This module introduces key techniques for converting text into numerical data for natural language processing, including bag-of-words, tokenization, and self-attention models. Learners will explore how machines interpret, process, and classify text using both traditional and deep learning approaches, and how pre-trained models can enhance sentiment analysis. Practical challenges such as character encoding and text representation are also addressed.

This module introduces the fundamentals of recommender systems, focusing on how collaborative filtering and Singular Value Decomposition (SVD) are used to personalize product and movie suggestions. Learners will explore real-world datasets, understand similarity measures, and discover how advanced techniques extract meaningful patterns from user behavior data.

This module explores practical strategies to enhance machine learning model performance, including analyzing learning curves, selecting appropriate evaluation metrics, and applying feature engineering techniques. Learners will gain hands-on experience with cross-validation and hyperparameter optimization to address bias and variance issues.

This module examines the principles of ethical data handling, focusing on legal compliance and the challenges of using personal information in AI training. Learners will explore common misconceptions about data ownership, pitfalls in inferring sensitive information, and strategies for recognizing rare but impactful events in data-driven systems.