Certificate in Building Modern Recommenders
-- ViewingNowThe Certificate in Building Modern Recommenders is a comprehensive course that focuses on developing essential skills for creating advanced recommendation systems. This program highlights the importance of recommenders in today's data-driven world, where they play a critical role in enhancing user experiences and boosting business growth.
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โข Introduction to Recommender Systems: Understanding the basics of recommendation algorithms, their applications, and the benefits they bring to businesses.
โข Data Analysis for Recommender Systems: Learning to collect, process, and analyze data to extract meaningful insights for recommendation engines.
โข Collaborative Filtering Techniques: Exploring user-based and item-based collaborative filtering methods, their advantages, and limitations.
โข Content-Based Filtering: Diving into content-based recommendation techniques, feature engineering, and similarity measures.
โข Hybrid Recommender Systems: Combining collaborative and content-based approaches to create more accurate and diverse recommendations.
โข Matrix Factorization Techniques: Mastering matrix factorization methods, such as Singular Value Decomposition (SVD) and Alternating Least Squares (ALS).
โข Evaluation Metrics for Recommender Systems: Measuring the performance of recommenders using metrics like precision, recall, F1 score, and Mean Absolute Error (MAE).
โข Deep Learning for Recommender Systems: Applying deep learning techniques, such as neural networks and autoencoders, to improve the accuracy and scalability of recommender systems.
โข Ethical Considerations in Recommender Systems: Understanding the ethical challenges, biases, and fairness issues in recommender systems and learning how to address them.
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