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Machine Learning

(The MIT Press Essential Knowledge)

3.62  ·  Rating details ·  446 ratings  ·  43 reviews
Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition -- as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As
Kindle Edition, 224 pages
Published September 30th 2016 by The MIT Press
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3.62  · 
Rating details
 ·  446 ratings  ·  43 reviews

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Jun 11, 2017 rated it really liked it
Recommended to me by a product manager at Hulu. It's not too technical, but I wish the book was condensed into more of a primer with more theory/conceptual discussion and examples rather than over-explaining technical details. The engineers reading will be sick of hearing it and the managers/non-engineers won't fully understand.

Overall, if you want to understand and introduction to machine learning and how it works, this book will do the job.
Anders Brabaek
Oct 15, 2016 rated it really liked it
Shelves: technology
Summary: this book is for understanding the concepts of machine learning, not the doing, not the technology, and not the business it will drive. That is, it explains the math and statistics at a conceptual level where anyone can follow.

This book, oddly, starts by explaining the absolutely most trivial things about technology and the Internet – e.g. we now have smart phones. But once that part has past, the author Alpaydin explains the conceptual ideas behind the algorithms and the thinking surro
Feb 07, 2019 rated it liked it
Shelves: 2019, technology
“Intelligence seems not to originate from some outlandish formula, but rather from the patient, almost brute force use of simple, straightforward algorithms. It"

My Take:
Machine Learning and AI are a fact. They have arrived. It's not a new phenomenon or technology but something that always existed but they are now for the first time, more accessible to businesses and even the average person without realizing.

Did you know that, for example, Judges ins some countries rely partly on a report tha
Abe Shocket
Dec 02, 2018 rated it it was ok
OK as an introduction, but you have to have some familiarity with data mgt, programming, etc. I don't know why it bothers me so much to see the word "nowadays", which usual shows up in the abstract of a technical paper and then turns me off immediately. This author got carried away with it and uses the word in practically every paragraph.
Brian Baquiran
Jan 05, 2017 rated it liked it
Shelves: machine-learning
A decent high-level overview of machine learning, for non-technical types. No math or code, but manages to convey the basic ideas behind fundamental ML algorithms from linear regressions to neural networks.

(I listened to this as an audiobook)
Jan 15, 2018 rated it it was ok
Shelves: ipad
Hardly qualify Essential Knowledge, better to read Wikipedia.
Mar 17, 2019 rated it liked it  ·  review of another edition
Shelves: compy-sci
Machine Learning was a bit of a mixed bag for me. As others have stated this is a high-level conceptual approach to the subject. There is very little mathematical expression and it appears aimed at the layperson; however, the reader would be served by at least a fundamental understanding of probability and statistics.
The book is probably useful to management types or just the the random subject scanner who wants to know a bit more about the subject. Alpaydin introduces several problems and mach
Aug 11, 2018 rated it liked it
I'm torn on my reaction to this. There will be a wide reaction to this based on the reader's expectations. If you are after learning about the algorithms or specifics of how machine learning works, you will likely be disappointed (which, admittedly, was my reaction because of my expectations and goals). If you just want an overview focused more on uses, history and where it may go, with only a little dipping into specifics, you will likely greatly appreciate this.

To me, it felt like a mixture of
Leonidas Kaplan
Jul 17, 2017 rated it really liked it
I listened to the audio-book very passively. The 'Machine Learning' had a moderately sized emphasis on explaining various algorithms... at which point I lost focus.

This is probably a great primer, I believe, for students learning programming and artificial intelligence.

But for the lay-person, this could be a difficult book to follow.
The upside, is that the book is currently very relevant, with its reference to 'Alpha Go', which is the artificial intelligence that beat one of the most complex b
Sunita Thapa
Nov 04, 2017 rated it really liked it
I got this book in an audio format; so thought it would be hard to understand with complicated formulas or algorithm, but it wasn't complicated at all. It is more about what is machine learning, how it evolved, or evolving, and what are some of the important topic of machine learning. I think, this book is a great introduction to machine learning for people who do not have good mathematical or statistical background. Of course, I didn't understand all the concepts mentioned, but whatever I under ...more
Feb 16, 2018 rated it really liked it
I would highly recommend this book if you like to conceptually understand the different topics and models of Machine Learning as it exists today. Ethem does a great job at explaining the big picture through common real-life examples, using relatively standard math. It's a great book for those who don't want to learn how to program Machine Learning but would rather understand how Machine Learning might influence design, strategy, and culture.
Dec 15, 2018 rated it it was ok  ·  review of another edition
This was a short book and I did not enjoy it. I found issue with the mixing of important concepts with unimportant ones to the point which the big ideas are not presented clearly. The book could have benefited from enumerating in a bullet list the points that the author wants the reader to know at the beginning of each chapter. The book was not pedagogical enough.
Julian M Drault
Dec 26, 2017 rated it it was amazing
This review has been hidden because it contains spoilers. To view it, click here.
Sten Vesterli
May 14, 2018 rated it really liked it
Another good book in the MIT Press Essential Knowledge Series: A compact overview of the different types of machine learning and what they are useful for. A good introduction for everybody whether in IT or general business, allowing you to understand the jargon and news in this fields. If you want to actually start using machine learning, you'll need a more comprehensive book, of course.
Peter Mortimer
Nov 22, 2017 rated it really liked it
This gives a great overview of what Machine Learning is and where it is being applied. The content is very current (AlphaGo, Deep Learning, GANs,...) and also mentions the history of different ideas that make up machine learning today. A good short read to get a non-technical review of Machine Learning.
Oct 09, 2017 rated it it was amazing
A great casual intro into the key concepts of AI and machine learning. I had some solid product ideas after this, as well as some realizations that I wasn’t thinking as deeply as I should. Great summary. I bought this at the gift shop at the “Robots!” Exhibit at the London Science Museum.
Ryan Pennell
Aug 05, 2018 rated it it was ok
Shelves: abandoned
The author gives you a nice, simple and quick overview of machine learning but gives you little of how to write a program yourself. For a more hands on understanding I would suggest looking elsewhere.
Feb 04, 2019 rated it it was amazing
Alot of good and clear description of the basic concepts of machine learning.

it covers alot of the concepts Hofstadter covered in Godel Echer Bach.

pattern recognition,
Layers head - features- expressions

Juan Carlos
Jan 26, 2018 rated it really liked it
A great overview of Machine Learning. Not a deep dive into the mathematics or technical aspects of machine learning. A great read nontheless.
Aug 17, 2017 rated it liked it  ·  review of another edition
Clearly written and clearly thought out, but shallow for anyone already familiar with the field.
Greg McGee
Sep 01, 2017 rated it it was amazing
Link to full version of book:
Sep 13, 2017 rated it really liked it  ·  review of another edition
Exactly what I was looking for: an overview of the field that is technical but without a mathematical exposition. More of a "physical" treatment.
Miroslav Pikus
Sep 11, 2018 rated it really liked it
užitočné, ale zvládol som to len tak do polovice, potom som sa stratil a zahodil
Vyki Englert
Dec 31, 2017 rated it it was ok
Shelves: programming, data
Useless text -- don't waste your time.
Abhinav Gupta
Nov 21, 2017 rated it really liked it  ·  review of another edition
The book great insights about what is machine learning, how are were using it, ways to enforce learning in machine and as a whole what impact it will create in our lives.
Edelhart Kempeneers
Was goed, maar te weinig diepgaand.
A very well done, non-technical primer on machine learning.
May 16, 2017 rated it it was ok  ·  review of another edition
Oscillates between being too simple and too complex.
Sandeep Shah
Feb 25, 2019 rated it liked it
One can have a quick read just to understand different scenarios in which machine learning is applied and some future scope of each application. Nothing technical in it.
Arkajit Dey
Oct 01, 2017 rated it it was amazing
Useful as a refresher and quick overview of the field, with pointers to the key papers for further in-depth reading as needed.
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Ethem ALPAYDIN received his BSc from Department of Computer Engineering of Bogazici University in 1987 and the degree of Docteur es Sciences from Ecole Polytechnique Fédérale de Lausanne in 1990. He did his postdoctoral work at the International Computer Science Institute, Berkeley in 1991 and afterwards was appointed Assistant Professor at the Department of Computer Engineering of Bogazici Univer ...more

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“Intelligence seems not to originate from some outlandish formula, but rather from the patient, almost brute force use of simple, straightforward algorithms. It” 2 likes
“Each of us, actually every animal, is a data scientist. We collect data from our sensors, and then we process the data to get abstract rules to perceive our environment and control our actions in that environment to minimize pain and/or maximize pleasure. We have memory to store those rules in our brains, and then we recall and use them when needed. Learning is lifelong; we forget rules when they no longer apply or revise them when the environment changes. Learning” 1 likes
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