Develop Attention Models and Recommender Systems

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By the end of this course, learners will develop attention-based translation models, implement encoder-decoder architectures, evaluate NLP pipelines, predict outcomes from tabular data, and design recommender systems. This advanced course extends deep learning beyond basic image and text classification. Learners will explore word generation, sequence generation, text translation, data loading, and preparation for natural language processing tasks. They will construct encoder-decoder models with attention mechanisms and implement training and evaluation functions for translation systems. The course also introduces deep learning applications for structured tabular data. Learners will examine preprocessing, feature preparation, model development, and prediction workflows. Finally, they will explore collaborative filtering and other recommendation approaches used to deliver personalized suggestions. Completing this course will help learners apply deep learning across language, structured datasets, and recommendation problems. What makes the course unique is its broad application focus, combining attention-based NLP, tabular prediction, and recommender systems. This provides learners with practical experience across several high-value AI use cases commonly encountered in data science and deep learning roles.