Machine Learning Data Pre-processing and Data Wrangling Using Python
Master Python data preprocessing techniques for machine learning, including handling missing values, encoding, scaling, outlier detection, and data transformation using popular libraries.
Production Machine Learning Monitoring: Outliers, Drift, Explainers and Statistical Performance
Dive deep into advanced ML model monitoring techniques, including outlier detection, concept drift, explainability, and performance metrics for robust production deployments.
Deploying and Managing Machine Learning Models at Scale - A Hands-On Workshop
Deploy and manage ML models at scale. Hands-on workshop covers creation, deployment, monitoring, and management using real-life scenarios. Gain practical experience with Seldon Deploy, explainers, and outlier detection.
Statistics in Machine Learning
Comprehensive guide to statistics for machine learning, covering fundamental concepts, distributions, hypothesis testing, and practical applications using Python.
Deep Generative Modeling
Explore deep generative modeling, including autoencoders, VAEs, and GANs. Learn about latent variables, representation learning, and applications in image generation and domain transformation.
Covariance and Subspace Inference: Handling Robustness, Variability and Heterogeneity
Explore covariance matrix inference and PCA for non-regular data, covering mixed effects models, Gaussian assumptions, outlier handling, and applications in radio-interferometry calibration and imaging.
Weighted Gene Co-expression Network Analysis - Step-by-Step Tutorial - Part 1
Learn step-by-step Weighted Gene Co-expression Network Analysis (WGCNA) for RNA-Seq data, covering data manipulation, outlier detection, normalization, module identification, and visualization techniques.
A Spurious Outlier Detection System for High Frequency Time Series Data
Discover an integrated approach for detecting spurious outliers in high-frequency time series data from IoT sensors, applicable across various domains like manufacturing, retail, and healthcare.
Prepare Your Data for Machine Learning Like a Rockstar Data Engineer With DataRobot AI Platform
Learn to prepare data for machine learning using DataRobot AI Platform. Covers AI catalog creation, EDA, feature engineering, outlier detection, data joining, and more. Ideal for data engineers and analysts.
Exploratory Data Analysis Tutorial - Basics of EDA with Python
Learn Python-based exploratory data analysis techniques to uncover insights, detect patterns, and visualize relationships in datasets for informed decision-making and hypothesis testing.
Simultaneous Feature Selection and Outlier Detection Using Mixed-Integer Programming
Explore mixed-integer programming for simultaneous feature selection and outlier detection in high-dimensional regressions, with applications to biomedical research and microbiome studies.
Modal Regression: A New Approach to Modeling Conditional Relationships - Lecture
Explore modal regression, a new tool revealing unique data structures and offering advantages over mean and quantile regressions for outliers, heavy-tailed, and truncated data.
Diagnostics to Improve Linear Model Fit - OLS Analysis and Residual Plots
Explore techniques to enhance linear model fit through residual analysis, normality checks, and outlier detection in OLS regression.
Modeling GDPR Violations in R With Tidymodels
Explore data preprocessing and modeling for GDPR violations using tidymodels in R. Learn feature engineering, workflow creation, and prediction techniques with real-world data.
Cloud Native Machine Learning - Automated Anomaly Detection
Explore automated anomaly detection techniques for machine learning, enhancing your ability to identify and respond to data irregularities in cloud-native environments.
Author Interview - VOS- Learning What You Don't Know by Virtual Outlier Synthesis
Interview exploring Virtual Outlier Synthesis for out-of-distribution detection in neural networks. Discusses motivations, methodology, and key findings of novel approach to improve model robustness using synthesized outlier data.
Learning What You Don't Know by Virtual Outlier Synthesis - Paper Explained
Explore Virtual Outlier Synthesis, a novel method for improving out-of-distribution detection in deep learning models by generating synthetic outliers during training to enhance decision boundary regularization.
Unsupervised Learning for Stellar Spectra with Deep Normalizing Flows - Jo Ciuca
Explore unsupervised machine learning techniques for analyzing stellar spectra using deep normalizing flows, with applications in galaxy evolution and big data astronomy.
Recent Progress in Predictive Inference - Emmanuel Candes, Stanford University
Emmanuel Candes discusses novel calibration techniques for machine learning predictions, addressing issues like false discovery rate control and adaptive conformal inference to handle distribution shifts.
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.
Dual Principal Component Pursuit
Explore a novel approach to learning subspaces from corrupted data, tolerating high outlier ratios and dimensions. Discover algorithms and theoretical guarantees for this non-convex optimization problem.