Download PDF Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press)
The first thing to visit the collection is thinking about exactly what book to read. When you are below as well as visiting this online collection, we will recommend you numerous advised publications for you. Guides that is truly ideal with your life and tasks. Fundamentals Of Machine Learning For Predictive Data Analytics: Algorithms, Worked Examples, And Case Studies (The MIT Press) is one of the optional book catalogues that can be most wanted.
Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press)
Download PDF Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press)
Preparing guides to review on a daily basis is enjoyable for many people. Nevertheless, there are still many people who likewise do not such as reading. This is a trouble. But, when you can support others to begin reading, it will certainly be better. One of the books that can be advised for brand-new visitors is Fundamentals Of Machine Learning For Predictive Data Analytics: Algorithms, Worked Examples, And Case Studies (The MIT Press) This publication is not sort of challenging book to read. It can be read and comprehend by the new readers.
Well, in connection with this problem, what kind of publication do you need currently? This Fundamentals Of Machine Learning For Predictive Data Analytics: Algorithms, Worked Examples, And Case Studies (The MIT Press) It's really wow! We are likewise including the collection of this book soft file here. It is not example by coincidence. This is the result of your initiative to always follow what we provide. By locating the book in this website it verifies that we constantly supply guides that you really require so much.
From the book, you will recognize that analysis is absolutely should do. It will lead you to obtain more valuable spending quality time. By reviewing the books, your hung around will certainly not lose incorrectly. You could locate what exactly you want and needs to observe. Below, the Fundamentals Of Machine Learning For Predictive Data Analytics: Algorithms, Worked Examples, And Case Studies (The MIT Press) ends up being a selection to check out guide since it offers you the fantastic features of the life. Also it is just the rep are for getting this sort of publication, you may see how you could appreciate guide exactly.
When you really feel that you're interested enough in this book, you could get it by clicking the connect to attach directly to the book. Fundamentals Of Machine Learning For Predictive Data Analytics: Algorithms, Worked Examples, And Case Studies (The MIT Press) is supplied in the soft file forms, so you could save and review it in different device. We mean that it is suitable and available to review every time you want. Even it remains in the train or every where you are, you can use the leisure for analysis.
Review
Erudite yet real-world relevant. It's true that predictive analytics and machine learning go hand-in-hand: To put it loosely, prediction depends on learning from past examples. And, while Fundamentals succeeds as a comprehensive university textbook covering exactly how that works, the authors also recognize that predictive analytics is today's most booming commercial application of machine learning. So, in an unusual turn, this highly enriching opus brings the concepts to light with industry case studies and best practices, ensuring you'll experience the real-world value and avoid getting lost in abstraction.―Eric Siegel, Ph.D., founder of Predictive Analytics World; author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or DieThis book provides excellent descriptions of the key methods used in predictive analytics. However, the unique value of this book is the insight it provides into the practical applications of these methods. The case studies and the sections on data preparation and data quality reflect the real-world challenges in the effective use of predictive analytics.―Pádraig Cunningham, Professor of Knowledge and Data Engineering, School of Computer Science, University College Dublin; coeditor of Machine Learning Techniques for MultimediaThis is a wonderful self-contained book that touches upon the essential aspects of machine learning and presents them in a clear and intuitive light. With its incremental discussions ranging from anecdotal accounts underlying the 'big idea' to more complex information theoretic, probabilistic, statistic, and optimization theoretic concepts, its emphasis on how to turn a business problem into an analytics solution, and its pertinent case studies and illustrations, this book makes for an easy and compelling read, which I recommend greatly to anyone interested in finding out more about machine learning and its applications to predictive analytics.―Nathalie Japkowicz, Professor of Computer Science, University of Ottawa; coauthor of Evaluating Learning Algorithms: A Classification Perspective
Read more
About the Author
John D. Kelleher is Academic Leader of the Information, Communication, and Entertainment Research Institute at the Technological University Dublin. He is the coauthor of Data Science (also in the MIT Press Essential Knowledge series) and Fundamentals of Machine Learning for Predictive Data Analytics (MIT Press).
Read more
Product details
Series: The MIT Press
Hardcover: 624 pages
Publisher: The MIT Press; 1 edition (July 24, 2015)
Language: English
ISBN-10: 0262029448
ISBN-13: 978-0262029445
Product Dimensions:
7 x 1.1 x 9 inches
Shipping Weight: 2.3 pounds (View shipping rates and policies)
Average Customer Review:
4.4 out of 5 stars
38 customer reviews
Amazon Best Sellers Rank:
#33,687 in Books (See Top 100 in Books)
Kindle version: images are too small.This is particularly bad for special chars and formulas which are rendered as images as they appear about as large as the punctuation.Normal diagrams are also small and must be viewed with the zoom function.Apologies for rating the book based on formatting, but there's no other apparent way to contact the publisher.Once the issues are resolved I will fix the rating to fix the "outlier" it has created.
Supervised machine learning only. Basically a bunch of applications for an undergrad CS class. Light on theory. Very well structured though and excellent if you want to see some applications of machine learning in action. For deeper treatment see coursera courses by Geoff Hinton of Toronto and the Stanford ML class.
I have already used machine algorithms in production with Spark and Python, but I wanted to have a better understanding of how the algorithms work and more importantly what the variations, strengths/weaknesses, and trade-offs are for each algorithm. This book was exactly what I've been looking for.The authors explain the algorithms fluidly without any reference to specific programming libraries or languages. They introduce the concepts very well before moving into the specifics of the logic and math behind the algorithms. Following a thorough explanation of how the algorithm works, the authors then describe variants and pitfalls based on their prior foundation.So, if you aren't a math major but would like to understand the concepts and details of how ML works along with practical knowledge of variants, parameter tuning, and trade-offs, then this book should be exactly what you need.Finally, the algorithms covered are the most commonly used in ML. AI isn't covered. Look at the Table of Contents to see which algorithms are explained.
Machine Learning is brilliantly explained in this outstanding book. You will learn the subject a lot better than in many other books in the market. The only downside of this book is the lack of examples with programming code, especially in Python. I strongly urge the authors to do so in a next edition. A lot in the area is learned by doing, by using good software development practices.
Great introductory book to this field. I would highly recommend this for computer scientists or other engineers looking to get an understanding of this field. I have read a number of books that are too heavy with theory and some that are a bit on the skimpy side and leave out details that are important for a true practical implementation. This has just the right mix.
I am ML specialist and instructor.There are many different types of books in Machine Learning. That cover various aspects of the field.Some books are base on theoretic side: Learning from the Data.Some books provide a gentle way for programming for Machine Learning in different languagesSome books combine theory and programmingThis book "Fundamentals of Machine Learning" a good written book for practitioner in machine learning. For people that want to know how machine learning experts work. That processes they use, and how them organize there work.In additional basic properties and ideas of general algorithms discussed.This book uses excellent plant English, many examples and real casesBut if you need mathematical background or programming background I think you need use another book.
Overall, the book is well written - plenty of examples and good approaches towards data preparation, analysis, and applied ML.
I wish I returned it. It did not have anything useful.
Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press) PDF
Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press) EPub
Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press) Doc
Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press) iBooks
Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press) rtf
Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press) Mobipocket
Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press) Kindle
0 komentar:
Posting Komentar