Machine learning is the science of getting computers to act without being explicitly programmed. In our classes, you will learn about effective machine learning techniques, practical examples and most importantly, you will learn by doing.  Designed for IT Operations and Development engineers looking to expand their knowledge in Data Science. Our classes are free and fully guided with a live instructor.

MACHINE LEARNING VIRTUAL COURSES

Introductory hands on classes to machine learning, data mining and statistical pattern recognition.

About Grok

Grok is the Leading AIOps Software Platform for growing and large corporations with complex IT infrastructures. Our mission is to be at the forefront of Machine Learning innovation and apply that knowledge and expertise to solve IT Operations at scale. Grok was formed as a partnership between the renowned AI research organization Numenta and the founders of Avik Partners who have successfully started and grown multiple technology companies in Service Assurance and automation before establishing Grok. Our decades of experience and groundbreaking research on developing technology based on the human brain’s neocortex has created a one of a kind AIOps platform for IT organizations.

About the Instructor

Dr. Mina Baghgar Bostan Abad is a data scientist at Grok. She obtained her PhD in condensed matter physics from University of Massachusetts at Amherst. She worked as a research scientist at Harvard and then UC Irvine before joining Grok in Oct 2019.  She has continuously taught several courses at different capacities in aforementioned institutions. Her current research and development interests at Grok are centered around designing Machine learning models for real-time monitoring, clustering, and prediction of streaming data.
info@grokstream.com 
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Intro to Hierarchical Clustering for Event data

Clustering is an unsupervised machine learning technique for identifying similar entities and grouping them together. It is broadly used in many fields such as pattern recognition and information retrieval. In particular, data stream clustering is attracting more attention as the applications relevant to streaming data are rapidly increasing, including phone records, financial transactions, social stream, etc. Real-time stream clustering allows analyzing continuous data streams to extract relevant information and predict patterns from data. In this lecture, we first give a quick rundown of some of the most common types of clustering, then demonstrate how we use hierarchical clustering to group event data in python.
A sample dataset will be provided and analyzed using the developed clustering method in the lecture. Participants are encouraged to install the following python libraries prior to the lecture so they can simultaneously write the clustering algorithm and run it in their local machine.
  • Pandas
  • NumPy
  • Matplotlib
  • SciPy
Some programming experience
Familiar with basic concepts of machine learning

Skills Required:

Semantic Clustering Log data in Python

Clustering is an unsupervised machine learning technique for identifying similar entities and grouping them together. It is broadly used in many fields such as pattern recognition and information retrieval. Semantic clustering involves extracting numerical data from semantic data such as the information produced by systems log files.  In this lecture, we first give a quick rundown of semantic embedding, then demonstrate how we use classical clustering techniques to group semantic data from a sample dataset composed of log files. The sample dataset will be provided and analyzed using the developed clustering method in the lecture

Skills Required:

Training a Classification Model

Classification is a supervised machine learning technique that involves training a predictive model on examples with labels.  In this lecture, we explore how anomaly data (from operational metrics) can be labeled such that a classifier recognizes evolving incidents as they unfold.   The sample dataset will be provided and analyzed using the developed classification method in the lecture.
Some programming experience
Familiar with basic concepts of machine learning

Skills Required:

Some programming experience
Familiar with basic concepts of machine learning

June 26th or July 10th 

July 31st or August 14th 

August 28th or September 18th 

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