Finding the Value of Pi from a Random Walk Using Python
Explore Python coding to calculate pi using random walks, from 1D to 3D simulations. Enhance mathematical understanding through practical programming.
Hands-on Python: Fitting Regression Models with Sklearn and Statsmodels - Part 3
Fit regression models using sklearn and statsmodels in Python. Learn model setup, evaluation, and implementation techniques.
Granger Causal Model: Introduction and Application in Regression Analysis
Explore the Granger Causal model, an application of regression that attempts to address causality in data analysis beyond mere correlation.
Handling Multicollinearity with Ridge Regression in Python
Implement Ridge Regression using Python and sklearn to handle multicollinearity in data analysis.
Introduction to Logistic Regression
Learn about binary classification, logistic regression, and related concepts like probit and logit links in this concise introduction to fundamental statistical modeling techniques.
Implementing Linear Discriminant Analysis (LDA) with Irish Data Sets in R
Implement LDA using Irish datasets in R, gaining practical experience with Linear Discriminant Analysis techniques.
Gaussian Process Regression
Explore non-linear regression techniques, focusing on Gaussian Process Regression, basis functions, and systems. Gain insights into GP priors for advanced statistical modeling.
Hands-on R: Implementing Tree Regression and Random Forest with Simulated Data
Implement tree regression and random forest techniques using simulated data in R, gaining practical experience with these powerful machine learning algorithms.
Offline Bayesian Estimation - MATLAB Demo
Explore offline Bayesian estimation techniques through hands-on MATLAB demonstrations and practical applications.
Writing the "Results" Section
Learn to effectively structure and present research findings in the "Results" section of scientific papers, enhancing clarity and impact.
K-Means Implementation in R
Implement K-means clustering algorithm in R programming language. Learn data preprocessing, algorithm application, and result interpretation for effective data analysis.
K-Nearest Neighbors Implementation in R
Implement K-Nearest Neighbors algorithm in R programming language. Learn practical application of this machine learning technique for classification and regression tasks.
Logistic Regression: Introduction to Binary Classification and Linear Classifiers
Master logistic regression for binary classification, covering linear classifiers, sigmoid functions, parameter estimation, and log-likelihood functions.
Logistic Regression Implementation in R for Automotive Crash Testing
Implement logistic regression in R for automotive crash testing, from data preparation to model evaluation and prediction.
Predictive Modelling
Explore predictive modeling techniques and applications for data-driven decision-making and forecasting in various domains.
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.
Hands-on with Python: Estimating Regression Coefficients with OLS and Abline - Part 2
Estimate regression coefficients using OLS and abline in Python, gaining practical experience with statistical analysis techniques.
Geometry of Regression Model and Feature Engineering
Explore regression model geometry and feature engineering techniques to enhance data analysis and model performance.
Nepal in the Loop - Human-Aided Decision Making in Machine Learning
Explore human-aided machine learning and its potential to empower Nepal's workforce in the global data economy, led by an experienced data scientist.
Latency Monitoring Metrics: Why Average Is Not a Good Measure
Explore why average latency metrics are inadequate. Learn about max latency, true impact, and latency distributions for comprehensive monitoring and analysis of system performance.
Composite SLAs: Calculation and Improvement Strategies - Lecture
Explore composite SLAs, their calculation, and improvement strategies. Learn to apply probability concepts to real components and enhance overall system reliability.