Seaborn Python Tutorial
Learn data visualization with Seaborn: create scatter plots, line charts, and distribution plots. Master regression models, FacetGrid, and color palettes for effective data analysis and presentation.
General Linear Models - Background Material
Comprehensive exploration of statistical concepts and matrix algebra, covering distributions, quadratic forms, projection matrices, and advanced matrix operations for linear modeling.
Applied Statistics
Comprehensive introduction to statistics covering descriptive and inferential methods, probability, distributions, hypothesis testing, and regression analysis with practical applications and examples.
Introduction to Regression Models by Using R and Tidymodels
Explore regression models using R and tidymodels to predict numeric values. Learn when to use regression, train models, and evaluate performance through hands-on bike rental prediction.
Weka Data Mining Tutorials
Learn data mining with Weka's user-friendly interface. Master regression, classification, and drug discovery models through hands-on tutorials and practical examples.
Tensorflow Tutorial for Beginner in Python - Hindi
हिंदी में TensorFlow की शुरुआत से लेकर उन्नत अवधारणाओं तक का व्यापक प्रशिक्षण, जिसमें न्यूरल नेटवर्क, रिग्रेशन, वर्गीकरण और CNN शामिल हैं।
Using Apache Spark for Predicting Degrading and Failing Parts in Aviation
Leveraging Apache Spark for predictive maintenance in aviation, enabling efficient data processing, anomaly detection, and component health scoring to improve fleet readiness and prevent failures.
Skew-Symmetric Approximations of Posterior Distributions - Colloque des Sciences Mathématiques du Québec
Explore unified skew-normal distributions as conjugate priors for discretized Gaussian linear regressions, enhancing Bayesian inference and computation in regression models across various fields.
臨床研究者のための生物統計学 - 傾向スコアによる交絡調整と平均曝露効果の推定 - 第4章
傾向スコアを用いた交絡調整と平均曝露効果の推定について、臨床研究における生物統計学の実践的な手法を学びます。
Probabilistic Deep Learning in TensorFlow - The Why and How - ODSC Europe 2019
Explore probabilistic deep learning in TensorFlow, focusing on Bayesian techniques, uncertainty modeling, and practical applications using TensorFlow Probability for more robust and interpretable neural networks.
Building the Gradient Descent Algorithm - Coding Challenge
Hands-on tutorial for building a gradient descent algorithm from scratch in Python, implementing a regression machine learning model using only NumPy.
LEMNA - Explaining Deep Learning Based Security Applications
Explore LEMNA, a novel technique for explaining deep learning models in security applications, addressing transparency concerns and improving interpretability for critical decisions.
The Automatic Statistician - Professor Zoubin Ghahramani
Explore Bayesian model selection strategies for automated data analysis and report generation, with insights on regression models, Gaussian processes, and computational resource allocation.
Randomized Least Squares Regression - Combining Model- and Algorithm-Induced Uncertainties
Explore randomized least squares regression, combining model and algorithm uncertainties for improved statistical analysis and numerical linear algebra applications.
Machine Learning-based Design of Proteins and Small Molecules
Explore machine learning approaches for protein engineering, focusing on data-driven design to create proteins with desired properties and the challenges of extrapolation in predictive modeling.
Time Series Forecasting with Regression Model
Learn to implement regression models for long-term and short-term time series forecasting, focusing on trend and seasonality modeling and auto-regressive techniques using the AirPassengers dataset.