An introduction to the world of Natural Language Processing (NLP). Get acquainted with the basic concepts of NLP and understand its significance in today's world.

Preprocessing is a fundamental part of any NLP task. Learn about the various techniques employed to clean and prepare textual data, ranging from basic tasks like lowercasing to more complex ones like tokenization and lemmatization.

Learn how to classify words based on their roles in sentences and how to identify and categorize named entities in your text.

Understand the fundamentals of sentiment analysis, the methodologies behind it, and the power of pre-trained transformer models in discerning sentiments.

Textual data in its raw form isn't suitable for machine learning algorithms. Discover techniques to transform text into numerical vectors, enabling computational processes.

Delve into the art of extracting underlying topics from vast text-based content. Explore various methods like Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA). Learn how to determine the optimal number of topics.

Familiarize yourself with popular algorithms like logistic regression, naive bayes, and support vector machines, and build custom text classifiers tailored to specific needs.

In this section of the course, you will have the opportunity to work on a real-world application of NLP. Through this case study, understand the nuances of fake news, process and analyze the data, and build a classifier to segregate genuine news from the fabricated ones.

Look ahead into the future of NLP. Understand the role of deep learning in advancing NLP, explore the challenges and opportunities in non-English NLP, and ponder on what the future holds for this dynamic field.