bigdata.ro

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