Boosting
Explore ensemble learning techniques like boosting to enhance machine learning performance. Learn about bias, variance, and popular algorithms for improved model accuracy and decision-making.
Machine Learning for Malware Detection
Learn to classify malware samples using PE headers, explore datasets, and implement a gradient boosting classifier for effective malware detection.
Improved Feature Importance Computation for Tree Models Based on the Banzhaf Value
Explore advanced feature importance computation for tree models using the Banzhaf value, offering faster algorithms and improved numerical robustness compared to Shapley value-based methods.
Evaluating Recommendation Algorithms - Offline and Online Approaches
Discover how Delivery Hero optimizes recommendation algorithms through offline evaluations and A/B testing, enhancing user experience while maintaining business metrics.
Boosting and Ensembles in Machine Learning - Lecture 19
Dive into advanced boosting and ensemble techniques in machine learning, exploring methods to combine multiple models for improved predictive performance and robustness.
XGBoost and Data Leakage in Machine Learning - Day 14 of 30 Days of ML
Explore XGBoost and data leakage in machine learning. Learn to build optimized models, prevent leakage issues, and enhance your ML skills with practical tutorials and exercises.
Machine Learning Techniques and Applications in Finance, Healthcare and Recommendation Systems
Explore state-of-the-art machine learning techniques and their applications in finance, healthcare, and recommendation systems, focusing on ensemble methods, collaborative filtering, and portfolio construction.
Recent Advances in Machine Learning with Applications to Internet of Things - Adam McElhinney
Explore machine learning advancements in IoT, covering failure prediction, data challenges, and real-world applications. Learn about deep learning, transfer learning, and future opportunities in this rapidly evolving field.
Introduction to XGBoost - Machine Learning Tutorial
Comprehensive tutorial on XGBoost: from basic principles to hands-on implementation. Learn gradient boosting, parameter tuning, and model evaluation for real-world machine learning applications.
Data-Driven Ecosystems in the Automotive Industry
Explore BMW's big data journey, technology stack, and real-world machine learning applications in the automotive industry, addressing challenges and innovative solutions.
CatBoost - The New Generation of Gradient Boosting
Explore CatBoost, a powerful gradient boosting library for machine learning. Learn its advantages, applications, and unique features for handling categorical data and achieving state-of-the-art results.
Walking the Random Forest and Boosting the Trees
Explore tree-based ensemble models like Random Forest and Gradient Boosting, understanding their theory and implementation in Python for effective data science applications.
10 Decision Trees are Better Than 1 - Random Forest and AdaBoost
Explore tree ensembles: Random Forest and AdaBoost. Learn bagging, boosting techniques, and their benefits. Apply knowledge to breast cancer prediction using example code.
SQL for Efficient Data Organization in Machine Learning
Explore JoinBoost, a Python library transforming tree training algorithms into SQL queries for efficient ML on normalized databases, offering improved performance and scalability.