ML on Spark workshop


Workshop date & duration:  November 5th – Tuesday, 14:00 – 18:00
Trainer: Sorin Peste
Supporting students: Alexandru Petrescu, Laurentiu Partas
Location: TechHub Bucuresti
Price: Free (upon approval by the organizer & trainer)
Number of places: 20 no more places left
Languages: Python

We are coming back in November with a new workshop on Machine Learning – this time with how to build a model using Spark ML logistic regression and gradient boosting.
So, come join us for an afternoon in which we will explore Apache Spark’s Machine Learning capabilities. We’ll be looking at using Spark to build a Credit Scoring model which estimates the probability of default for current and existing customers.

You can check out the agenda and register here.

ML Intro using Decision Trees and Random Forest

workshop ML intro using decision trees and random forest

Using Python, Jupyter and SKlearn

Course date duration: July 13th, 9:30 – 14:00, 30 min break included
Trainers: Tudor Lapusan
Location: Impact Hub Bucuresti, Timpuri Noi area
Price: 200 RON (including VAT)
Number of places: 20 no more places left
Languages: Python

Getting started with Machine Learning can seem a pretty hefty task to some people: understanding the algorithms, learning a bit of programming, deciding which libraries to use, getting some data to learn on, etc… But in reality if you’re actually setting your expectations right and willing to start small and learn step by step, learning the basics of ML it’s actually quite doable. After learning a bit it’s actually up to you to take your knowledge in the real world and apply and expand what you have learned.

This workshop aims to introduce you into ML world and to teach you how to solve classification and regression problems through the usage of decision trees and random forest algorithms. We will go from the theory to hands on in just a couple of hours aiming mostly to make you understand the main pipeline of an ML project, while of course learning a bit of ML:

    • Software and hardware requirements for a ML project
    • Common Python libraries for data analysis
    • Feature encoding and feature preprocessing
    • Exploratory Data Analysis (EDA)
    • Model validation
    • Model hyperparameter optimization
    • Tree based models for classification and regression:
      • Decision Tree
      • Random Forest
    • Repetitive model improvement

You can check out the agenda and register here.