Value: $65.99 - $59.06
(as of Aug 13, 2025 16:23:27 UTC – Particulars)
Study to expertly apply a spread of machine studying strategies to actual knowledge with this sensible information.
Full of actual datasets and sensible examples, The Artwork of Machine Studying will make it easier to develop an intuitive understanding of how and why ML strategies work, with out the necessity for superior math.
As you’re employed by means of the e book, you’ll discover ways to implement a spread of highly effective ML strategies, beginning with the k-Nearest Neighbors (k-NN) technique and random forests, and shifting on to gradient boosting, assist vector machines (SVMs), neural networks, and extra.
With assistance from actual datasets, you’ll delve into regression fashions by means of using a bike-sharing dataset, discover resolution timber by leveraging New York Metropolis taxi knowledge, and dissect parametric strategies with baseball participant stats. You’ll additionally discover skilled suggestions for avoiding frequent issues, like dealing with “soiled” or unbalanced knowledge, and learn how to troubleshoot pitfalls.
You’ll additionally discover:
The way to cope with giant datasets and strategies for dimension reductionDetails on how the Bias-Variance Commerce-off performs out in particular ML methodsModels primarily based on linear relationships, together with ridge and LASSO regressionReal-world picture and textual content classification and learn how to deal with time collection knowledge
Machine studying is an artwork that requires cautious tuning and tweaking. With The Artwork of Machine Studying as your information, you’ll grasp the underlying rules of ML that can empower you to successfully use these fashions, reasonably than merely present a number of inventory actions with restricted sensible use.
Necessities: A primary understanding of graphs and charts and familiarity with the R programming language
From the Writer
Concerning the Creator
Norman Matloff is an award-winning trainer at UC Davis, with a PhD in Arithmetic from UCLA. He’s the creator of quite a few books within the knowledge science space, and his software program and net tutorials are used everywhere in the world. His e book, Statistical Regression and Classification: from Linear Fashions to Machine Studying, was the recipient of the 2017 Ziegel Award, given by the outstanding technical journal Technometrics. Matloff is incessantly requested to provide keynote addresses at knowledge science conferences and he additionally writes about social points. He was the recipient of the Distinguished Public Service Award from UC Davis and can be the creator of The Artwork of Debugging with GDB, DDD, and Eclipse and The Artwork of R Programming (each No Starch Press).
Concerning the Writer
No Starch Press has revealed the best in geek leisure since 1994, creating each well timed and timeless titles like Python Crash Course, Python for Children, How Linux Works, and Hacking: The Artwork of Exploitation. An unbiased, San Francisco-based publishing firm, No Starch Press focuses on a curated listing of well-crafted books that make a distinction. They publish on many matters, together with laptop programming, cybersecurity, working techniques, and LEGO. The titles have character, the authors are passionate consultants, and all of the content material goes by means of intensive editorial and technical critiques. Lengthy identified for its enjoyable, fearless strategy to expertise, No Starch Press has earned extensive assist from STEM lovers worldwide.
Writer : No Starch Press
Publication date : Jan. 9 2024
Language : English
Print size : 272 pages
ISBN-10 : 1718502109
ISBN-13 : 978-1718502109
Merchandise weight : 369 g
Dimensions : 17.78 x 1.63 x 23.5 cm
Finest Sellers Rank: #734,698 in Books (See Prime 100 in Books) #104 in Mathematical & Statistical Software program (Books) #929 in AI Machine Studying #1,513 in Programming Languages Textbooks
Buyer Opinions: 4.8 4.8 out of 5 stars 8 rankings var dpAcrHasRegisteredArcLinkClickAction; P.when(‘A’, ‘prepared’).execute(perform(A) { if (dpAcrHasRegisteredArcLinkClickAction !== true) { dpAcrHasRegisteredArcLinkClickAction = true; A.declarative( ‘acrLink-click-metrics’, ‘click on’, { “allowLinkDefault”: true }, perform (occasion) { if (window.ue) 0) + 1); } ); } }); P.when(‘A’, ‘cf’).execute(perform(A) { A.declarative(‘acrStarsLink-click-metrics’, ‘click on’, { “allowLinkDefault” : true }, perform(occasion){ if(window.ue) }); });
