Build AI Recommender Systems

Introduction to recommender systems, setup of development environment with Anaconda, and basics of creating simple movie recommendations.

Coverage of Python essentials including syntax, data structures, functions, and practical challenges with Boolean expressions and loops.

Exploration of recommender system evaluation methods including accuracy metrics, hit rates, and diversity measures.

Overview of recommender engine architecture with code walkthroughs and implementation activities.

Deep dive into content-based filtering methods, including cosine similarity and KNN, with practical movie recommendation activities.

Comprehensive coverage of collaborative filtering techniques including user-based and item-based methods with practical exercises.

Exploration of matrix factorization techniques including PCA and SVD, with focus on implementation and optimization.

Optional deep dive into deep learning fundamentals, neural networks, and practical implementations with TensorFlow and Keras.

Application of deep learning to recommender systems, including RBMs and auto-encoders with practical evaluation methods.

Methods for scaling recommendation systems using Apache Spark and cloud solutions like Amazon's DSSTNE and SageMaker.

Addressing practical challenges in recommender systems including cold start problems, filtering bubbles, and fraud.

Analysis of real-world recommendation systems from YouTube and Netflix, focusing on their strategies and implementations.

Exploration of hybrid recommendation approaches combining multiple algorithms for improved accuracy and diversity.

Course summary with resources for further study and introduction to advanced topics and emerging trends.