The new shoots of spring are just starting to make an appearance, the first flush of snowdrops (the plant, not the weather) are in bloom and warmer weather is on the way.
Except that here in Europe and across the North-Western US right now we're in the grip of unusually large amounts of snow. Oh dear...
I guess there's no better time than now to curl up with a good book, get wrapped up with an extra blanket and get your brain cells going - it's time to learn some new data skills with the three free ebooks we're bringing you this month.
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:
- Machine Learning, Neural and Statistical Classification
- Social Media Mining: An Introduction
- Practical Data Analysis
They're all FREE, so help yourselves...
Disclosure: the three books highlighted here do not have affiliate links.
However, links to other resources on this page may be affiliate links, and we may earn an affiliate commission for purchases you make when using these links.
by D. Michie, D.J. Spiegelhalter, C.C. Taylor (eds)
This book is based on the EC (ESPRIT) project StatLog which compare and evaluated a range of classification techniques, with an assessment of their merits, disadvantages and range of application.
This integrated volume provides a concise introduction to each method, and reviews comparative trials in large-scale commercial and industrial problems.
It makes accessible to a wide range of workers the complex issue of classification as approached through machine learning, statistics and neural networks, encouraging a cross-fertilization between these disciplines.
by Beza Zafarani, Mohammad Ali Abbasi & Huan Liu
The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development.
Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining.
Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts, principles, and methods in various scenarios of social media mining.
by Hector Cuesta
Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service.
This book explains the basic data algorithms without the theoretical jargon, and you'll get hands-on turning data into insights using machine learning techniques.
We will perform data-driven innovation processing for several types of data such as text, images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark.