In this module, we will guide you on how to obtain the necessary code for the course and set up your working environment. Additionally, we’ll provide key tips and strategies to help you succeed as you progress through the course. This section ensures you're fully prepared to dive into the content effectively.

In this module, we will explore the problem of spam detection and introduce the Naive Bayes algorithm, focusing on its intuition. You will also work through an exercise prompt and gain insights into key metrics like ROC, AUC, and F1 score, with a special emphasis on handling class imbalance. Finally, we will guide you through implementing a spam detection model in Python.

In this module, we will introduce the concept of sentiment analysis and discuss its wide-ranging applications. You'll gain insights into logistic regression, including both binary and multiclass classifications, and learn how to train and interpret these models. The module will conclude with hands-on exercises, guiding you through implementing sentiment analysis in Python across two parts.

In this module, we will introduce text summarization and discuss its core principles and techniques. You’ll learn how to implement both vector-based and TextRank summarization methods, from basic to advanced levels, using Python. The module includes exercises and hands-on coding to reinforce concepts, ensuring a thorough understanding of text summarization.

In this module, we will introduce topic modeling and its significance in NLP. You will learn about Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) algorithms, exploring both their theoretical foundations and practical applications. Hands-on coding will guide you through implementing topic modeling in Python, helping you extract meaningful topics from text data.

In this module, we will introduce Latent Semantic Analysis (LSA) and Latent Semantic Indexing (LSI), focusing on their application in natural language processing. You will learn the intuition behind Singular Value Decomposition (SVD) and how to apply it to NLP tasks. The module includes practical implementation of LSA and LSI in Python, followed by exercises to help solidify your understanding.