Scikit-Learn Pipeline and Cross-Validation - Day 13 of 30 Days of ML
Learn advanced machine learning techniques with Scikit-Learn, focusing on pipelines for organized code and cross-validation for improved model performance evaluation. Part of a 30-day ML challenge.
PLS Number of Components
Learn validation techniques for PLS models, including outlier detection, error measures, cross-validation, and practical tips for selecting the optimal number of components.
Statistical Rethinking 2023 - Fitting Over & Under
Explore advanced statistical concepts including cross-validation, regularization, and robust regression to enhance model fitting and selection techniques in data analysis.
Statistical Rethinking 2022 - Overfitting
Explore overfitting in statistical modeling, covering cross-validation, regularization, information criteria, and robust regression techniques for improved predictive accuracy.
Complete Guide to Cross Validation
Comprehensive guide to cross-validation in machine learning, covering setup, datasets, common pitfalls, train-test splits, and practical applications using scikit-learn in Python.
Deep Learning Cross Validation for TensorFlow and Keras
Learn to implement cross-validation techniques for deep learning models using TensorFlow and Keras, enhancing model evaluation and performance in regression and classification tasks.
Introduction to Machine Learning - Lecture 2
Explore key machine learning concepts and their applications in healthcare, bridging the gap between computational modeling and clinical problem-solving.
Selecting the Right Cross-Validation Method in Machine Learning
Learn to choose the right cross-validation method for your machine learning model based on data characteristics, including hold-out, k-fold, time-split, and group-fold techniques.
Sleep Classification with Python Using EEG, Sklearn and MNE - Part 1
Enhance EEG sleep stage classification using Python, Sklearn, and MNE. Expand dataset, add new class, and implement Leave-One-Subject-Out cross-validation for improved results.
Learning from Mutants - Using Code Mutation to Learn and Monitor Invariants of a Cyber-Physical System
Innovative approach to automatically construct CPS models using supervised machine learning on data from systematically seeded software faults, demonstrating efficacy in detecting various attacks on a water purification plant simulator.
Data-Driven Process Optimisation at Mondi
Explore data-driven process optimization techniques, from data collection to model deployment and management, using real-world examples from Mondi's manufacturing operations.
Machine Learning Under Test
Explore testing strategies for machine learning algorithms, focusing on code quality, domain-specific challenges, and practical implementation techniques for more robust and reliable ML models.
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.
Performance Measurement for Machine Learning
Master essential metrics and techniques for evaluating machine learning models, from accuracy assessment to advanced performance measurements in AI applications.
Cross Validation in Machine Learning - Bias-Variance Trade-off and Validation Techniques
Learn cross-validation techniques for model evaluation, including bias-variance trade-off, validation set approach, LOOCV, and k-fold CV, to improve predictive performance.
Machine Learning - Testing and Error Metrics
Explore machine learning model evaluation, including metrics, error types, cross-validation, and optimization techniques for improved performance and decision-making.
A Guide to Cross-Validation for AI - Avoiding Overfitting and Ensuring Generalizability
Explore cross-validation techniques for AI, including K-fold and nested methods. Learn to avoid overfitting, ensure generalizability, and select optimal approaches for model evaluation.
Machine Learning for Predicting Additive Manufacturing Part Quality - Tutorial on Support Vector Regression
Learn to predict additive manufacturing part quality using support vector regression. Explore data analysis, hyperparameter tuning, and cross-validation techniques for machine learning in 3D printing applications.
Nonlinear Regression and Regularization in Data Mining - Spring 2023
Master nonlinear regression concepts, from polynomial mappings to regularization techniques, while learning practical implementation in Python and understanding cross-validation for model optimization.