The Conference for Machine Learning Innovation

Essential Workshop to Exploratory Data Analysis and Feature Engineering

Workshop
Join the ML Revolution!
Register until October 20:
✓ Save up to $233
✓ Team discount
✓ Extra Specials for Freelancers
Register Now
Join the ML Revolution!
Register until October 20:
✓ Save up to $233
✓ Team discount
✓ Extra Specials for Freelancers
Register Now
Join the ML Revolution!
Register until November 03:
✓ Save up to €494
✓ 10% Team Discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Register until November 03:
✓ Save up to €494
✓ 10% Team Discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Infos
Booking note:
Data Analysis Workshop

Most experienced data scientists would agree that data processing takes the most time when undertaking machine learning projects. Both data pre-processing and feature engineering quality is crucial for model performance. However, it is not typically an easy thing to do. When dealing with real data, you are likely to encounter such problems as noise, missing values, excessive information, etc. Building a good feature vector turns out to be just as hard. In this workshop, you will learn some simple but effective ways of handling these problems. First of all, we’ll explore and preprocess the data: clean them, fix the errors, convert to the appropriate type, etc. Then we will analyze data relations. After that, we will use several ways to engineer new features. Finally, we will show how feature engineering affects model efficiency. Therefore, the workshop will cover: Primary data analysis and Preprocessing Exploratory Data AnalysisFeature EngineeringData Analysis and Feature engineering tools (new).

Even though the field is not as new to the general IT community as it was 10 years ago, it is still really young. This leads to a lot of hype and as a concequence,
most of the attention in papers and researches payed towards machine learning models, frameworks and the results of some applied cases (the "more exiting" part of DS). At the same time, the process of
gathering, analyzing and preprocessing the data is regulary overlooked (despite one of the main rulels of thumb in ML – trash in -> trash out).

In the workshop you’ll see:
– Preliminary Data Analysis and Processing
– Exploratory Data Analysis
– Feature Engineering
– Machine Learning model training and evaluation
This division might not fit the exact 90 minute blocks of the workshop.

Keywords: data preprocessing, data analysis, feature engineering, machine learning basics

For this workshop we require participants to have a basic knowledge of Python and Machine Learning. If you do not have any coding experience you are still welcome to join the workshop as all the solutions will be shared at the end. Every participant should bring their own laptop and consider if VPN restrictions might block the connection to Google Colab.

This Session Diese Session Take me to the current program of . Hier geht es zum aktuellen Programm von Singapore Singapore , Berlin Berlin or oder Munich Munich .

Behind the Tracks