Play all

Intro

RESCUE

Source Ministry

CLIENT PROBLEM

M BACKLINKS CLASSIFY THEM

1ST APPROACH IF-OLOGY UGLY CODE FOR POC

ND APPROACH NAIVE MACHINE LEARNING

DOING WITHOUT KNOWING ISA. RECIPE FOR A FAILURE

RD APPROACH, FINAL DATA ORIENTED MACHINE LEARNING WORKFLOW

CLASSIFICATION REGRESSION CLUSTERING DIMENSIONALITY REDUCTION ASSOCIATION RULES

SUPERVISED LEARNING UNSUPERVISED LEARNING REINFORCEMENT LEARNING

OUR PROBLEM

DEVELOPERS DATASET

REGRESSION PREDICTING VALUES

CLUSTERING K-MEANS

1936, RONALD FISHER IRIS DATASET

RESULTS STABILITY

CLASSIFICATION FAST ARTIFICIAL NEURAL NETWORK

HOW TO CLASSIFY OUR DATASET AUTOMATED WAY TO FIND JUNIOR/SENIOR DEVELOPER?

TECHNOLOGY

FOCUS ON IDEAS NOT TOOLS

ML IS NOT A SINGLE RUN

IT'S A PROCESS

DEFINE A PROBLEM ANALYZE YOUR DATA UNDERSTAND YOUR DATA PREPARE DATA FOR ML SELECT & RUN ALGO(S) TUNE ALGO(S) PARAMETERS SELECT FINAL MODEL VALIDATE FINAL MODEL

Description:

Explore machine learning concepts and applications in a 53-minute conference talk from code::dive 2016. Dive into real-world problem-solving using ML techniques, starting with a client issue from the RESCUE Source Ministry. Progress through various approaches, from basic if-statements to sophisticated machine learning workflows. Examine classification, regression, clustering, and other ML paradigms. Learn about supervised, unsupervised, and reinforcement learning. Analyze a developers dataset using regression, K-means clustering, and neural networks. Discover how to automate the classification of junior and senior developers. Emphasize the importance of focusing on ideas rather than specific tools, and understand machine learning as a comprehensive process involving problem definition, data analysis, algorithm selection, and model validation.

Machine Learning for a Rescue

code::dive conference

Add to list

#Conference Talks #Code::Dive #Data Science #Data Analysis #Computer Science #Machine Learning #Supervised Learning #Classification #Dimensionality Reduction #Clustering