In this module, you will explore the fundamentals of machine learning (ML) from theory to application. You will also be able to define ML and learn to assess its performance. Additionally, you will gain practical experience constructing and evaluating ML classifiers. You will be able to compare the effectiveness of various ML models like Decision Trees, understanding their role in operationalizing data and the importance of data normalization in achieving optimal results.

In this module, you will explore the foundational aspects of Natural Language Processing (NLP) in the context of social media. You will also learn essential techniques such as text pre-processing using NLTK, understanding Part of Speech (PoS) tagging and parsing challenges, and leveraging advanced models like BERT. Along with this, you will gain insights into the history of NLP and tackle specific challenges associated with parsing social media text, preparing you to analyze and interpret digital content effectively.

In this module, you will delve into the intricacies of sentiment analysis, exploring its various types such as Sentiment 140 and Aspect-Based Sentiment Analysis. You will understand the methodologies and tools used to perform sentiment analysis on social media content. You will also get a chance to address the challenges inherent in sentiment analysis and discuss emerging research trends aimed at enhancing accuracy and applicability in diverse contexts.

In this module, you will dive deep into Topic Modeling, focusing on Latent Dirichlet Allocation (LDA) and its variants. You will learn to apply these techniques to analyze and extract topics from social media content. You will also explore how to construct semantic networks tailored for social media applications, enhancing your ability to uncover hidden thematic structures and insights within textual data.