Applied Recommender Systems with Python: Build Recommender Systems with Deep Learning, Nlp and Graph-Based Techniques
暫譯: 使用 Python 應用推薦系統：利用深度學習、自然語言處理和圖形技術構建推薦系統

Kulkarni, Akshay, Shivananda, Adarsha, Kulkarni, Anoosh

Applied Recommender Systems with Python: Build Recommender Systems with Deep Learning, Nlp and Graph-Based Techniques

商品描述

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.

商品描述(中文翻譯)

這本書將教你如何使用 Python 和機器學習演算法來構建推薦系統。推薦系統已成為當今每個基於互聯網的商業的重要組成部分。

你將從學習推薦系統的基本概念開始，概述不同類型的推薦引擎及其運作方式。接下來，你將看到如何使用傳統演算法來構建推薦系統，例如市場籃分析以及基於內容和知識的推薦系統，並結合自然語言處理（NLP）。作者接著展示了使用矩陣分解的協同過濾技術和結合內容基礎與協同過濾技術的混合推薦系統。隨後是一個關於使用聚類和分類演算法（如 K-means 和隨機森林）構建基於機器學習的推薦系統的教程。最後幾章涵蓋了 NLP、深度學習和基於圖的技術來構建推薦引擎。每一章都包括數據準備、多種評估和優化推薦系統的方法、支持的範例和插圖。

在本書結束時，你將理解並能夠使用各種工具和技術構建推薦系統，包括機器學習、深度學習和基於圖的演算法。

你將學到的內容：
- 理解並實現不同的推薦系統技術，使用 Python
- 採用流行的方法，如基於內容和知識的推薦、協同過濾、市場籃分析和矩陣分解
- 構建結合內容基礎和協同過濾的混合推薦系統
- 利用機器學習、NLP 和深度學習來構建推薦系統

本書適合對象：
數據科學家、機器學習工程師和有興趣構建和實施推薦系統以解決問題的 Python 程式設計師。

作者簡介

Akshay R Kulkarni is an AI and machine learning evangelist and a thought leader. He has consulted several Fortune 500 and global enterprises to drive AI and data science-led strategic transformations. He is a Google developer, Author, and a regular speaker at major AI and data science conferences including Strata, O'Reilly AI Conf, and GIDS. He is a visiting faculty member for some of the top graduate institutes in India. In 2019, he has been also featured as one of the top 40 under 40 Data Scientists in India. In his spare time, he enjoys reading, writing, coding, and helping aspiring data scientists. He lives in Bangalore with his family.

Adarsha Shivananda is Data science and MLOps Leader. He is working on creating world-class MLOps capabilities to ensure continuous value delivery from AI. He aims to build a pool of exceptional data scientists within and outside of the organization to solve problems through training programs, and always wants to stay ahead of the curve. He has worked extensively in the pharma, healthcare, CPG, retail, and marketing domains. He lives in Bangalore and loves to read and teach data science.

Anoosh Kulkarni is a data scientist and an AI consultant. He has worked with global clients across multiple domains and helped them solve their business problems using machine learning (ML), natural language processing (NLP), and deep learning. Anoosh is passionate about guiding and mentoring people in their data science journey. He leads data science/machine learning meet-ups and helps aspiring data scientists navigate their careers. He also conducts ML/AI workshops at universities and is actively involved in conducting webinars, talks, and sessions on AI and data science. He lives in Bangalore with his family.

V Adithya Krishnan is a data scientist and ML Ops Engineer. He has worked with various global clients across multiple domains and helped them to solve their business problems extensively using advanced Machine learning (ML) applications. He has experience across multiple fields of AI-ML, including, Time-series forecasting, Deep Learning, NLP, ML Operations, Image processing, and data analytics. Presently, he is developing a state-of-the-art value observability suite for models in production, which includes continuous model and data monitoring along with the business value realized. He also published a paper at an IEEE conference, "Deep Learning Based Approach for Range Estimation", written in collaboration with the DRDO. He lives in Chennai with his family.

作者簡介(中文翻譯)

阿克夏·R·庫爾卡尼 是一位人工智慧和機器學習的推廣者及思想領袖。他曾為多家《財富》500 強企業及全球企業提供諮詢，推動以人工智慧和數據科學為主導的戰略轉型。他是 Google 開發者、作者，並且是多個主要人工智慧和數據科學會議的常規演講者，包括 Strata、O'Reilly AI Conf 和 GIDS。他是印度一些頂尖研究所的客座教授。2019 年，他被評選為印度 40 位以下的頂尖數據科學家之一。在空閒時間，他喜歡閱讀、寫作、編程，並幫助有志於成為數據科學家的朋友。他與家人居住在班加羅爾。

阿達爾沙·希瓦南達 是數據科學和 MLOps 領導者。他致力於創建世界級的 MLOps 能力，以確保從人工智慧中持續交付價值。他的目標是在組織內外建立一支卓越的數據科學家團隊，通過培訓計劃解決問題，並始終希望走在潮流的前端。他在製藥、醫療保健、消費品、零售和市場營銷領域有廣泛的工作經驗。他居住在班加羅爾，熱愛閱讀和教授數據科學。

阿努什·庫爾卡尼 是一位數據科學家和人工智慧顧問。他曾與多個領域的全球客戶合作，幫助他們利用機器學習（ML）、自然語言處理（NLP）和深度學習解決商業問題。阿努什熱衷於指導和輔導人們在數據科學的旅程中。他主導數據科學/機器學習的聚會，幫助有志於成為數據科學家的朋友規劃職業生涯。他還在大學舉辦 ML/AI 工作坊，並積極參與舉辦有關人工智慧和數據科學的網絡研討會、演講和會議。他與家人居住在班加羅爾。

V·阿迪提亞·克里希南 是一位數據科學家和 ML Ops 工程師。他曾與多個領域的全球客戶合作，廣泛利用先進的機器學習（ML）應用幫助他們解決商業問題。他在人工智慧和機器學習的多個領域擁有經驗，包括時間序列預測、深度學習、自然語言處理、機器學習運營、影像處理和數據分析。目前，他正在為生產中的模型開發一套最先進的價值可觀察性套件，該套件包括持續的模型和數據監控以及實現的商業價值。他還在 IEEE 會議上發表了一篇論文，題為《基於深度學習的範圍估計方法》，該論文是與 DRDO 合作撰寫的。他與家人居住在金奈。

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