Hi! I am a Junior Data Scientist based in London with a strong background in Engineering Sciences.
I have been self-learning Machine Learning and related skills throughout the past year, leveraging my background in Maths and Engineering Sciences.
Below, I share a list of machine learning books that are now on my bookshelf. 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
PS: I am looking for a full-time Data Science position in London from April 2017. You can find out more about me on the home page.
| An Introduction to Statistical Learning | Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani |
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| Official website View on Amazon |
| 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.
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| The Elements of Statistical Learning | Trevor Hastie, Robert Tibshirani, Jerome Friedman |
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| Official website View on Amazon |
| HN: Ask HN: How to get started with machine learning? (950) |
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.
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| Python Machine Learning | Sebastian Raschka |
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| Official website View on Amazon |
| HN: Python Machine Learning (128) |
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.
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| Advanced Analytics with Spark | Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills |
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| Official website View on Amazon |
| HN: Apache Spark Scale: A 60 TB+ production use case (254) |
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.
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| Resonate: Present Visual Stories That Transform Audiences | Nancy Duarte |
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| Official website View on Amazon |
| HN: n/a |
Resonate helps you make a strong connection with your audience and lead them to purposeful action. The author’s approach is simple: building a presentation today is a bit like writing a documentary. Using this approach, you’ll convey your content with passion, persuasion, and impact.
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