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.

Spark Structured Streaming vs Kafka Streams

workshop Spark Structured Streaming vs Kafka Streams

Date: TBD
Trainers: Felix Crisan, Valentina Crisan, Maria Catana
Location: TBD
Number of places: 20
Price: 150 RON (including VAT)

Streams processing can be solved at application level or cluster level (stream processing framework) and two of the existing solutions in these areas are Kafka Streams and Spark Structured Streaming, the former choosing a microservices approach by exposing an API and the later extending the well known Spark processing capabilities to structured streaming processing.

This workshop aims to discuss the major differences between the Kafka and Spark approach when it comes to streams processing: starting from the architecture, the functionalities, the limitations in both solutions, the possible use cases for both and some of the implementation details.

You can check out the agenda and register here.