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Machine Learning

3.59  ·  Rating details ·  619 ratings  ·  62 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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Average rating 3.59  · 
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Peter (Pete) Mcloughlin
Call me paranoid but when I pick up audiobooks from MIT on machine learning I get the feeling that a joke is being pulled on the reader and that the book may be a product of machine learning itself rather than a human author especially when the author has a suspicious sounding name. This book generally passes what goes for an author's Turing test. I mean it seems okay and sounds like it comes from a human but I have a suspicious and paranoid temperament.

Here is a video that is related to the fie
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
Colin Thomson
May 07, 2020 rated it liked it
This was an interesting and broad introduction to the topic of machine learning. It was accessible enough not to feel overwhelmed, and managed not to bombard you with technical terms. I read this for professional development, and feel I reaped the necessary benefits.
Given I normally read for pleasure, I'm not sure how to rate this. Maybe it's 4 stars?!?!
Regardless, I'd recommend it to anyone in tech.
May 16, 2020 rated it liked it  ·  review of another edition
Shelves: audiobook
I found this a good re-introduction to machine learning. By re-introduction, I’m using my perspective based on experience, having worked in AI and neural nets twenty years back, but keeping up through pop science magazine articles and such since then. For me, I was reminded of many of the methods I knew, and a few I hadn’t heard of. Short. Nice description, just what you want.
Feb 07, 2019 rated it liked it
Shelves: technology, 2019
“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 re
Stefan Kanev
Nov 03, 2019 rated it liked it
This is a curious book.

I'm mostly grateful that it introduced me to the MIT Essential Knowledge series, which seem pretty promising. They are short, small and beautifully produced. I ordered a bunch of others and am quite keen to start reading them.

That being said, this book had a lot of promise, but I don't feel it delivered fully. It's a very high-level overview of what Machine Learning is and not much more. It doesn't go to code (or even math), but uses well-written prose to explain each key
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.
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
An Te
Jul 01, 2020 rated it liked it
A helpful primer on machine learning. A near-complete introduction to the subject of machine learning from its applications, theories, tools, manifestations and its possible future. Some material is a rehash of his book "Data Science."

For a reader, one would be aware of the recommendations that come our way from websites/retailers, all generated from machine learning methods, as they may simply be reinforcing your preferences as we speak. The future methods will hope to add more diversity to our
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
Antonis Maronikolakis
Machine Learning by Ethem Alpaydin is a short book on Machine Learning. It serves as an introduction to the field, explaining in a nutshell the different techniques and algorithms in Machine Learning. The author takes great care to bring forward the concepts in a simple manner so that newcomers to the field can get a taste of what to expect.

It does not go in depth on the specifics and it doesn’t introduce the mathematical background of the field, but it gives enough to entice and give a quick ov
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.
This is a good introductory book to Machine Learning and related topics (Data Analysis, Artificial Intelligence, etc), for those who might want to eventually deepen their technical knowledge of ML, without having to gruel through the details of the methods and algorithms from the get-go. It may also be a good read for non-technical people who have deep interest in the subject, and would not mind grappling with some of the advanced jargon in the domains of statistics and computer science.
Danny Moril-Las
Dec 30, 2019 rated it really liked it
Very important to note that this book is a very basic introduction to Machine Learning. If you already work with ML, if you already have experience, if you are advanced... this is not your book.

It's not a technical book and it explains the main and basic concepts of Machine Learning. If you are now getting into ML and you want an introduction to some wording and concepts this is an interesting and helpful book.
Philip Weiss
This book is for the person who wants an introduction to a whole collection of technologies. The author is descriptive, informative, and very clear.

The author avoids going into too many details, so this is not the book for someone to learn how to implement the ideas. The theme is more targeted to the reader who wants to learn the terminology, the background, and the uses of the various machine learning techniques.
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.
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.
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.
Sep 06, 2019 rated it it was ok  ·  review of another edition
Shelves: pdf
This book lacks of a proper structure to follow the overview on machine learning tools and applications: I thought it was supposed to help neophytes but it seems not written in pedagogic terms. A pity to say that the most useful part is the Glossary at the end: consider it a sort of summary of the book's content and the main long-term take away.
Kieran Wood
Sep 08, 2020 rated it really liked it
Shelves: computing
This is a great introduction to machine learning, and a useful dictionary of sorts. Having every form explained in the chronology presented gives you a good idea of why different algorithms were implemented at different points, and what shortcomings they both resolve and present.

Well worth a read as an introduction to 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
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

Antoine Balaine
Jun 25, 2019 rated it did not like it
Shelves: 2019
I have seldom ever read a book which's introduction's condescending tone so strongly insulted my intelligence, to then just throw muddy information in every direction, without bothering to clearly hierarchize details from core concepts.

This author plowed through the writing in a rush, with zero empathy for his reader.
Rick Sam
Sep 25, 2020 rated it liked it
Shelves: computer-science
A Primer for Machine Learning. If you are an undergrad or want to know broad topics in Machine Learning, I'd recommend you this book. This could be used as the first gentle introduction, perhaps for 5th grade-10th grade.

Deus Vult,
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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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