This is the first time I've actually taken the time to write out a review. There's a point every developer hits, that point where everything seems mundane, repetitive and not worth doing anymore. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. Very practical. There's a problem loading this menu right now. GitHub is where the world builds software. You Grab something from the models, do some funky stuff in the business logic and then present. Instead, it explains when the algorithms can be used, and how to implement and use them appropriately. Highly recommended. 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. The book touches quite heavily on using collective information and social site APIs, but what it is really about is data mining. You know what to expect, your know how to do it. When this book fist come out in 2007, it generates quite a thrill. It covers basic ideas from the ground up and doesn't rely on knowledge of statistics and deeper math. 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. Regardless, he tends to gloss over important details and not explain his rationale for many key points in the algorithms he lays out. 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. It also analyzes reviews to verify trustworthiness. Overall, I give this book 4 stars. If you like books and love to build cool products, we may be looking for you. Taxonomic, clustering, neural networks, etc. There is little theory or mathematics used. Instead of reading magazines and newspapers we use blogs as our source of news. Start by marking “Programming Collective Intelligence: Building Smart Web 2.0 Applications” as Want to Read: Error rating book. 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. 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. I have gotten past most of that though. Nice learning curve, no sudden unexplained jumps. Maybe the newest edition doesn't have this problem. In typical O'Reilly fashion, there's very little math but lots of code snippets. In that sens. The book covers recommendation systems, classifiers, clustering, and regression models as well as less obvious searching and ranking, optimization and genetic programming. 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. Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. You might go on and try another language to spice up your life, but you then, again, realise same old, same old. 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. 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. This book does a good job making an introduction of machine learning technologies to the average programmer. To calculate the overall star rating and percentage breakdown by star, we don’t use a simple average. I do not regret purchasing the book; however, I must say that the writer's clarity is sub-par when compared with that … For many programmers like me, this opens a door to the world of machine learning. by O'Reilly Media, Programming Collective Intelligence: Building Smart Web 2.0 Applications. In a typical newspaper, how much of its content is of interest to a reader? 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. 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. Some said that many explained techniques are not very useful anymore with the excessive loads of data the nowadays-applications are dealing with.. 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. 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.) 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'm about half way through Programming Collective Intelligence as I write this review. Welcome back. 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. For many programmers like me, this opens a door to the world of machine learning. Reviewed in the United States on August 8, 2012. 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. Very good introduction into machine-learning, information retrieval & data mining related questions. 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. 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! 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 if you know the math but not the programming, Reviewed in the United States on February 10, 2012. 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. 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. You can still see all customer reviews for the product. 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 was extremely helpful in refreshing my knowledge in many topics I came across in the fields of machine learning, data mining, and optimization. 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 guess half is a big value but typically it is less than that. 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. The programming code allows someone to work through all of the examples discussed in the book. 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. Find helpful customer reviews and review ratings for Programming Collective Intelligence: Building Smart Web 2.0 Applications at Amazon.com. I would recommend this book to anyone using any-type of clustering process for review and analyzing documents and data. 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. At times, some more advanced examples require additional library downloads, but everything in the book is accessible to the reader. I've started getting acquainted with machine learning with this book. Excellent Resource for Clustering Algorithoms and Other AI Algorithoms, Reviewed in the United States on April 23, 2008. How do we process information in the Internet age? August 23rd 2007 Prime members enjoy FREE Delivery and exclusive access to music, movies, TV shows, original audio series, and Kindle books. This is an important thing to know. 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. 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. My favourite was chapter 11 genetic programming and chapter 4 for web search engines, bit outdated in places. Sign up for free Dismiss master. 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. This is a visionary book because it predicts a lot of what will happen to the Internet soon. 5 years ago, this may have been *the* book for the aspiring Artificial Intelligence practioner. 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. Mathematical formulas in which code snippets are based can only be found (without further expla. I lost my copy of this book, which is too bad. This is a good overview of various algorithms/techniques used by Google, Netflix and others to do things like. 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. 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. With simple but practical data set so that the algorithm makes intuitive sense 's fan group 33... 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