Professor: Dr. Robert J. Brunner
This class is an asynchronous, online course. This course will introduce and explore advanced data science concepts through practical demonstration of algorithms and technologies on cloud computing systems.
We learn about the basic tasks in machine learning, including the importance of data preparation. Next, linear regression is introduced along with concepts like regularization and an extension to logistic regression. Supervised learning is introduced with examples for both classification and regression presented including naive Bayes, k-nn, SVM, decision trees, and ensemble techniques. Unsupervised techniques are presented with applications in both clustering and dimensional reduction. Specific application areas are explored for these machine learning techniques, including text analysis, network analysis, and social media analysis. The last part of the course focuses on cloud computing technologies, including Hadoop, MapReduce, NoSQL data stores, Spark, and streaming data analysis. The course concludes with a brief introduction of deep learning.
Copyright(c) belongs to Dr.Robert J. Brunner