Grad-CAM, which stands for Gradient-weighted Class Activation Mapping, is a technique used in artificial intelligence (AI) to understand and visualize how a Convolutional Neural Network (CNN) makes its predictions, particularly in computer vision tasks. It… Grad-CAM: definitions, applications and drawbacks
Forward feature selection with cross-validation incorporates cross-validation at each step to get a reliable estimate of how well a model with a particular set of features is likely to perform on unseen data. Without cross-validation,… A comparison between forward feature selection with cross-validation, forward selection guided by AIC/BIC, and Lasso regularization with Python Code
Explainable AI refers to methods and techniques that help humans understand and interpret the predictions and decisions made by machine learning (ML) models. It aims to open up the “black box” nature of complex models… Explainable AI (XAI) methods & Cheat Sheet
Polynomial regression is a form of regression analysis in which the relationship between the independent variable and the dependent variable is modeled as an nth degree polynomial. This approach allows for a more flexible fit… Enhancing Regression Models with Polynomial Features and L1 Lasso Regularization
Analyzing various data types and characteristics enhances model efficiency, aiding in pattern recognition and informed decisions. An example of building a Predictive Model for Customer Churn is provided to illustrate this idea.
Statistical Context: Projection and transformation matrices appear frequently in statistics, especially in regression and PCA, where they play a crucial role in simplifying complex datasets and revealing underlying patterns. These matrices help in miniminimizemizing errors… Key Roles of Transformation Matrices in Regression and PCA
Basic probability & statistics Optimization & Background for Machine Learning and Deep Learning Deep learning: Introductory courses Advanced: Machine Learning Programming courses Other: Google Cloud Machine Learning Crash Course:
Polynomial regression is a form of regression analysis in which the relationship between the independent variable and the dependent variable is modeled as an -degree polynomial. It’s an extension of linear regression that can capture… Understanding Polynomial Regression: A Comprehensive Comic Guide with codes
Random forests enhance predictive performance by allowing quantile predictions, offering insights into outcome variability. This method is vital for risk assessment, aiding informed decision-making in uncertain environments.
Support Vector Classifier (SVC) is a powerful algorithm for classification tasks, capable of handling linear and non-linear data using different kernel functions. It efficiently handles high-dimensional data for applications like image recognition and bioinformatics. Python and R codes demonstrate SVM usage for binary classification with breast cancer and mtcars datasets, respectively.