π» Almost finished a series of online hands-on labs about Data Ingestion and Visualisation with Elasticseach and Kibana. You will be able to execute the examples on your own machine.
(It will also be free of course)
π¨βπ« De tijd vliegt. Vandaag alweer Dag 4 van de (avond) training Programming Essentials in Python die ik verzorg vanuit Cisco Networking Academy
O.a. de "Data Trust" en "Digitcal Contact Tracing" zijn interessante technieken uit het Top 10 lijstje van MIT met "Breaktrough Technologies" in 2021.
https://t.co/jaS58B4j6j
π» Naast Python en Machine Learning, geef ik ook praktische trainingen voor Elasticsearch, waar ik natuurlijk ook een enorme fan van ben! Dag 1 van de training Elasticsearch Fundamentals zit er weer op.
Cursus Data Analytics dag 4:
- Grote datasets bewerken met Apache Spark en R
- H2O Automated Machine Learning
- Machine Learning models: Gradient Boosting
π» You can play with our demo again at:
https://t.co/wT7p96BdCF
(it might have some little issues since this is a simple lightweight demo, you can always comment if you find one)
π» For everyone working with (Python) Pandas and Group By's --> Have a method in which you can specify for which variables and values you want to calculate the proportion's (or percentages) for each different combination
Code:
https://t.co/FX54WLYtPq
I stumbled upon a post about using Deep Learning for applying 3D effects to existing pictures. I had to experiment it myself. Some look more like a scrapbook than 3D, but some are really good π±(β¬οΈ References and repository)
π» Github repo:
https://t.co/EMumgpkabb
π» For anyone who loves the topic of Explanatory Machine Learning (like me): Here is a visualisation tool to play with: Facebook's HiPlot
https://t.co/RHllGeNzAE
Call me old fashioned... For as long as I know I use physical notebooks during my Machine Learning or Software Engineering projects. I think drawing, making mind maps or other types of scribbling are great ways to organise the ideas in your mind to get the job done. π