Someone new to the math may have problems, but the examples might still be enough to wrap your mind around the concepts and give you a good understanding of the usage. It also analyzes reviews to verify trustworthiness. Highly recommended. The worst part is probably that the files that are used in some of the examples are hosted on the authors blog and have been taken down. Reviewed in the United States on February 17, 2011. Instead of reading magazines and newspapers we use blogs as our source of news. This book explains: * Collaborative … Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. The first thing to say is that his code is in Python which isn’t the most obvious choice of computer language for a book on artificial intelligence but this too doesn’t really matter. Start by marking “Programming Collective Intelligence: Building Smart Web 2.0 Applications” as Want to Read: Error rating book. The programming code allows someone to work through all of the examples discussed in the book. I would not recommend anyone buying this book. Programming Collective Intelligence is easy to read, small but concise, and its only major flaw is the title; and that is because it is misleading. This is a visionary book because it predicts a lot of what will happen to the Internet soon. Slightly outdated for today's times, but still does a good job at describing the practical techniques required to make small features for a pet web application without all the morbidity that surrounds today's age of statistical inference. Taxonomic, clustering, neural networks, etc. 5 years ago, this may have been *the* book for the aspiring Artificial Intelligence practioner. Regardless, he tends to gloss over important details and not explain his rationale for many key points in the algorithms he lays out. Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. It hasn't held up as well, or maybe I'm just a lazy whiner, but this book requires, Programming Collective Intelligence is easy to read, small but concise, and its only major flaw is the title; and that is because it is misleading. Extremely basic if you're familiar at all with ML, but its intended audience probably isn't. Be the first to ask a question about Programming Collective Intelligence. Reviewed by: Mike James. Sign up . You might go on and try another language to spice up your life, but you then, again, realise same old, same old. There's a point every developer hits, that point where everything seems mundane, repetitive and not worth doing anymore. Segaran mixes equal parts math, theory and practice in a way that keeps the reader's attention while introducing a number of somewhat complex machine learning topics. I've read this book multiple times and still refer back to it. How do we process information in the Internet age? The examples are outdated, based on sites that don't even exist anymore. I would say that if you are in any field that uses statistical analysis of experimental or research data, you would find something to use in this book. Programming Collective Intelligence is a new book from O'Reilly and Associates on the concept of collecting data from disparate sources – like, say, users – and integrate that data into your programs. Having said that, the introduction to the subjects is very simplified, so you'll need further reference to actually implement anything at all. See all details for Programming Collective Intelligence: Building Smart Web 2.0... © 1996-2020, Amazon.com, Inc. or its affiliates. Mathematical formulas in which code snippets are based can only be found (without further expla. I found the text to be readable with broad application in other areas including document classification systems for analyzing large amount of documents in the context of e-discovery. Excellent Resource for Clustering Algorithoms and Other AI Algorithoms, Reviewed in the United States on April 23, 2008. My main criticism would be that the book doesn't fully succeed at explaining exactly what you should use each technique for and which are their pros and cons. This is because blogs offer much more customized news feed. In a typical newspaper, how much of its content is of interest to a reader? This book does a good job making an introduction of machine learning technologies to the average programmer. You know what to expect, your know how to do it. The book touches quite heavily on using collective information and social site APIs, but what it is really about is data mining. Reviewed in the United States on April 9, 2013. 5 stars to this book for being easy to read and well written, presenting some really sophisticated concepts in a very neat way, and finally putting all these concepts along with interesting ideas and examples all in one place. Disabling it will result in some disabled or missing features. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from ot. I bought this book a couple of years ago as machine learning and deep neural networks were becoming the big news in the smart algorithms world, and while it was a bit old even then, the concepts have aged well. This book is not about collective intelligence at all. This book is loaded with lots of algorithms related to machine learning and collaborative filtering, which is good, as there are a dearth of non-textbooks that cover this material from a practical vantage point. Read honest and unbiased product reviews from our users. There's a lot more math than I'm used to -- every example so far contains a mathematical function. Reviewed in the United States on December 4, 2013. Very good introduction into machine-learning, information retrieval & data mining related questions. This is the first time I've actually taken the time to write out a review. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. This page works best with JavaScript. Instead, it explains when the algorithms can be used, and how to implement and use them appropriately. Excelent book for introduction to machine learning! I bought this book a couple of years ago as machine learning and deep neural networks were becoming the big news in the smart algorithms world, and while it was a bit old even then, the concepts have aged well. Instead, it explains when the algorithms can be used, and how to implement and use them appropriately. Overall, I give this book 4 stars. There's a problem loading this menu right now. This book does a good job making an introduction of machine learning technologies to the average programmer. Programming Collective Intelligence: Building Smart Web 2.0 Applications. I had hoped for a bit more answering of why the more complicated algorithms can be expected to work, but this book was not written for that audience. The book is quite good to get a general idea how some common algorithms work, and does that in a very nice way. Let me start with pointing out what this book does well: Reviewed in the United States on October 29, 2018. The book also needs a good revision, since some of the APIs described are not available or had changes in the last years. To calculate the overall star rating and percentage breakdown by star, we don’t use a simple average. Also, the name of the book kinda sucks -- makes me think of something else (not sure what). The list of errata is huge (and doesn't even include all the errata I found). To see what your friends thought of this book. Top subscription boxes – right to your door. Join O'Reilly Media's fan group, 33 Reader Approved, Highly Rated Fiction to Discover Now. Let us know what’s wrong with this preview of, Published I've started getting acquainted with machine learning with this book. The book covers recommendation systems, classifiers, clustering, and regression models as well as less obvious searching and ranking, optimization and genetic pr. Having said that, the introduction to the subjects is very simplified, so you'll need further reference to actually implement anything at all. There is little theory or mathematics used. I do not regret purchasing the book; however, I must say that the writer's clarity is sub-par when compared with that of most books that I read. I bought this book a couple of years ago as machine learning and deep neural networks were becoming the big news in the smart algorithms world, and while it was a bit old even then, the concepts have aged well. It may not be a flaw with the majority of readers, but personally I wouldn't care about the collective, the Facebook API or anything like that, but I was really interested in the different ways to analyse data. Nice learning curve, no sudden unexplained jumps. For many programmers like me, this opens a door to the world of machine learning. Top 10 Books according to Hacker News Books, Like this book? Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. The book touches quite heavily on using collective information and social site APIs, but what it is really about is data mining. Amazing read, very captivating. Instead, it explains when the algorithms can be used, and how to implement and use them appropriately. Instead the emphasis is on program code. Also basic artificial intelligence and machine learning techniques are covered (knn, neural nets, svms, decision trees, bayes rule, linear regression, clustering) and even some optimization techniques and a bit of genetic programming. I love the fact that these examples are real world and quite useful and interesting to look at (though not all of them are working as is now, things evolve quickly and so are APIs after all). great intro to ML/AI algorithms, having worked through the code I can tell you it's worth it, but have the errata page handy on O'Reilly's website as there are often slight mistakes or tweaks. Toby Segaran’s book isn’t really about the hot topic of collective intelligence but this really doesn’t matter. This book is a survey of machine learning algorithms useful for tasks like spam filters and recommendation engines. Mathematical formulas in which code snippets are based can only be found (without further explanation) on an annex. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. In that sens. It covers basic ideas from the ground up and doesn't rely on knowledge of statistics and deeper math. I'm about half way through Programming Collective Intelligence as I write this review. Maybe the newest edition doesn't have this problem. This is an incredibly useful book for all those who are looking to divine intelligence with data collected through their web apps. The author is not using Python ML ecosystem and builds all algorithms from the start - which is pretty good if you want to understand the internals of the algorithms. In typical O'Reilly fashion, there's very little math but lots of code snippets. I've started getting acquainted with machine learning with this book. are sold generally to the public as magic while in fact the concepts are readily accessible in this book. I purchsed this for advanced natural language processing research... A visionary book that illuminates the Internet, Reviewed in the United States on October 21, 2008. It's much better for coding examples and to see results quickly, but most of the times you feel there's something missing on the explanations. Toby Segaran does an excellent job of explaining the concepts behind collective intelligence then walks your thru the process of writing code to capture/analyze big data sets. It's a good book, though it has some little mistakes. This is its main merit. If you have taken graduate level statistical and computer science classes: CS ones like AI, NLP, Analysis of Algorithms and Math ones like Simulation Modeling, Forecasting, Analysis and Design of Experiements you learned how to use R and MatLab to display your results that were provided in a project or calculated using another programming language that may not have the ability to display graphs at all (lisp), this book makes the process of moving to Python for both processing (something you probably did not do in math class, as data is generally just given in the text or projects, but would do if you were taking a research course in about any given field of study) and analysis (the R/MatLab data analysis part) trivial! Some said that many explained techniques are not very useful anymore with the excessive loads of data the nowadays-appl. This is an important thing to know. It may not be a flaw with the majority of readers, but personally I wouldn't care about the collective, the Facebook API or anything like that, but I was really interested in the different ways to analyse data. I use python as my primary programming language, when I ordered this book I was concerned it would be more about website design then AI algorithms (collective intelligence encompasses a subset of soft AI algorithms that draw upon information from various sources readily avaliable on the Internet, large document collections, etc.) If he can't be bothered to continue hosting old files for people who may buy the book (or point us to somewhere to get them) we shouldn't be bothered to buy it. For many programmers like me, this opens a door to the world of machine learning. I had hoped for a bit more answering of why the more complicated algorithms can be expected to work, but this book was not written for that audience. I would recommend this book to anyone using any-type of clustering process for review and analyzing documents and data. This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. We’d love your help. I do not regret purchasing the book; however, I must say that the writer's clarity is sub-par when compared with that … I had hoped for a bit more answering of why the more complicated algorithms can be expected to work, but this book was not written for that audience. Too much focus on data scraping at the expense of algorithmic/mathematical theory. It's full of Python code snippets only work to make the subject appear accessible to the programmer, and look like waffle to me. This book is loaded with lots of algorithms related to machine learning and collaborative filtering, which is good, as there are a dearth of non-textbooks that cover this material from a practical vantage point. Just a moment while we sign you in to your Goodreads account. While you will learn some motivation for using various techniques, you won't be able to start actively using them with just the overviews in this book. Find helpful customer reviews and review ratings for Programming Collective Intelligence: Building Smart Web 2.0 Applications at Amazon.com. Goodreads helps you keep track of books you want to read. When this book fist come out in 2007, it generates quite a thrill. This is a beginner's guide to machine learning techniques. Reviewed in the United States on August 8, 2012. If you're looking for a great starting point to learning about machine learning & data analytics, this is it. Quite a good book with lots of useful code examples in Python. Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? There is little theory or mathematics used. A book for anyone wanting to tell a story, Reviewed in the United States on March 1, 2014. Python was a wise choice for the example programs as well. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. From independent presses, to tales in translation, to critical darlings and new debut novels, these books (all published in the U.S. this year)... Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? Nice if you know the math but not the programming, Reviewed in the United States on February 10, 2012. It's full of Python code snippets only work to make the subject appear accessible to the programmer, and look like waffle to me. Refresh and try again. Programming Collective Intelligence (Segaran, 2007) uses a multitude of examples to show how data can be combined and analyzed to produce results that are “more human.” The book intersperses text with Python programming snippets. This book was extremely helpful in refreshing my knowledge in many topics I came across in the fields of machine learning, data mining, and optimization. It does come up with simple but practical data set so that the algorithm makes intuitive sense. I guess half is a big value but typically it is less than that. Could be used to get high-order overview of corresponding topics, especially by non-CS peoples. This book was extremely helpful in refreshing my knowledge in many topics I came across in the fields of machine learning, data mining, and optimization. I have gotten past most of that though. The book introduces a range of machine learn algorithm solving problems such as classification, clustering and optimization by learning from data and making statistical inference. Some said that many explained techniques are not very useful anymore with the excessive loads of data the nowadays-applications are dealing with.. Your recently viewed items and featured recommendations, Select the department you want to search in, Reviewed in the United States on April 17, 2020. Very practical. Anyways, enjoy reading this book,, it is really a great book (I admired the last chapter, #12, which listed all the aforementioned algorithms along with their uses and their pros & cons). GitHub is where the world builds software. by O'Reilly Media, Programming Collective Intelligence: Building Smart Web 2.0 Applications. It could be that he has attempted to cover too many different techniques. I lost my copy of this book, which is too bad. At times, some more advanced examples require additional library downloads, but everything in the book is accessible to the reader. After viewing product detail pages, look here to find an easy way to navigate back to pages you are interested in. This is its main merit. Instead the emphasis is on program code. Good practical guide for a first contact with analytics, but does not go too deep on the explanations. It covers basic ideas from the ground up and doesn't rely on knowledge of statistics and deeper math. The book covers recommendation systems, classifiers, clustering, and regression models as well as less obvious searching and ranking, optimization and genetic programming. However, it has become outdated and it is riddled with either old syntax and errors. But in the end, this is the objective of this book. You Grab something from the models, do some funky stuff in the business logic and then present. Read honest and unbiased product reviews from our users. My favourite was chapter 11 genetic programming and chapter 4 for web search engines, bit outdated in places. When this book fist come out in 2007, it generates quite a thrill. August 23rd 2007 This is a good overview of various algorithms/techniques used by Google, Netflix and others to do things like. It's a great book if you're a practicing programmer that want to get thing done, less great if you're looking for a deep exploration of a particular topic. In that sense, this book can be taken as a reference guide on data mining. It does come up with simple but practical data set so that the algorithm makes intuiti. Prime members enjoy FREE Delivery and exclusive access to music, movies, TV shows, original audio series, and Kindle books. Good description on algorithms and their relevant use cases. This is an important thing to know, as well, and the book is much more on the applied than theoretical side. Sign up for free Dismiss master. Many of them are pretty good, but they tend to blend together. The links provided by the book are also broken, would be much better if the author had used tools like bit.ly for the URLs. Find helpful customer reviews and review ratings for Programming Collective Intelligence at Amazon.com. Welcome back. I'm about half way through Programming Collective Intelligence as I write this review. I say this is true but I think that was out of the scope of the book. Collective intelligence is the idea that small simple algorithms can produce something that is … It's an excellent book for anyone who wonders how to use data from other websites or how to use user behavior to learn how to service those users better. The biggest problem for me, which is not a fault in the book, is that most os the materials that the book uses to teach the algorithms are not available anymore or are outdated, so in the end I end up reading the book only, without applying the code. This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. The author is not using Python ML ecosystem and builds all algorithms from the start - which is pretty good if you want to understand the internals of the algorithms. Programming Collective Intelligence is easy to read, small but concise, and its only major flaw is the title; and that is because it is misleading. I'm sure this book was awesome when it first came out, it is clear, concise and has a nice follow-along structure. It may not be a flaw with the majority of readers, but personally I wouldn't care about the collective, the Facebook API or anything like that, but I was … 5 stars to this book for being easy to read and well written, presenting some really sophisticated concepts in a very neat way, and finally putting all these concepts along with interesting ideas and examples all in one place. The book introduces a range of machine learn algorithm solving problems such as classification, clustering and optimization by learning from data and making statistical inference. This is an important thing to know, as well, and the book is much more on the applied than theoretical side. If you like books and love to build cool products, we may be looking for you. The book touches quite heavily on using collective information and social site APIs, but what it is really about is data mining. Algorithm alternatives or optimizations for real-life operations are not described. You can still see all customer reviews for the product. Programming Collective Intelligence I receive dozens of computer books to review every month. This book was one of my first books about application building, as opposed to User Interface or general Computer Science.
2020 programming collective intelligence review