Courses from 1000+ universities
$7.2 billion in combined revenue since 2020. $8 billion in lost market value. This merger marks the end of an era in online education.
600 Free Google Certifications
Marketing
Cybersecurity
Machine Learning
Circuits and Electronics 1: Basic Circuit Analysis
Academic Writing Made Easy
Nutrition, Exercise and Sports
Organize and share your learning with Class Central Lists.
View our Lists Showcase
Explore the foundations of chemometrics with Bruce Kowalski's 1991 lecture, covering key concepts like CLS, PLS, regression, and neural networks that remain relevant in modern data science.
Discover sparse modeling techniques for linear regression, PLS, and PCA with the LASSO method in this concise introduction.
Explore Anova Simultaneous Components Analysis (ASCA), a powerful tool for analyzing multivariate data from designed experiments, in this comprehensive one-hour overview.
Explore simple linear regression using R, learning its powerful applications and detailed implementation in data analysis.
Explore the revamped PARADISe 5.7 beta for enhanced untargeted GCMS data analysis, offering improved information extraction and advanced features for comprehensive analytical insights.
Explore advanced techniques for variable selection in PLS_Toolbox, enhancing your data analysis skills and improving model performance.
Learn techniques for removing baselines and artifacts from data, enhancing signal quality and improving analytical results in chemometrics and machine learning.
Learn techniques for centering and scaling data to enhance information in chemometric machine learning contexts like PCA and PLS models.
Explore the fundamentals and practical applications of t-tests in statistical analysis, enhancing your ability to interpret and conduct hypothesis testing effectively.
Explore key methods for variable selection in multivariate data analysis, including a-priori, a-posteriori, and model-based approaches. Learn their importance and applications.
Advanced methodologies for variable selection in chemometrics, including genetic algorithms, jack-knifing, and iPLS, building on fundamental concepts.
Learn statistical inference techniques for omics data analysis, focusing on variable selection methods to enhance spectral matching and predict molecular structures from LC-MS/MS spectra using chemometrics and deep learning approaches.
Explore joint tensor alignment and coupled factorization for analyzing coupled datasets with unknown entity correspondence, outperforming multi-stage approaches in data mining applications.
Explore advanced anomaly detection techniques in hyperspectral imaging, focusing on GLS, ELS, and WPCA methods for improved sensitivity and selectivity in detecting minor signals within complex mixtures.
Explore new developments in chemometric preprocessing, including multi-block strategies and Variable Sorting for Normalization, to reduce unwanted variability in experimental data.
Get personalized course recommendations, track subjects and courses with reminders, and more.