In this module, we will cover the essentials of Python programming, including basic syntax, data structures, and functions. We will also delve into Boolean expressions and loops through hands-on challenges.

In this module, we will explore various methods for evaluating recommender systems, including accuracy metrics, hit rates, and diversity measures. We will also review practical examples and quizzes to reinforce learning.

In this module, we will focus on the architecture of a recommender engine framework, guiding you through code walkthroughs and activities to implement and test various recommendation algorithms.

In this module, we will dive into content-based filtering methods, exploring metrics like cosine similarity and KNN. We will also conduct hands-on activities to produce and evaluate movie recommendations.

In this module, we will cover neighborhood-based collaborative filtering techniques, including user-based and item-based methods. Practical exercises and activities will help solidify your understanding of these approaches.

In this module, we will explore matrix factorization methods like PCA and SVD, demonstrating how to apply these techniques to movie rating datasets. We will also focus on improving these methods through hyperparameter tuning.

In this module, we will provide an optional deep dive into deep learning, covering fundamental concepts, neural network architectures, and practical implementations using TensorFlow and Keras.

In this module, we will focus on applying deep learning to recommender systems, exploring techniques like Restricted Boltzmann Machines (RBM) and auto-encoders. We will also cover practical evaluation and tuning methods.

In this module, we will explore methods to scale up recommendation systems, including using Apache Spark for large-scale data processing and Amazon's DSSTNE and SageMaker for deploying scalable machine learning models.

In this module, we will tackle real-world challenges faced by recommender systems, such as the cold start problem, filtering bubbles, and fraud. We will also explore solutions to these issues through practical exercises.

In this module, we will study real-world case studies of YouTube and Netflix, focusing on their recommendation strategies and the use of deep learning and hybrid approaches to enhance recommendation quality.

In this module, we will explore hybrid recommendation approaches, combining multiple algorithms to improve recommendation accuracy and diversity. Practical exercises will guide you through implementing and evaluating hybrid systems.

In this module, we will wrap up the course by summarizing key points, providing resources for further study, and introducing advanced topics and emerging trends in recommender systems to keep you up-to-date.