Video: Introduction to Machine Learning
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
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Data Science
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.
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
How to Automate Data Cleaning, in a Nutshell
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
How to build your data quality team
As the adage goes, a workman is only as good as his tools. There is no disputing that, but you can never overlook the power… Read More »How to build your data quality team
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
15 Data Issues and How to Fix Them – Second Part
In the first part here, I discussed missing, outdated and unobserved data, data that is costly to produce, as well as dirty, unbalanced and unstructured… Read More »15 Data Issues and How to Fix Them – Second Part
A Model For Technical Debt In Machine Learning Systems
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.
Data cleansing for reliable analytics and business intelligence
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
What is a Data Quality Framework and How to Implement it?
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?
Top 10 Projects for Data Science and Machine Learning
Building machine learning projects can give you a much more comprehensive education about how they work.