Use cases for ML are seemingly infinite, from automatic responses to queries and automated stock trading, to recommendation engines and customer experience enhancements
This rubric covers the use of statistical tools working on large datasets to create models and derive inferences, as well as coverage of the field in its entirety. This differs from machine learning primarily in that the latter focuses on functional gradient analysis or neural networks (kernels) to derive models.
This 30 minutes video features my interview about the upcoming course “Intuitive Machine Learning”, based on my new book with the same title. Hosted by… Read More »Video: Introduction to Machine Learning
DSC Podcast Series – How to Optimize Your Data Center Network Fabric in the Era of GPU Computing.mp4
The podcast will explore the challenges of building and running next-generation enterprise data centers and storage solutions for increasing infrastructure efficiency and manageability. Discussions will… Read More »DSC Podcast Series – How to Optimize Your Data Center Network Fabric in the Era of GPU Computing.mp4
Data scientists spend 80% of their time on data cleaning and exploratory analysis. What if you could automate most of this? What if data scientists… Read More »How to Automate Data Cleaning, in a Nutshell
DSC Webinar Series – Bringing AI to your marketing stack – fireside chat with Chee Chew and Alex Holub.mp4
AI has changed the landscape of what is possible for marketers, and in this webinar you will learn how to harness that power. From creating… Read More »DSC Webinar Series – Bringing AI to your marketing stack – fireside chat with Chee Chew and Alex Holub.mp4
In the first part here, I discussed missing, outdated and unobserved data, data that is costly to produce, as well as dirty, unbalanced and unstructured… Read More »15 Data Issues and How to Fix Them – Second Part
Technical Debt describes what results when development teams take conscious actions to expedite the delivery of a piece of functionality or a project which later needs to be remediated via refactoring.
According to Forbes, data scientists spend about 80% of their time on data collection, cleansing, and preparation, while only 20% of it is left for… Read More »Data cleansing for reliable analytics and business intelligence
30-50% of businesses experience gaps between their data expectations and reality. They have the data they need, but due to the presence of intolerable defects,… Read More »What is a Data Quality Framework and How to Implement it?