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Learning From Data Hardcover – 2012
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This book, together with specially prepared online material freely accessible to our readers, provides a complete introduction to Machine Learning, the technology that enables computational systems to adaptively improve their performance with experience accumulated from the observed data. Such techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. In addition, our readers are given free access to online e-Chapters that we update with the current trends in Machine Learning, such as deep learning and support vector machines. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. What we have emphasized are the necessary fundamentals that give any student of learning from data a solid foundation. The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.
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A. For somewhat theoretical approach to machine learning
1. If you have less than a month to study it: Read this book.
2. If you have a semester: Read this book along with lecture series by Yaser's on youtube.
B. For more applied approach to machine learning
1. If you have semester: Go through Andrew Ng's lecture series
For intermediate to advanced:
1. If you have a semester: Read "Machine Learning - A Probabilistic Perspective" by Kevin Murphy (expensive but good reference book).
Other classic machine learning textbooks, if you have more time:
1. PRML - Bishop (The first book I read on Machine Learning. Very accessible. More detailed than Yasir's book, but less than Kevin's book)
2. Nature of Statistical Machine Learning - Vapnik (One of the pioneer in this field. Extremely theoretical approach)
3. Elements of Statistical Learning - Hastie et al (free pdf copy available)
"Learning from Data" but it also can be used.
The author make a miracle - he explained difficult entities in elegant interesting but precise way.
Must read for everyone who want to know the profound basis of ML and not only to use code.
I have just started reading it and love it.
I personally struggle with the exercises but that's only because I have not done maths for a long long time (13-years!) and I'm very very rusty with probability etc.
I don't usually buy hardback anymore but I am very happy with this one. The paper and printing quality is brilliant.
I ordered it from the US to be delivered in the UK. It arrived on time and in mint condition.
Can't ask for more. It suits my needs 100% hence the 5 stars.
The book itself is a joy to behold. Hardcover on high quality paper with neat figures/tables/diagrams and beautiful use of colour.
I like this book because it sticks to the basics and doesn't try to over complicate things. It has an adequate treatment of theory but ultimately emphasizes practical intuition and ideas. I also really appreciate the extensive supplementary material on the website( online lectures, slides and additional homework problems).
Lastly, the price (as of January 2014) of $29 is very reasonable and was one of the main reasons I felt comfortable making it required for the class.
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