The Conference for Machine Learning Innovation

Essential Workshop to Exploratory Data Analysis and Feature Engineering

Workshop
#Machine Learning Business & Strategy

Speaker

Aleksandr Mester

Aleksandr Mester

More Info

Vladimir Rybakov

Vladimir Rybakov

More Info

Join the ML Revolution!
New date:
November 16 – 18, 2020
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Join the ML Revolution!
New date:
November 16 – 18, 2020
Register Now
Join the ML Revolution!
Register until July 2:
✓ Raspberry Pi or C64 Mini for free
✓ Save up to $310
✓ 10% team discount
Register Now
Join the ML Revolution!
Register until July 2:
✓ Raspberry Pi or C64 Mini for free
✓ Save up to $310
✓ 10% team discount
Register Now
Join the ML Revolution!
Register until November 7th:
✓Save up to € 210
✓10% Team Discount
Register Now
Join the ML Revolution!
Register until November 7th:
✓Save up to € 210
✓10% Team Discount
Register Now

More talks in the program:

Hands-on Workshop: Reinforcement Learning with TF-Agents & TensorFlow 2.0 [SOLD OUT]
Conversational Design Workshop: Build awesome Chatbots and Voice Assistants [SOLD OUT]
  •
    Infos
    Tuesday, September 8 2020
    09:00 - 17:00
    Booking note:
    Essential Data Analysis Workshop
    Infos
    Monday, December 9 2019
    09:00 - 17:00
    Room:
    Salon 8+9
    Booking note:
    Essential Data Analysis Workshop
    Most experienced data scientists would agree that data processing takes most of the 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. 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 using a public Google Play Store dataset as an example.

    First of all, we’ll explore and preprocess the data: clean them, fix the errors, convert to appropriate type, etc. Then we will more thoroughly analyze the data: its correlations, relationships, and distribution of variables. After that, we will get rid of the least useful features and try to engineer new ones. Finally, you will train different machine learning models and see how data processing and feature engineering affect the model accuracy and training time.

     
    Therefore, the workshop will cover:
    1. Primary data analysis and Preprocessing   
    2. Exploratory Data Analysis  
    3. Feature Engineering     
    4. Machine Learning model training and evaluation

    The workshop requires 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 us to get a sense of how much effort is invested to develop a machine learning project. Also, all the solutions will be ​provided during the workshop. Each participant should bring their own laptop and make sure VPN restrictions do not block the connection to Google Colab.​

    This Session belongs to the Diese Session gehört zum Programm vom SingaporeSingapore and  und BerlinBerlin program. Take me to the program of . Hier geht es zum Programm von Munich Munich .

    Take me to the full program of Zum vollständigen Programm von Singapore Singapore .

    Take me to the full program of Zum vollständigen Programm von Berlin Berlin .

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

    Behind the Tracks

    Machine Learning & Principles

     Advanced ML Development

    Business & Strategy

    Tools, APIs & Frameworks