Applied Recommender Systems with Python

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 For Data scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.

Covers hybrid recommender systems, deep learning-based techniques, and graph-based recommender systems Includes step-by-step implementation of all techniques using Python with real-world examples Explains end-to-end pipeline from defining the approach, pre-processing data, and building models

Auteur
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.

Contenu
Chapter 1: Introduction to Recommender Systems.- Chapter 2: Association Rule Mining.- Chapter 3: Content and Knowledge-Based Recommender System.- Chapter 4: Collaborative Filtering using KNN.- Chapter 5: Collaborative Filtering Using Matrix Factorization, SVD and ALS.- Chapter 6: Hybrid Recommender System.- Chapter 7: Clustering Algorithm-Based Recommender System.- Chapter 8: Classification Algorithm-Based Recommender System.- Chapter 9: Deep Learning and NLP Based Recommender System.- Chapter 10: Graph-Based Recommender System. - Chapter 11: Emerging Areas and Techniques in Recommender System.