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Reel in the complete guide to every aspect of fishing The Complete Fishing Manual is the definitive guide to fishing covering everything from strategies and techniques, to choosing bait, tackle and equipment. Whether you're beach-fishing for sharks or fly-fishing in the rivers of England, advice on anatomy, behaviour and habitat and detailed photography on every technique will mean that no fish is safe from you! And for those who want to cast their net further? The Complete Fishing Manual gives you an amazing insight into the world's best fishing destinations, and the once-in-a-lifetime species that you can find there. Inspiring novices and experienced fishermen and women alike, The Complete Fishing Manual is an invaluable resource for any keen angler. Content previously published in Eyewitness Companions: Fishing.

William Lowell Kane and Abel Rosnovski, one the son of a Boston millionaire, the other a penniless Polish immigrant. Two men, born on the same day, on opposite sides of the world, their paths destined to cross in their ruthless struggle to build a fortune.An unputdownable story, spanning sixty years, The Complete Fishing Manual ebook pdf of two powerful men linked by an all-consuming hatred, brought together by fate to save and finally destroy each other." Summary "Machine Learning in Action" is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About the Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. "Machine Learning in Action" is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's InsideA no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and AdaboosTable of ContentsPART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: naive Bayes Logistic regression Support vector machines Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regression Tree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clustering Association analysis with the Apriori algorithm Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify data Simplifying data with the singular value decomposition Big data and MapReduce

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Author: Henry Gilbey,
Number of Pages: 352 pages
Published Date: 02 May 2011
Publisher: Dorling Kindersley Ltd
Publication Country: London, United Kingdom
Language: English
ISBN: 9781405361781
Download Link: Click Here
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