Workshop "Practice Machine Learning and Artificial Intelligence on the open source professional platform Kaggle - Part 2: Practicing advanced machine learning models"

After 3 practical training workshops part 1 went smoothly, the Institute for Computational Science and Artificial Intelligence continued to organize the workshop "Practice Machine Learning and Artificial Intelligence on the open source professional platform Kaggle - Part 2: Practicing advanced machine learning models", creating conditions for all lecturers and students at Van Lang University to have the opportunity to learn and experience reality.
 

Under the guidance of Dr. Nguyen Sy Tuan - Head of the Laboratory for Computational Mechanics, Institute for Computational Science and Artificial Intelligence, learners review the important knowledge of part 1 and learn about the nature and operation of the tree model, Practice mining tabular data, use the Pandas library, practice simple tree models, practice optimizing Machine Learning models to increase the accuracy and prediction efficiency of the model, practice models Random Forest tree,...
 

From October 27 - 28, 2023, lecturers and students will be guided in turn through many important practical content:

• Handling missing data and clustered data;
• Develop standard processes to train, test and deploy complex machine learning models;
• Practice model cross-validation and optimization;
• Practice XGBoost model, advanced tree model;
• Share information to prevent problems related to data leakage in training models.

The workshop series "Practice Machine Learning and Artificial Intelligence on the open source professional platform Kaggle" is conducted to develop a dynamic learning and research environment on machine learning and artificial intelligence at Van Lang University.  By learning and applying Machine Learning into practical lessons, lecturers and students will quickly grasp core knowledge, practice skills and create conditions for learners to develop research, teaching and project areas. predict the quality of training or solve future scientific data problems.