Applied Recommender Systems With Python

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Hands-On Recommendation Systems with Python

Author : Rounak Banik
Publisher : Packt Publishing Ltd
ISBN 13 : 1788992539
Total Pages : 141 pages
Book Rating : 4.7/5 (889 download)

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Book Synopsis Hands-On Recommendation Systems with Python by : Rounak Banik

Download or read book Hands-On Recommendation Systems with Python written by Rounak Banik and published by Packt Publishing Ltd. This book was released on 2018-07-31 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: With Hands-On Recommendation Systems with Python, learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web Key Features Build industry-standard recommender systems Only familiarity with Python is required No need to wade through complicated machine learning theory to use this book Book Description Recommendation systems are at the heart of almost every internet business today; from Facebook to Netflix to Amazon. Providing good recommendations, whether it's friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform. This book shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory—you'll get started with building and learning about recommenders as quickly as possible.. In this book, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques With this book, all you need to get started with building recommendation systems is a familiarity with Python, and by the time you're fnished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains. What you will learn Get to grips with the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content based engine to recommend movies based on movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative fltering Who this book is for If you are a Python developer and want to develop applications for social networking, news personalization or smart advertising, this is the book for you. Basic knowledge of machine learning techniques will be helpful, but not mandatory.

Approaching (Almost) Any Machine Learning Problem

Author : Abhishek Thakur
Publisher : Abhishek Thakur
ISBN 13 : 8269211508
Total Pages : 300 pages
Book Rating : 4.2/5 (692 download)

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Book Synopsis Approaching (Almost) Any Machine Learning Problem by : Abhishek Thakur

Download or read book Approaching (Almost) Any Machine Learning Problem written by Abhishek Thakur and published by Abhishek Thakur. This book was released on 2020-07-04 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is not a traditional book. The book has a lot of code. If you don't like the code first approach do not buy this book. Making code available on Github is not an option. This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book doesn't explain the algorithms but is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is not for you if you are looking for pure basics. The book is for you if you are looking for guidance on approaching machine learning problems. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along. Table of contents: - Setting up your working environment - Supervised vs unsupervised learning - Cross-validation - Evaluation metrics - Arranging machine learning projects - Approaching categorical variables - Feature engineering - Feature selection - Hyperparameter optimization - Approaching image classification & segmentation - Approaching text classification/regression - Approaching ensembling and stacking - Approaching reproducible code & model serving There are no sub-headings. Important terms are written in bold. I will be answering all your queries related to the book and will be making YouTube tutorials to cover what has not been discussed in the book. To ask questions/doubts, visit this link: https://bit.ly/aamlquestions And Subscribe to my youtube channel: https://bit.ly/abhitubesub

Recommender Systems

Author : Charu C. Aggarwal
Publisher : Springer
ISBN 13 : 3319296590
Total Pages : 518 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Recommender Systems by : Charu C. Aggarwal

Download or read book Recommender Systems written by Charu C. Aggarwal and published by Springer. This book was released on 2016-03-28 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.

Applied Recommender Systems with Python

Author : Akshay Kulkarni
Publisher : Apress
ISBN 13 : 9781484289532
Total Pages : 0 pages
Book Rating : 4.2/5 (895 download)

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Book Synopsis Applied Recommender Systems with Python by : Akshay Kulkarni

Download or read book Applied Recommender Systems with Python written by Akshay Kulkarni and published by Apress. This book was released on 2022-12-08 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today. You'll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations. By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms. What You Will Learn Understand and implement different recommender systems techniques with Python Employ popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization Build hybrid recommender systems that incorporate both content-based and collaborative filtering Leverage machine learning, NLP, and deep learning for building recommender systems Who This Book Is ForData scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.

Practical Recommender Systems

Author : Kim Falk
Publisher : Simon and Schuster
ISBN 13 : 1638353980
Total Pages : 743 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Practical Recommender Systems by : Kim Falk

Download or read book Practical Recommender Systems written by Kim Falk and published by Simon and Schuster. This book was released on 2019-01-18 with total page 743 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Online recommender systems help users find movies, jobs, restaurants-even romance! There's an art in combining statistics, demographics, and query terms to achieve results that will delight them. Learn to build a recommender system the right way: it can make or break your application! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Recommender systems are everywhere, helping you find everything from movies to jobs, restaurants to hospitals, even romance. Using behavioral and demographic data, these systems make predictions about what users will be most interested in at a particular time, resulting in high-quality, ordered, personalized suggestions. Recommender systems are practically a necessity for keeping your site content current, useful, and interesting to your visitors. About the Book Practical Recommender Systems explains how recommender systems work and shows how to create and apply them for your site. After covering the basics, you'll see how to collect user data and produce personalized recommendations. You'll learn how to use the most popular recommendation algorithms and see examples of them in action on sites like Amazon and Netflix. Finally, the book covers scaling problems and other issues you'll encounter as your site grows. What's inside How to collect and understand user behavior Collaborative and content-based filtering Machine learning algorithms Real-world examples in Python About the Reader Readers need intermediate programming and database skills. About the Author Kim Falk is an experienced data scientist who works daily with machine learning and recommender systems. Table of Contents PART 1 - GETTING READY FOR RECOMMENDER SYSTEMS What is a recommender? User behavior and how to collect it Monitoring the system Ratings and how to calculate them Non-personalized recommendations The user (and content) who came in from the cold PART 2 - RECOMMENDER ALGORITHMS Finding similarities among users and among content Collaborative filtering in the neighborhood Evaluating and testing your recommender Content-based filtering Finding hidden genres with matrix factorization Taking the best of all algorithms: implementing hybrid recommenders Ranking and learning to rank Future of recommender systems

Recommender Systems Handbook

Author : Francesco Ricci
Publisher : Springer
ISBN 13 : 148997637X
Total Pages : 1008 pages
Book Rating : 4.4/5 (899 download)

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Book Synopsis Recommender Systems Handbook by : Francesco Ricci

Download or read book Recommender Systems Handbook written by Francesco Ricci and published by Springer. This book was released on 2015-11-17 with total page 1008 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems.

Statistical Methods for Recommender Systems

Author : Deepak K. Agarwal
Publisher : Cambridge University Press
ISBN 13 : 1316565130
Total Pages : 317 pages
Book Rating : 4.3/5 (165 download)

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Book Synopsis Statistical Methods for Recommender Systems by : Deepak K. Agarwal

Download or read book Statistical Methods for Recommender Systems written by Deepak K. Agarwal and published by Cambridge University Press. This book was released on 2016-02-24 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designing algorithms to recommend items such as news articles and movies to users is a challenging task in numerous web applications. The crux of the problem is to rank items based on users' responses to different items to optimize for multiple objectives. Major technical challenges are high dimensional prediction with sparse data and constructing high dimensional sequential designs to collect data for user modeling and system design. This comprehensive treatment of the statistical issues that arise in recommender systems includes detailed, in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods), bilinear random-effects models (matrix factorization) and scalable model fitting using modern computing paradigms like MapReduce. The authors draw upon their vast experience working with such large-scale systems at Yahoo! and LinkedIn, and bridge the gap between theory and practice by illustrating complex concepts with examples from applications they are directly involved with.

Hands-On Unsupervised Learning Using Python

Author : Ankur A. Patel
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492035599
Total Pages : 310 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Hands-On Unsupervised Learning Using Python by : Ankur A. Patel

Download or read book Hands-On Unsupervised Learning Using Python written by Ankur A. Patel and published by "O'Reilly Media, Inc.". This book was released on 2019-02-21 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started. Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning Set up and manage machine learning projects end-to-end Build an anomaly detection system to catch credit card fraud Clusters users into distinct and homogeneous groups Perform semisupervised learning Develop movie recommender systems using restricted Boltzmann machines Generate synthetic images using generative adversarial networks

Building Recommendation Systems in Python and JAX

Author : Bryan Bischof Ph.D
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492097950
Total Pages : 296 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Building Recommendation Systems in Python and JAX by : Bryan Bischof Ph.D

Download or read book Building Recommendation Systems in Python and JAX written by Bryan Bischof Ph.D and published by "O'Reilly Media, Inc.". This book was released on 2023-12-04 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Implementing and designing systems that make suggestions to users are among the most popular and essential machine learning applications available. Whether you want customers to find the most appealing items at your online store, videos to enrich and entertain them, or news they need to know, recommendation systems (RecSys) provide the way. In this practical book, authors Bryan Bischof and Hector Yee illustrate the core concepts and examples to help you create a RecSys for any industry or scale. You'll learn the math, ideas, and implementation details you need to succeed. This book includes the RecSys platform components, relevant MLOps tools in your stack, plus code examples and helpful suggestions in PySpark, SparkSQL, FastAPI, and Weights & Biases. You'll learn: The data essential for building a RecSys How to frame your data and business as a RecSys problem Ways to evaluate models appropriate for your system Methods to implement, train, test, and deploy the model you choose Metrics you need to track to ensure your system is working as planned How to improve your system as you learn more about your users, products, and business case

Mahout in Action

Author : Sean Owen
Publisher : Simon and Schuster
ISBN 13 : 1638355371
Total Pages : 616 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Mahout in Action by : Sean Owen

Download or read book Mahout in Action written by Sean Owen and published by Simon and Schuster. This book was released on 2011-10-04 with total page 616 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Mahout in Action is a hands-on introduction to machine learning with Apache Mahout. Following real-world examples, the book presents practical use cases and then illustrates how Mahout can be applied to solve them. Includes a free audio- and video-enhanced ebook. About the Technology A computer system that learns and adapts as it collects data can be really powerful. Mahout, Apache's open source machine learning project, captures the core algorithms of recommendation systems, classification, and clustering in ready-to-use, scalable libraries. With Mahout, you can immediately apply to your own projects the machine learning techniques that drive Amazon, Netflix, and others. About this Book This book covers machine learning using Apache Mahout. Based on experience with real-world applications, it introduces practical use cases and illustrates how Mahout can be applied to solve them. It places particular focus on issues of scalability and how to apply these techniques against large data sets using the Apache Hadoop framework. This book is written for developers familiar with Java -- no prior experience with Mahout is assumed. Owners of a Manning pBook purchased anywhere in the world can download a free eBook from manning.com at any time. They can do so multiple times and in any or all formats available (PDF, ePub or Kindle). To do so, customers must register their printed copy on Manning's site by creating a user account and then following instructions printed on the pBook registration insert at the front of the book. What's Inside Use group data to make individual recommendations Find logical clusters within your data Filter and refine with on-the-fly classification Free audio and video extras Table of Contents Meet Apache Mahout PART 1 RECOMMENDATIONS Introducing recommenders Representing recommender data Making recommendations Taking recommenders to production Distributing recommendation computations PART 2 CLUSTERING Introduction to clustering Representing data Clustering algorithms in Mahout Evaluating and improving clustering quality Taking clustering to production Real-world applications of clustering PART 3 CLASSIFICATION Introduction to classification Training a classifier Evaluating and tuning a classifier Deploying a classifier Case study: Shop It To Me

Agile Technical Practices Distilled

Author : Pedro M. Santos
Publisher : Packt Publishing Ltd
ISBN 13 : 1838986626
Total Pages : 443 pages
Book Rating : 4.8/5 (389 download)

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Book Synopsis Agile Technical Practices Distilled by : Pedro M. Santos

Download or read book Agile Technical Practices Distilled written by Pedro M. Santos and published by Packt Publishing Ltd. This book was released on 2019-06-28 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: Delve deep into the various technical practices, principles, and values of Agile. Key FeaturesDiscover the essence of Agile software development and the key principles of software designExplore the fundamental practices of Agile working, including test-driven development (TDD), refactoring, pair programming, and continuous integrationLearn and apply the four elements of simple designBook Description The number of popular technical practices has grown exponentially in the last few years. Learning the common fundamental software development practices can help you become a better programmer. This book uses the term Agile as a wide umbrella and covers Agile principles and practices, as well as most methodologies associated with it. You’ll begin by discovering how driver-navigator, chess clock, and other techniques used in the pair programming approach introduce discipline while writing code. You’ll then learn to safely change the design of your code using refactoring. While learning these techniques, you’ll also explore various best practices to write efficient tests. The concluding chapters of the book delve deep into the SOLID principles - the five design principles that you can use to make your software more understandable, flexible and maintainable. By the end of the book, you will have discovered new ideas for improving your software design skills, the relationship within your team, and the way your business works. What you will learnLearn the red, green, refactor cycle of classic TDD and practice the best habits such as the rule of 3, triangulation, object calisthenics, and moreRefactor using parallel change and improve legacy code with characterization tests, approval tests, and Golden MasterUse code smells as feedback to improve your designLearn the double cycle of ATDD and the outside-in mindset using mocks and stubs correctly in your testsUnderstand how Coupling, Cohesion, Connascence, SOLID principles, and code smells are all relatedImprove the understanding of your business domain using BDD and other principles for "doing the right thing, not only the thing right"Who this book is for This book is designed for software developers looking to improve their technical practices. Software coaches may also find it helpful as a teaching reference manual. This is not a beginner's book on how to program. You must be comfortable with at least one programming language and must be able to write unit tests using any unit testing framework.

Pro Machine Learning Algorithms

Author : V Kishore Ayyadevara
Publisher : Apress
ISBN 13 : 1484235649
Total Pages : 379 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Pro Machine Learning Algorithms by : V Kishore Ayyadevara

Download or read book Pro Machine Learning Algorithms written by V Kishore Ayyadevara and published by Apress. This book was released on 2018-06-30 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a practical understanding of all the levers that can be tuned in a model, before implementing the models in Python/R. You will cover all the major algorithms: supervised and unsupervised learning, which include linear/logistic regression; k-means clustering; PCA; recommender system; decision tree; random forest; GBM; and neural networks. You will also be exposed to the latest in deep learning through CNNs, RNNs, and word2vec for text mining. You will be learning not only the algorithms, but also the concepts of feature engineering to maximize the performance of a model. You will see the theory along with case studies, such as sentiment classification, fraud detection, recommender systems, and image recognition, so that you get the best of both theory and practice for the vast majority of the machine learning algorithms used in industry. Along with learning the algorithms, you will also be exposed to running machine-learning models on all the major cloud service providers. You are expected to have minimal knowledge of statistics/software programming and by the end of this book you should be able to work on a machine learning project with confidence. What You Will Learn Get an in-depth understanding of all the major machine learning and deep learning algorithms Fully appreciate the pitfalls to avoid while building models Implement machine learning algorithms in the cloud Follow a hands-on approach through case studies for each algorithm Gain the tricks of ensemble learning to build more accurate models Discover the basics of programming in R/Python and the Keras framework for deep learning Who This Book Is For Business analysts/ IT professionals who want to transition into data science roles. Data scientists who want to solidify their knowledge in machine learning.

Practical Guide to Applied Conformal Prediction in Python

Author : Valery Manokhin
Publisher : Packt Publishing Ltd
ISBN 13 : 1805120913
Total Pages : 240 pages
Book Rating : 4.8/5 (51 download)

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Book Synopsis Practical Guide to Applied Conformal Prediction in Python by : Valery Manokhin

Download or read book Practical Guide to Applied Conformal Prediction in Python written by Valery Manokhin and published by Packt Publishing Ltd. This book was released on 2023-12-20 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Elevate your machine learning skills using the Conformal Prediction framework for uncertainty quantification. Dive into unique strategies, overcome real-world challenges, and become confident and precise with forecasting. Key Features Master Conformal Prediction, a fast-growing ML framework, with Python applications Explore cutting-edge methods to measure and manage uncertainty in industry applications Understand how Conformal Prediction differs from traditional machine learning Book DescriptionIn the rapidly evolving landscape of machine learning, the ability to accurately quantify uncertainty is pivotal. The book addresses this need by offering an in-depth exploration of Conformal Prediction, a cutting-edge framework to manage uncertainty in various ML applications. Learn how Conformal Prediction excels in calibrating classification models, produces well-calibrated prediction intervals for regression, and resolves challenges in time series forecasting and imbalanced data. Discover specialised applications of conformal prediction in cutting-edge domains like computer vision and NLP. Each chapter delves into specific aspects, offering hands-on insights and best practices for enhancing prediction reliability. The book concludes with a focus on multi-class classification nuances, providing expert-level proficiency to seamlessly integrate Conformal Prediction into diverse industries. With practical examples in Python using real-world datasets, expert insights, and open-source library applications, you will gain a solid understanding of this modern framework for uncertainty quantification. By the end of this book, you will be able to master Conformal Prediction in Python with a blend of theory and practical application, enabling you to confidently apply this powerful framework to quantify uncertainty in diverse fields.What you will learn The fundamental concepts and principles of conformal prediction Learn how conformal prediction differs from traditional ML methods Apply real-world examples to your own industry applications Explore advanced topics - imbalanced data and multi-class CP Dive into the details of the conformal prediction framework Boost your career as a data scientist, ML engineer, or researcher Learn to apply conformal prediction to forecasting and NLP Who this book is for Ideal for readers with a basic understanding of machine learning concepts and Python programming, this book caters to data scientists, ML engineers, academics, and anyone keen on advancing their skills in uncertainty quantification in ML.

Programming Collective Intelligence

Author : Toby Segaran
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 0596550685
Total Pages : 361 pages
Book Rating : 4.5/5 (965 download)

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Book Synopsis Programming Collective Intelligence by : Toby Segaran

Download or read book Programming Collective Intelligence written by Toby Segaran and published by "O'Reilly Media, Inc.". This book was released on 2007-08-16 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? 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. 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. 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. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." -- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths." -- Tim Wolters, CTO, Collective Intellect

Applied Text Analysis with Python

Author : Benjamin Bengfort
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491962992
Total Pages : 328 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Applied Text Analysis with Python by : Benjamin Bengfort

Download or read book Applied Text Analysis with Python written by Benjamin Bengfort and published by "O'Reilly Media, Inc.". This book was released on 2018-06-11 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning. You’ll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you’ll be equipped with practical methods to solve any number of complex real-world problems. Preprocess and vectorize text into high-dimensional feature representations Perform document classification and topic modeling Steer the model selection process with visual diagnostics Extract key phrases, named entities, and graph structures to reason about data in text Build a dialog framework to enable chatbots and language-driven interaction Use Spark to scale processing power and neural networks to scale model complexity

Collaborative Filtering Recommender Systems

Author : Michael D. Ekstrand
Publisher : Now Publishers Inc
ISBN 13 : 1601984421
Total Pages : 104 pages
Book Rating : 4.6/5 (19 download)

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Book Synopsis Collaborative Filtering Recommender Systems by : Michael D. Ekstrand

Download or read book Collaborative Filtering Recommender Systems written by Michael D. Ekstrand and published by Now Publishers Inc. This book was released on 2011 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Collaborative Filtering Recommender Systems discusses a wide variety of the recommender choices available and their implications, providing both practitioners and researchers with an introduction to the important issues underlying recommenders and current best practices for addressing these issues.

Recommendation Engines

Author : Michael Schrage
Publisher : MIT Press
ISBN 13 : 0262358786
Total Pages : 306 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Recommendation Engines by : Michael Schrage

Download or read book Recommendation Engines written by Michael Schrage and published by MIT Press. This book was released on 2020-09-01 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: How companies like Amazon, Netflix, and Spotify know what "you might also like": the history, technology, business, and societal impact of online recommendation engines. Increasingly, our technologies are giving us better, faster, smarter, and more personal advice than our own families and best friends. Amazon already knows what kind of books and household goods you like and is more than eager to recommend more; YouTube and TikTok always have another video lined up to show you; Netflix has crunched the numbers of your viewing habits to suggest whole genres that you would enjoy. In this volume in the MIT Press's Essential Knowledge series, innovation expert Michael Schrage explains the origins, technologies, business applications, and increasing societal impact of recommendation engines, the systems that allow companies worldwide to know what products, services, and experiences "you might also like."