Building Recommendation Systems With Python
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Hands On Recommendation Systems With Python
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Author : Rounak Banik
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-07-31
Hands On Recommendation Systems With Python written by Rounak Banik and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-31 with Computers categories.
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.
Building Recommendation Systems In Python And Jax
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Author : Bryan Bischof Ph.D
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-12-04
Building Recommendation Systems In Python And Jax written by Bryan Bischof Ph.D and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-04 with Computers categories.
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
Building Recommender Systems With Machine Learning And Ai
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Author : Frank Kane
language : en
Publisher:
Release Date : 2018
Building Recommender Systems With Machine Learning And Ai written by Frank Kane and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Automated recommendations are everywhere: Netflix, Amazon, YouTube, and more. Recommender systems learn about your unique interests and show the products or content they think you'll like best. Discover how to build your own recommender systems from one of the pioneers in the field. Frank Kane spent over nine years at Amazon, where he led the development of many of the company's personalized product recommendation technologies. In this course, he covers recommendation algorithms based on neighborhood-based collaborative filtering and more modern techniques, including matrix factorization and even deep learning with artificial neural networks. Along the way, you can learn from Frank's extensive industry experience and understand the real-world challenges of applying these algorithms at a large scale with real-world data. You can also go hands-on, developing your own framework to test algorithms and building your own neural networks using technologies like Amazon DSSTNE, AWS SageMaker, and TensorFlow.
Practical Recommender Systems
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Author : Kim Falk
language : en
Publisher: Simon and Schuster
Release Date : 2019-01-18
Practical Recommender Systems written by Kim Falk and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-18 with Computers categories.
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
Machine Learning Make Your Own Recommender System
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Author : Oliver Theobald
language : en
Publisher: Machine Learning for Beginners
Release Date : 2018-10-06
Machine Learning Make Your Own Recommender System written by Oliver Theobald and has been published by Machine Learning for Beginners this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-06 with Computers categories.
Learn How to Make Your Own Recommender System in an Afternoon.Recommender systems are one of the most visible applications of machine learning and data mining today and their uncanny ability to convert our unspoken actions into items we desire is both addicting and concerning. And whether recommender systems excite or scare you, the best way to manage their influence and impact is to understand the architecture and algorithms that play on your personal data. Recommender systems are here to stay and for anyone beginning their journey in data science, this is a lucrative space for future employment.This book will get you up and running with the basics as well as the steps to coding your own recommender system. Exercises include predicting book recommendations, relevant house properties for online marketing purposes, and whether a user will click on an ad campaign. The contents of this book is designed for beginners with some background knowledge of data science, including classical statistics and computing programming. If this is your first exposure to data science, you may want to spend a few hours to read my first book Machine Learning for Absolute Beginners before you get started here.Topics covered in this book: Setting Up A Sandbox Environment With Jupyter NotebookWorking With DataData ReductionBuilding a Collaborative Filtering ModelBuilding a Content-Based Filtering ModelEvaluationPrivacy & EthicsFuture of Recommender SystemsPlease feel welcome to join this introductory course by buying a copy or sending a free sample to your preferred device.
Recommender Systems
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Author : Charu C. Aggarwal
language : en
Publisher: Springer
Release Date : 2016-03-28
Recommender Systems written by Charu C. Aggarwal and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-28 with Computers categories.
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.
Building Recommendation Systems With Python
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Author : Eric Rodríguez
language : en
Publisher:
Release Date : 2019
Building Recommendation Systems With Python written by Eric Rodríguez and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Build real-world recommendation systems using collaborative, content-based, and hybrid filtering techniques in Python About This Video Understand how to work with real data using a recommendation in Python Graphical representation of categories or classes to visualize your data Comparison of different recommendation systems and learning to help you choose the right one In Detail Recommendation Engines have become an integral part of any application. For accurate recommendations, you require user information. The more data you feed to your engine, the more output it can generate - for example, a movie recommendation based on its rating, a YouTube video recommendation to a viewer, or recommending a product to a shopper online. In this practical course, you will be building three powerful real-world recommendation engines using three different filtering techniques. You'll start by creating usable data from your data source and implementing the best data filtering techniques for recommendations. Then you will use machine learning techniques to create your own algorithm, which will predict and recommend accurate data. By the end of the course, you'll be able to build effective online recommendation engines with machine learning and Python - on your own. Downloading the example code for this course: You can download the example code files for this course on GitHub at the following link: https://github.com/PacktPublishing/Building-Recommendation-Systems-with-Python . If you require support please email: [email protected].
R Machine Learning Projects
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Author : Dr. Sunil Kumar Chinnamgari
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-01-14
R Machine Learning Projects written by Dr. Sunil Kumar Chinnamgari and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-14 with Mathematics categories.
Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and more Key FeaturesMaster machine learning, deep learning, and predictive modeling concepts in R 3.5Build intelligent end-to-end projects for finance, retail, social media, and a variety of domainsImplement smart cognitive models with helpful tips and best practicesBook Description R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you’ll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You’ll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine. By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations. What you will learnExplore deep neural networks and various frameworks that can be used in RDevelop a joke recommendation engine to recommend jokes that match users’ tastesCreate powerful ML models with ensembles to predict employee attritionBuild autoencoders for credit card fraud detectionWork with image recognition and convolutional neural networks Make predictions for casino slot machine using reinforcement learningImplement NLP techniques for sentiment analysis and customer segmentationWho this book is for If you’re a data analyst, data scientist, or machine learning developer who wants to master machine learning concepts using R by building real-world projects, this is the book for you. Each project will help you test your skills in implementing machine learning algorithms and techniques. A basic understanding of machine learning and working knowledge of R programming is necessary to get the most out of this book.
Approaching Almost Any Machine Learning Problem
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Author : Abhishek Thakur
language : en
Publisher: Abhishek Thakur
Release Date : 2020-07-04
Approaching Almost Any Machine Learning Problem written by Abhishek Thakur and has been published by Abhishek Thakur this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-04 with Computers categories.
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
Building Machine Learning Systems With Python
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Author : Willi Richert
language : en
Publisher: Packt Publishing Ltd
Release Date : 2013-01-01
Building Machine Learning Systems With Python written by Willi Richert and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-01-01 with Computers categories.
This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on using existing technologies instead of showing how to write your own implementations of algorithms. This book is a scenario-based, example-driven tutorial. By the end of the book you will have learnt critical aspects of Machine Learning Python projects and experienced the power of ML-based systems by actually working on them.This book primarily targets Python developers who want to learn about and build Machine Learning into their projects, or who want to pro.