Hi! I am a Data Scientist based in London with a strong background in Engineering Sciences.
Below, I share a list of interesting machine learning books. Most of these books have a free version available on their website and can be ordered from Amazon. I have included links to relevant HN discussions, as it is how I found out about these books in most of the cases.
Have a great read, Ghyslain
An Introduction to Statistical Learning Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani Official website HN: Ask HN: How to get started with machine learning? (950)
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The Elements of Statistical Learning Trevor Hastie, Robert Tibshirani, Jerome Friedman Official website HN: Ask HN: How to get started with machine learning? (950)
This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more.
Python Machine Learning Sebastian Raschka Official website HN: Python Machine Learning (128)
This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry.
Advanced Analytics with Spark Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills Official website HN: Apache Spark Scale: A 60 TB+ production use case (254)
If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.
Resonate: Present Visual Stories That Transform Audiences Nancy Duarte Official website HN: n/a
In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example.
Reinforcement Learning: An Introduction Richard S. Sutton , Andrew G. Barto Official website HN: New Draft of Reinforcement Learning: An Introduction, Second Edition (170)
Resonate helps you make a strong connection with your audience and lead them to purposeful action. The authors approach is simple: building a presentation today is a bit like writing a documentary. Using this approach, youll convey your content with passion, persuasion, and impact.
In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the fields intellectual foundations to the most recent developments and applications.