In this module, we will help you get started by showing you where to access the course code and supporting resources. You'll also receive actionable advice on how to stay engaged and make the most of your learning journey. This foundational setup ensures you're fully prepared for the lessons ahead.

In this module, we will explore the real-world problem of spam detection using machine learning. You'll gain a solid understanding of the Naive Bayes algorithm, key evaluation metrics, and how to handle class imbalance. The module concludes with a hands-on implementation of a spam classifier in Python.

In this module, we will dive into sentiment analysis—a key application of NLP used to determine the emotional tone of text. You’ll learn the intuition and mechanics behind logistic regression and explore both binary and multiclass scenarios. The module wraps up with a guided Python implementation, allowing you to apply these concepts in practice.

In this module, we will explore the field of text summarization and the different strategies used to condense large volumes of text. You'll learn both vector-based methods and the more advanced TextRank algorithm, with intuitive explanations and hands-on Python implementations. This section includes guided exercises for all skill levels, ensuring a strong grasp of summarization techniques.

In this module, we will dive into topic modeling techniques that help uncover the underlying themes within large text datasets. You'll explore both LDA and NMF, learning the theory, intuition, and practical implementation of each. By the end, you’ll be equipped to apply topic modeling in Python and analyze results effectively.

In this module, we will explore Latent Semantic Analysis and Indexing, techniques used to discover hidden patterns and meanings in text data. You'll gain a conceptual understanding of Singular Value Decomposition and how it's applied to NLP tasks. The module includes Python-based implementation and exercises to deepen your practical skills.