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Зарежда Събития
Спонсори на събитието:
Спонсори на събитието:

За събитието

*The event will be in English.

This event is part of the DEV.BG’s Machine Learning user group.

 

AGENDA: 

>>> Project Goal
>>> Problem and data understanding
>>> Data preparation and preprocessing
>>> Training and tuning the model
>>> Validation and model competition
>>> How to implement this with AZURE
>>> Key Findings
>>> Next Steps

>>> Q&A част


This is an online event. Register and few hours prior to the event you’ll receive an attendance link.

User group: Machine Learning
The event is part of the Machine Learning User Group. Every month we organize events on Machine Learning related topics. In order not to miss interesting information on the topic – subscribe

Organized by: DEV.BG is a specialized Job Board for IT jobs.

Machine Learning jobs: If you are looking for a job related to the Machine Learning technology, view the job listings here.

March 13th, 7:30 pm (EET)

speaker bio
Roman Rudenskyi Roman Rudenskyi Data Scientist Intellias

За лектора

Practitioner with over 8 years of experience as a Data Scientist. Key projects/achievements:

Medicines sales forecasting – develop, finetune models for sales prediction, automatizing ETL and forecast delivery processes,

Maps enhancement project – developed quality metrics for features extraction service that compares identified objects of different types against the ground truth data, developed service for calculating, collecting and publishing new quality metrics and generated diverse KPI dashboards for their monitoring.

Venture investment company: data enrichment, scores development, predictive analytics –

developed Likely to raise funds model; predict successful pipeline of venture investment company (LightGbm), Model user engagement (developed Intent to buy score) based on visitor behavior; Signals development for venture company analytics.

AdTech market predictions – Develop ML algorithms for bidding prices prediction for RTB. Data enrichment. Market segmentation based on response/action type prediction. Predicting click-through-rate (CTR), determine the most influential factors on CTR.

Smoke detection on video (Yolo, VGG16, OpenCV), Picture based furniture recommendation algorithm using unsupervised furniture clustering, Warehouse object detection (Yolov6)

Нашите

Партньори

Intellias is an international IT company with 2600 professionals. More than 2 billion people worldwide use technology products developed by Intellias engineers.

FactSet provides superior analytics, content and technology to help more than 88,000 users see and take advantage of opportunities.

Iris.ai is your best research assistant that uses NLP to find, extract and summarize key data from massive scientific databases.

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