Opioid Use Disorder Prediction Using AI and Existing Risk Models
Comparative study on AI models for predicting opioid use disorder, showcasing superior performance of transformer-based AI over existing clinical tools and other machine learning approaches.
XGBoost
Learn to implement XGBoost for regression and classification, explore its mathematical foundations, and master advanced optimizations in Python.
From Concept to Production: Template for the Entire ML Journey - Part 1
Develop a production-ready ML pipeline from concept to deployment using Python. Gain hands-on experience with data prep, model training, and deployment on Google Cloud Platform.
From Concept to Production: Building a Production-Ready ML Pipeline - Part 2
Develop a production-ready ML pipeline from concept to deployment using Python, Kubeflow, and Google Cloud Platform. Master data prep, model training, and orchestration techniques.
Shopify's Machine Learning Platform Journey: Building Merlin with Open Source Tools
Explore Shopify's journey in building Merlin, an open-source ML platform. Gain insights into the architecture, technologies, and tools used to scale machine learning work effectively.
Low Latency Neural Network Inference for ML Ranking Applications - Yelp Case Study
Explore Yelp's ML platform overhaul for low-latency neural network inference in ranking applications. Learn about Tensorflow model training, deployment using MLEAP, and integration with Nrtsearch for improved performance.
Shopify's Machine Learning Platform Journey: Building Merlin with Open Source Tools
Explore Shopify's journey in building Merlin, an open-source ML platform. Gain insights into the architecture, technologies, and tools used to scale machine learning work effectively.
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.
Amazon SageMaker
Learn to build, train, and deploy ML models using AWS SageMaker, covering notebook instances, S3 buckets, XGBoost algorithm, and endpoint predictions.
MLOps: Databricks MLFlow y Optuna para Ajuste de Hiperparámetros - Español
Optimiza modelos de machine learning con Databricks, MLflow y Optuna. Aprende técnicas de ajuste de hiperparámetros usando XGBoost en un entorno práctico.
MLOps MLFlow: Databricks y MLFlow Hyper-Parameter Tuning en Español
Optimiza modelos de machine learning con Databricks y MLflow, utilizando Hyperopt y XGBoost para ajuste de hiperparámetros.
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.
Scaling XGBoost for Thousands of Features with Databricks
Scaling XGBoost for online advertising: Learn to handle thousands of features, optimize model training, and avoid common pitfalls in both Python and Scala implementations on Spark.
Predict Housing Prices in Austin TX with Tidymodels and XGBoost
Explore feature engineering for boosted tree models using Austin housing data. Learn to incorporate text information as indicator variables and tune models for accurate price predictions.
FOLD-SE: An Efficient Rule-based Machine Learning Algorithm with Scalable Explainability
Explore FOLD-SE, an efficient rule-based ML algorithm for classification tasks, offering scalable explainability and competitive performance with state-of-the-art methods.
Balancing Speed and Accuracy in Model Development
Explore strategies for optimizing machine learning model development, balancing speed and accuracy. Learn to assess business objectives, improve data quality, and select efficient features and models.
MotherNet: A Foundational Hypernetwork for Tabular Classification
Discover how MotherNet hypernetwork architecture revolutionizes tabular classification by generating optimized neural networks through in-context learning, offering faster training and competitive performance against XGBoost.
XGBoost in Python from Start to Finish
Comprehensive guide to implementing XGBoost in Python, covering data preparation, model building, and optimization techniques for effective machine learning applications.
XGBoost Part 4 - Crazy Cool Optimizations
Explore advanced XGBoost optimizations for large datasets, including approximate algorithms, parallel learning, and sparsity-aware techniques to enhance machine learning efficiency.
XGBoost Part 2 - Classification
Learn how XGBoost trees are built for classification, covering similarity scores, gain, cover, pruning, and logistic regression in this advanced machine learning tutorial.
XGBoost Part 1 - Regression
Explore XGBoost's unique regression trees, focusing on similarity scores, gain calculation, pruning, regularization, and making predictions in this comprehensive tutorial.