Introduction

This is our final week of lectures. In this weeks class I will try to wrap up the whole data mining process and what it involves in a practical sense. The CRISP-DM lifecyle is good but not complete as it doesn’t discuss what needs to happen with each iteration. I’ll go over this and talk about some of the things you need to watch out for and things you need to do. You can then apply these guidelines in your work projects. I will also look at some advances in Data Mining that cover Adaptive Intelligence Applications and Automated Machine Learning, from a data mining perspective and not from a machine learning perspective. Finally, I’ll look at some future areas for data mining, data science, etc that are coming in the very near future.

Notes

Click here to download the notes for this week.

Read this Twitter discussion on 30 best practices for Machine Learning engineering

Top 5 Mistakes of Greenhorn Data Scientists

Lab Exercises

There are no lab exercises for this week,

Use the time to work your assignment for the module.

Videos

Additional Reading

Machine Learning- IS the emperor wearing clothes?

Will Data Science Continue to be the Sexist job?

Data science is different now

What Data Scientists Really Do, According to 35 Data Scientists

Why Data Science Teams Need Generalists, Not Specialists

Don’t believe the hype: The media are unwittingly selling us an AI Fantasy

Make AI Boring: The road from Experimental to Practical

Graph Technologies – Papers on Deep Learning using Graphs