Machine Learning Techniques for Social Media Data Analytics

The course “Machine Learning Techniques for Social Media Data Analytics” introduces learners to the applications of machine learning in understanding, analyzing, and interpreting social media data. With the exponential growth of platforms like Twitter, Facebook, Instagram, and LinkedIn, social media has become a powerful source of real-time information reflecting opinions, trends, and user behavior. This course explores the fundamentals of machine learning models, natural language processing (NLP), and sentiment analysis to extract meaningful insights from unstructured data such as text, images, and multimedia. Learners will also gain exposure to classification, clustering, recommendation systems, and predictive analytics techniques tailored to social media datasets.The course emphasizes both theoretical foundations and practical implementation, enabling participants to work with real-world social media data using Python and popular ML libraries. Case studies on sentiment mining, influencer detection, fake news identification, and trend prediction will highlight the societal and business value of social media analytics. By the end of this course, learners will be equipped with essential skills to design machine learning workflows for social media applications, preparing them for careers in data science, digital marketing, policy analysis, and research domains where actionable insights from online platforms are increasingly vital.