Lecture 15 explains Big Data Analytics for Data Mining, including Hadoop, Spark, HDFS, MapReduce, distributed storage, real-time processing, MLlib, large-scale clustering, classification, and industry case studies. As organizations generate enormous volumes of data from sensors, mobile apps, IoT devices, social…
Lecture 14 explains time series data mining, sequential pattern algorithms, forecasting models (ARIMA, LSTM), preprocessing, similarity measures, anomaly detection, and real-world applications in finance, healthcare, IoT, and cybersecurity. Time-series data is everywhere stock markets, weather sensors, medical signals, website traffic,…
Lecture 13 provides a complete guide to Deep Learning for Data Mining, covering neural network foundations, CNNs, RNNs, LSTMs, Autoencoders, feature learning, backpropagation, training techniques, real-world applications, and case studies. Deep Learning represents one of the most powerful and transformative…
Understand the role of physics in electronics, computing, sensors, and modern AI systems with real-world examples and clear concepts. 1. Introduction Applied Physics is not just a theoretical subject; it is the foundation of modern technology. From smartphones and computers…
Lecture 12 explains Data Warehousing & OLAP for Data Mining, covering architecture, ETL processes, star/snowflake schema, multidimensional models, OLAP operations, cubes, case studies, and real-world applications. Data Mining is only effective when the underlying data is integrated, clean, consistent, and…
Lecture 11 explored the ethical, legal, and privacy implications of data mining and modern AI systems. Covering laws (GDPR, CCPA, HIPAA), privacy risks, algorithmic bias, fairness, transparency, security threats, and privacy-preserving solutions, this lecture prepares students to think critically about…