Descriptive analytics, machine learning, and deep learning viewed via the lens of CRISP-DM
CRISP-DM methodology is a must teach to explain analytics project steps. This article purpose it to complement it with specific chart flow that explain as simply as possible how it is more likely used in descriptive analytics, classic machine learning or deep learning.
By Stéphane Faure, IBM.
As part of an organization where I often have to explain basics of “classical” machine learning, I usually introduce CRISP-DM methodology (see https://www.kdnuggets.com/2014/10/crisp-dm-top-methodology-analytics-data-mining-data-science-projects.html).
This methodology is probably the most appropriate and the different phases provide a strong framework to practitioners but, in order to support more specific explanation of how it apply to “classic” Machine Learning and Deep Learning I needed to complement CRISP-DM with more specific design flow:
Since the aim of the flows is to explain the difference between the three approaches, some (lot of…) are not incorporated to the flows, but I think it would help those who intend to introduce Machine Learning and Deep Learning to not specialists.
Bio: Stéphane Faure is an IT professional at IBM where he supports server sales across Europe. In the last years, he has been working on payment fraud detections, credit scoring and regularly presents and teaches predictive analytics.
Related:
- What is the most important step in a machine learning project?
- Descriptive Statistics: The Mighty Dwarf of Data Science – Crest Factor
- Find the Right Accelerator for Your Deep Learning Needs
