Come learn about AI and machine learning from industry data scientists and practitioners. To sign up, please contact the Grok team.
Grok offers introductory, hands-on classes to better understand machine learning, data mining and statistical pattern recognition.
In these classes, we will discuss effective machine learning techniques, provide practical examples and most importantly, apply the content in a practice environment. Our classes are free and fully-guided by a live instructor.
Clustering is an unsupervised machine learning technique used to identify similar entries for grouping purposes. It is broadly used in many applications for pattern recognition and information retrieval. Data stream clustering is attracting more attention as the number of relevant applications is rapidly increasing. Examples include, but are not limited to phone records, financial transactions, social streams, and equipment maintenance. In this course, we will present the most common clustering types, then demonstrate how Grok uses hierarchical clustering to group event data in Python.
Clustering is an unsupervised machine learning technique used to identify similar entries for grouping purposes. It is broadly used in many applications such as pattern recognition and information retrieval. Semantic clustering involves extracting numerical data from semantic data, such as that produced by system log files. In this course, we will provide an overview of semantic embedding, then demonstrate how Grok uses classical clustering techniques. The sample dataset will be provided and analyzed using the developed clustering method in the lecture