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.

• 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