We've left Christmas and the New Year behind us, and January is in the rear-view mirror. What happened to the New Year resolutions? Did you manage to stick to them? It's not too late to get started and get ready to learn some new data skills with the three free ebooks we're bringing you this month.
Now that those long nights are just that bit shorter, you might even have enough light to read them by!
I hope these books prove to be a valuable resource to you and that you will visit regularly (and share with your friends in social media too).
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This month we highlight 3 books:
- Algorithms for Reinforcement Learning
- Data Mining And Analysis : Fundamental Concepts and Algorithms
- Getting Started with Python in the Lab
They're all FREE, so help yourselves...
by Csaba Szepesvári
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective.
In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner’s predictions.
Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms’ merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering.
by Mohammed J. Zaki, Wagner Meira, Jr, Wagner Meira
The fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated methods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics.
This textbook for senior undergraduate and graduate data mining courses provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics.
The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks, and also covers cutting-edge topics such as kernel methods, high-dimensional data analysis, and complex graphs and networks.
With its comprehensive coverage, algorithmic perspective, and wealth of examples, this book offers solid guidance in data mining for students, researchers, and practitioners alike.
by Gordon Webster
Many life science researchers are missing out on some of the wonderful computational tools that are out there, and that could be invaluable in helping them to manage and analyze their laboratory data.
This may be due to a lack of awareness that these tools exist and that many of them are inexpensive or free, or it may be out of fear that learning to use them will take too much time out of an already hectic schedule.
This brief Python code tutorial for life science computing, aims to point life scientists with relatively little exposure to programming languages, in the right direction to be able to start using the Python programming language to write useful code that can solve real problems in their work and their research.