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

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.

這本書將教你如何使用Python機器學習演算法建立推薦系統。推薦系統已成為現今每個基於互聯網的企業的重要組成部分。

你將從學習推薦系統的基本概念開始，瞭解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（如市場籃分析）以及利用自然語言處理（NLP）建立基於內容和知識的推薦系統。作者們還演示了使用矩陣分解進行協同過濾以及結合基於內容和協同過濾技術的混合推薦系統的技巧。接著，介紹了使用聚類和分類演算法（如K-means和隨機森林）建立基於機器學習的推薦系統的教程。最後幾章涵蓋了使用NLP、深度學習和基於圖形的技術來建立推薦引擎。每個章節都包括數據準備、多種評估和優化推薦系統的方法、支援示例和插圖。

通過閱讀本書，你將理解並能夠使用機器學習、深度學習和基於圖形的演算法以及其他工具和技術建立推薦系統。

你將學到以下內容：
- 使用Python理解並實現不同的推薦系統技術
- 運用流行的方法，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
- 建立結合基於內容和協同過濾的混合推薦系統
- 利用機器學習、NLP和深度學習來建立推薦系統

本書適合數據科學家、機器學習工程師和Python程序員，他們有興趣建立和實施推薦系統來解決問題。

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.

Akshay R Kulkarni是一位AI和機器學習的倡導者和思想領袖。他曾為多家財富500強和全球企業提供咨詢，推動AI和數據科學引領的戰略轉型。他是一位Google開發者、作家，並經常在主要的AI和數據科學會議上發表演講，包括Strata、O'Reilly AI Conf和GIDS。他還是印度一些頂尖研究所的客座教師。2019年，他被評為印度40位40歲以下的頂尖數據科學家之一。在閒暇時間，他喜歡閱讀、寫作、編程和幫助有志成為數據科學家的人。他與家人一起居住在班加羅爾。

Adarsha Shivananda是數據科學和MLOps領域的領導者。他致力於建立世界一流的MLOps能力，以確保從AI中持續提供價值。他的目標是在組織內外建立一支優秀的數據科學家團隊，通過培訓計劃解決問題，並始終保持領先。他在製藥、醫療保健、消費品、零售和營銷領域有豐富的工作經驗。他居住在班加羅爾，喜歡閱讀和教授數據科學。

Anoosh Kulkarni是一位數據科學家和AI顧問。他曾與多個領域的全球客戶合作，利用機器學習（ML）、自然語言處理（NLP）和深度學習幫助他們解決業務問題。Anoosh熱衷於指導和指導人們在數據科學之旅中的成長。他領導數據科學/機器學習聚會，幫助有志成為數據科學家的人規劃職業生涯。他還在大學進行機器學習/人工智能研討會，積極參與關於AI和數據科學的網絡研討會、講座和活動。他與家人一起居住在班加羅爾。

V Adithya Krishnan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，廣泛應用先進的機器學習（ML）應用解決他們的業務問題。他在AI-ML的多個領域擁有經驗，包括時間序列預測、深度學習、NLP、ML運營、圖像處理和數據分析。目前，他正在開發一個先進的價值可觀察性套件，用於在生產中的模型中進行持續的模型和數據監控，並實現業務價值。他還與DRDO合作在IEEE會議上發表了一篇名為“基於深度學習的範圍估計方法”的論文。他與家人一起居住在金奈。