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With this machines in motion building set, children engineer their own playtime fun with the help of creative, open-ended building activities. Using the included instructions, children can build their own versions of numerous different interactive machines and vehicles, many of which incorporate special parts including a wrecking ball, chains, pulleys, and wheels. Turn out a tank with its own helicopter propeller or craft a complex contraption that spins with the twist of a lever. Children can also set the instructions aside and bring their own designs to life. Thanks to the open-ended designs and interchangeable parts, this set encourages children to discover their inner engineer and hone STEM skills such as critical thinking and trial and error. Interchangeable parts work with other Learning Resources Gears sets (sold separately) to increase the playability of every piece. Building set is designed for children ages 4 and above.

New! This book is in the same immaculate condition as when it was published 0.68

Michalski, Ryszard S., Carbonell, Jaime G., Mitchell, Tom M.

New Book. Shipped From Uk. This Book Is Printed On Demand. Established Seller Since 2000.

Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!

Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.

May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 1.69

This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Book is in Used-VeryGood condition. Pages and cover are clean and intact. Used items may not include supplementary materials such as CDs or access codes. May show signs of minor shelf wear and contain very limited notes and highlighting. 1.23

Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis.Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike.Learn how machine learning has contributed to the success of modern spam filtersQuickly detect anomalies, including breaches, fraud, and impending system failureConduct malware analysis by extracting useful information from computer binariesUncover attackers within the network by finding patterns inside datasetsExamine how attackers exploit consumer-facing websites and app functionalityTranslate your machine learning algorithms from the lab to productionUnderstand the threat attackers pose to machine learning solutions

New! This book is in the same immaculate condition as when it was published 0.51

Book is in NEW condition. 0.91

Book is in NEW condition. 2.65

A fully self-contained introduction to machine learning. All that the reader requires is an understanding of the basics of matrix algebra and calculus. Machine Learning: An Applied Mathematics Introduction covers the essential mathematics behind all of the most important techniques. Chapter list:Introduction (Putting ML into context. Comparing and contrasting with classical mathematical and statistical modelling)General Matters (In one chapter all of the mathematical concepts you'll need to know. From jargon and notation to maximum likelihood, from information theory and entropy to bias and variance, from cost functions to confusion matrices, and more)K Nearest Neighbours K Means ClusteringNaïve Bayes ClassifierRegression MethodsSupport Vector MachinesSelf-Organizing MapsDecision TreesNeural NetworksReinforcement LearningAn appendix contains links to data used in the book, and more.The book includes many real-world examples from a variety of fields includingfinance (volatility modelling)economics (interest rates, inflation and GDP)politics (classifying politicians according to their voting records, and using speeches to determine whether a politician is left or right wing)biology (recognising flower varieties, and using heights and weights of adults to determine gender)sociology (classifying locations according to crime statistics)gambling (fruit machines and Blackjack)business (classifying the members of his own website to see who will subscribe to his magazine)Paul Wilmott brings three decades of experience in education, and his inimitable style, to this, the hottest of subjects. This book is an accessible introduction for anyone who wants to understand the foundations and put the tools into practice.Paul Wilmott has been called “cult derivatives lecturer” by the Financial Times and “financial mathematics guru” by the BBC.

Book is in Used-VeryGood condition. Pages and cover are clean and intact. Used items may not include supplementary materials such as CDs or access codes. May show signs of minor shelf wear and contain very limited notes and highlighting. 0.51

Book is in Used-VeryGood condition. Pages and cover are clean and intact. Used items may not include supplementary materials such as CDs or access codes. May show signs of minor shelf wear and contain very limited notes and highlighting. 1.81

The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Unread book in perfect condition.

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.