2019
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About the Author
Ankur Patel is an applied machine learning researcher and data scientist with expertise in financial markets. His work focuses on unsupervised learning, natural language processing, time series prediction, and sequential data problems. Currently, Ankur finds hidden patterns in large-scale unlabeled data for clients around the world as a data scientist at ThetaRay, an Israeli artificial intelligence firm. Ankur started his career as the lead emerging markets trader at Bridgewater Associates and later founded and managed the machine learning-based hedge fund R-Squared Macro.
From the Publisher
From the Preface
Objective and Approach
Most of the successful commercial applications to date—in areas such as computer vision, speech recognition, machine translation, and natural language processing—have involved supervised learning, taking advantage of labeled datasets. However, most of the world’s data is unlabeled.
In this book, we will cover the field of unsupervised learning (which is a branch of machine learning used to find hidden patterns) and learn the underlying structure in unlabeled data. According to many industry experts, such as Yann LeCun, the Director of AI Research at Facebook and a professor at NYU, unsupervised learning is the next frontier in AI and may hold the key to AGI. For this and many other reasons, unsupervised learning is one of the trendiest topics in AI today.
The book’s goal is to outline the concepts and tools required for you to develop the intuition necessary for applying this technology to everyday problems that you work on. In other words, this is an applied book, one that will allow you to build real-world systems. We will also explore how to efficiently label unlabeled datasets to turn unsupervised learning problems into semisupervised ones.
The book will use a hands-on approach, introducing some theory but focusing mostly on applying unsupervised learning techniques to solving real-world problems. The datasets and code are available online as Jupyter notebooks on GitHub.
Armed with the conceptual understanding and hands-on experience you’ll gain from this book, you will be able to apply unsupervised learning to large, unlabeled datasets to uncover hidden patterns, obtain deeper business insight, detect anomalies, cluster groups based on similarity, perform automatic feature engineering and selection, generate synthetic datasets, and more.
This book assumes that you have some Python programming experience, including familiarity with NumPy and Pandas.
For more on Python, visit the official Python website. For more on Jupyter Notebook, visit the official Jupyter website. For a refresher on college-level calculus, linear algebra, probability, and statistics, read Part I of theDeep Learning textbook by Ian Goodfellow and Yoshua Bengio. For a refresher on machine learning, read The Elements of Statistical Learning.
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Most helpful customer reviews on Amazon.com
I love how the book takes you from ground up implementing real systems in python. Not just unfinished snippets to show you how the m/l packages work (you can read their docs for that!) but he fills in the rest of the bigger picture which is "how do i make something that actually accomplishes a task in the world" your college prof probably has never had to ask that.
At $50 it's a steal, pickup a copy and don't look back.
While having domain knowledge helps with any kind of data science work, it is especially important in case of unsupervised learning. This book doesn't (and realistically can't) help with that. That said, if you bring the domain knowledge, this book will give you the tools to work with the data and learn its distribution well enough to move the problem forward, often giving you tools to learn its distribution and automatically label it at scale, converting it to a supervised learning problem, where you can bring many more tools to bear on it.
In addition, the author also provides insights into various techniques for improving the quality of your unsupervised learning. The book also discusses a comprehensive evaluation framework used to evaluate the example solutions, which can be adapted for evaluating your own unsupervised learning solutions.
Overall, I think this is a very useful book, and provides a great resource for an area which hasn't been covered very well in the past. I have access to lots of text and image data through my work, but sadly very little of it is labeled, so while I am not an expert on unsupervised learning, I am not a newbie either. However, I am happy to report I also learned quite a few things from the book.
DISCLAIMER: I did receive a complimentary copy of the book and a non-binding request to review if I could. I am reviewing the book because I believe it deserves attention.
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