Ebook Free Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press)
Associated with this condition, you will certainly additionally learn May books that can be sources for your life. It is not only this sort of subject; you might also locate others comparable to this book to serve. Obviously, just what we provide is just what ideal in this globe. So, you might not be stressed to select Fundamentals Of Machine Learning For Predictive Data Analytics: Algorithms, Worked Examples, And Case Studies (The MIT Press) as one of inspiring reading book. Now, no matter what to do, you have to get this book and obtain adhering to the system to be much easier and quicker.
Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press)
Ebook Free Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The MIT Press)
Come follow us on a daily basis to know what publications upgraded each day. You understand, guides that we offer everyday will certainly be updated. As well as now, we will give you the brand-new book that can be recommendation. You can choose Fundamentals Of Machine Learning For Predictive Data Analytics: Algorithms, Worked Examples, And Case Studies (The MIT Press) as guide to read currently. Why should be this book? This is one of the most up to date book collections to upgrade in this website. The book is additionally recommended as a result of the strong reasons that make numerous people love to utilize as reading product.
Do you still have no suggestion with this publication? Why must Fundamentals Of Machine Learning For Predictive Data Analytics: Algorithms, Worked Examples, And Case Studies (The MIT Press) that ends up being the motivation? Everyone has various issue in the life. But, related to the accurate informational as well as expertise, they will certainly have same conclusions, naturally based upon truths and also research. And also currently, just how the Fundamentals Of Machine Learning For Predictive Data Analytics: Algorithms, Worked Examples, And Case Studies (The MIT Press) will deliver the presentation concerning exactly what truths to always be mind will certainly influent exactly how some individuals believe and also keep in mind concerning that issue.
This publication will certainly reveal you the recent book that can be gained in some areas. However, the motivating book will certainly be far more established. However this Fundamentals Of Machine Learning For Predictive Data Analytics: Algorithms, Worked Examples, And Case Studies (The MIT Press), it will reveal you current thing that you need to know. Reading book as one of the activities in your vacations is really smart. Not everybody will have willing to do it. So, when you are person who love this publication to check out, you should appreciate the time analysis as well as completing this publication.
This is exactly what you could extract from this book. By soft file types, you can be readily available to review it in the gizmo when you remain in your method home in automobile or bus or perhaps train. It is your time also to review it when you are being in a waiting checklist. As well as how you could review Fundamentals Of Machine Learning For Predictive Data Analytics: Algorithms, Worked Examples, And Case Studies (The MIT Press) in your house could use the moment before sleeping and also functioning.
A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.
After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
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