During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.

    It is our turn," writes Conrad Black in this scintillating manifesto for how Canada can achieve an exalted role in world affairs. For over 400 years we have toiled in the shadows of our potential and achieved an indifferent recognition among other nations. Chipper, patient, and courteous, we have pursued an improbable destiny as a splendid nation in the northern section of the new world, a demi-continent of relatively good and ably self-governing people, but most would agree we have neither developed a vivid national personality nor realized our true potential. Our main chance, writes Black, is now before us and it is not in the usual realms of military or economic dominance. With the rest of the West engaged in a sterile and platitudinous left-right tug of war, Canada has the opportunity to lead the advanced world to its next stage of development in the arts of government. By transforming itself into a controlled and sensible public policy laboratory, it can forge new solutions to the tiresome problems besetting welfare, education, health care, foreign policy, and other governmental sectors the world over, and make an enormous contribution to the welfare of mankind. Canada has no excuse not to lead in this field, argues Black, who offers nineteen visionary policy proposals of his own. "This is the destiny, and the vocation, Canada could have, not in the next century, but in the next five years of imaginative government.

    Like its sister textbook, Discovering Statistics Using R is written in an irreverent style and follows the same ground-breaking structure and pedagogical approach. The core material is enhanced by a cast of characters to help the reader on their way, hundreds of examples, self-assessment tests to consolidate knowledge, and additional website material for those wanting to learn more.

    Its 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. 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. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main 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.