Data Preparation for ML/DL Model
Data Preparation for ML/DL Model
Key topics included data cleaning techniques for addressing missing values, duplicates, and outliers, ensuring data integrity for reliable model predictions. The session also covered data transformation methods such as normalization, standardization, and encoding categorical variables, along with the significance of feature engineering to enhance model performance.
By the end of the session, attendees gained a clear understanding of the critical role of data preparation and practical strategies to improve their ML/DL projects.
Date and Time
Location
Hosts
Registration
- Date: 01 Oct 2024
- Time: 02:00 PM to 04:00 PM
- All times are (UTC+05:30) Chennai
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- Sage University Indore, Rau Road, Dewas-Indore Bypass Road
- INDORE, Madhya Pradesh
- India 452001
- Building: F-Block
- Room Number: S-13
Speakers
Dr. Shilpa Bhalerao
Data Preparation for ML/DL Model
The expert session on "Data Preparation for ML/DL Models" aimed to provide participants with essential skills for effective data handling in machine learning and deep learning projects. It began by highlighting the importance of high-quality data and the challenges posed by different data types—structured, unstructured, and semi-structured.
Key topics included data cleaning techniques for addressing missing values, duplicates, and outliers, ensuring data integrity for reliable model predictions. The session also covered data transformation methods such as normalization, standardization, and encoding categorical variables, along with the significance of feature engineering to enhance model performance.
By the end of the session, attendees gained a clear understanding of the critical role of data preparation and practical strategies to improve their ML/DL projects.
Address:Sage University Indore, Rau Road, Dewas-Indore Bypass Road, , INDORE, India, 452001
Agenda
The primary objective of this expert session was to provide attendees with a comprehensive understanding of the critical role data preparation plays in the development of Machine Learning (ML) and Deep Learning (DL) models. Participants aimed to learn best practices, methodologies, and tools that enhance data quality and, consequently, model performance.