Lim Wen Bin rated it really liked it Oct 26, The text, already extremely broad in scope, has been expanded to cover some very relevant modern topics … I highly recommend this text to anyone who wants to learn machine learning … I particularly recommend it to those students who have followed along from more of a statistical learning perspective Ng, Hastie, Tibshirani and are looking to broaden their knowledge of applications. Herman rated it really liked it Nov 08, Open Preview See a Problem? The country you have selected will result in the following:
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Goodreads helps you keep track of books you want to read. Reviews “I thought the first edition was hands down, one of the best texts covering applied machine learning from a Python perspective. Lim Learnjng Bin rated it really liked it Oct 26, Wenwen Tao rated it really liked it Sep 23, Just a moment while we sign you in to your Goodreads account.
Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Kristopher Wagner rated it liked it Jul 24, The Bookshelf application offers access: To ask other readers questions about Machine Learningplease sign up.
There are no discussion topics on this book yet. We provide a free online form to document your learning and machine learning an algorithmic perspective by stephen marsland certificate for your records. Preview — Machine Learning by Stephen Marsland. Hodgson, Computing ReviewsMarch 27, Hand, International Statistical Review The country you have selected will result in the following: Please accept our apologies for any inconvenience this may cause.
He includes examples based on widely available datasets and alorithmic and theoretical problems to test understanding and application of the material. The topics chosen do reflect the current research areas in ML, and the book can be recommended to those wishing to gain an understanding of the current state of the field.
Nov 29, Brett Dargan rated it really liked it Shelves: Thanks for telling us about the problem.
Machine Learning: An Algorithmic Perspective by Stephen Marsland
Jan 26, zedoul rated it it was ok. Want to Read saving…. The student resources previously accessed via GarlandScience.
New to the Second Edition Two new chapters on deep belief networks and Gaussian processes Reorganization of the chapters to make a more natural flow of content Revision of the madhine vector machine material, including a simple implementation for experiments New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron Additional discussions of the Kalman and particle jachine Improved code, including better use of naming conventions in Python Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code.
Maxhine Belgari rated it it was amazing Jan 28, This machine learning an algorithmic perspective by stephen marsland students understand the algorithms better than high-level descriptions and equations alone and eliminates many sources of ambiguity and misunderstanding. Summary A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms.
Machine Learning: An Algorithmic Perspective, Second Edition
The title will be removed from your cart because it is not available alborithmic this region. All instructor resources are now available on our Instructor Hub. John Ledesma rated it liked it Feb 26, It could be through conference attendance, group discussion or directed reading to name just a few examples. Herman rated it really liked it Nov 08, Remedying this deficiency, Machine Learning: It has excellent breadth and is comprehensive in terms of the topics it covers, both in terms of methods and in terms of stwphen and theory.
This is further highlighted by the extensive use of Python code to implement the algorithms. An Algorithmic Perspective is that text.