Week 5 Learning Session: Feature Engineering & Pipelining

Master the art of Feature Engineering & Pipelining in our upcoming workshop led by Muhammad Khalif Umana, a Graduate AI Research Assistant at UUM. Learn essential techniques like handling missing values, scaling, normalization, removing outliers, and building efficient pipelines.

Jan 19, 8:00 – 10:00 AM



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About this event

Join us for an immersive learning experience at our workshop on Feature Engineering & Pipelining. Led by Muhammad Khalif Umana, a highly skilled Graduate AI Research Assistant at UUM, this event is designed to equip you with the knowledge and techniques necessary to excel in feature engineering and build efficient data pipelines.

In this comprehensive workshop, we'll explore essential techniques for handling data in the preprocessing phase. We'll begin by diving into finding missing values and understanding effective strategies to deal with them. You'll learn how to identify missing data patterns, impute missing values using various methods, and ensure the quality and integrity of your datasets.

Next, we'll delve into scaling and transformation, an important step in preparing features for machine learning algorithms. You'll discover techniques such as standardization, min-max scaling, and logarithmic transformations that help normalize data distributions and improve model performance.

Normalization, another crucial aspect of feature engineering, will be covered extensively. You'll learn how to normalize variables to a common scale, ensuring fair comparisons and preventing any single feature from dominating the learning algorithm. Muhammad Khalif Umana will guide you through normalization techniques such as Z-score normalization, decimal scaling, and min-max normalization.

We'll then explore the process of removing outliers, an essential step to enhance the robustness of your models. You'll gain insights into identifying and handling outliers effectively, ensuring accurate and reliable results.

In addition to feature engineering techniques, we'll dive into the concept of pipelining, a powerful approach to streamlining the data preprocessing workflow. You'll learn how to build efficient data pipelines that automate and orchestrate the sequential execution of various preprocessing steps. Muhammad Khalif Umana will walk you through the implementation of pipelines using popular libraries such as sci-kit-learn, enabling you to efficiently preprocess data and seamlessly integrate it into your machine-learning models.

Whether you're a data scientist, machine learning engineer, or aspiring AI enthusiast, this workshop is tailored to enhance your skills and deepen your understanding of feature engineering and pipelining. Join us and connect with fellow participants who share your passion for data-driven insights. Reserve your spot now and embark on a transformative journey to become a proficient practitioner in feature engineering and data pipeline development.


  • Muhammad Khalifa Umana

    University Utara Malaysia

    Graduate AI Research Assistant


  • Josiah Farrel Suwito

    GDSC Lead

  • Vinca C A

    Vice Lead

  • Raisa Aquila Zahra Kholiq


  • Achmad Hadzami Setiawan



  • Jonathan Edmund Kusnadi


  • albert cristianto halim


  • Maulana Arya Alambana

    Universitas Gadjah Mada


  • Firja Athanadhif


  • Ananda Shabrina Putri Gunawan

    Universitas Gadjah Mada


  • Wenka Wendira


    Public Relations

  • Christopher Arya

    Public Relations

  • Radhyanas Oetomo


  • Azzahra Darsono


  • Nadia Hasna


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