📙 AI-Machine-Learning Index

📚 14 Posts
🕒 Last Updated: Tue Mar 03 2026

This folder contains AI-Machine-Learning-related posts.

#	Blog Link	Date	Excerpt	Tags
1	AI-Machine-Learning Index	Tue Mar 03 2026	📙 Index of AI-Machine-Learning posts
2	Machine Learning Learning Path	Fri Feb 27 2026	Overview of AI infrastructure fundamentals including NVIDIA GPU architecture, training vs inference workloads, data center design, networking, storage, virtualization, and AI operations best practices.	AI Infrastructure AI Operations GPU Computing Data Center CUDA AI Training AI Inference Networking Storage Virtualization MLOps
3	Machine Learning: Introduction and Core Algorithms	Tue Feb 24 2026	Beginner-friendly introduction to machine learning, covering key concepts, model types, supervised and unsupervised learning, and essential algorithms such as linear regression, logistic regression, decision trees, and clustering.	Machine Learning AI Supervised Learning Unsupervised Learning Regression Classification Clustering Algorithms Data Science
4	K-Means Clustering	Fri Feb 27 2026	K-Means is a powerful unsupervised learning algorithm for clustering data into coherent subsets. It iteratively assigns points to the nearest centroid and updates centroids to minimize distortion, making it widely used in practice.	Regularization Cost Function Bias-Variance Tradeoff Machine Learning Overfitting Underfitting Lasso Regression Ridge Regression Model Complexity Supervised Learning Data Science
5	Linear Regression Explained: Single Variable and Multivariate Models with Gradient Descent	Thu Feb 26 2026	Learn linear regression in machine learning, including single-variable and multivariate models, hypothesis function, cost function (MSE), gradient descent optimization, feature scaling, assumptions, and real-world implementation examples.	Linear Regression Machine Learning Single Variable Linear Regression Multivariate Linear Regression Supervised Learning Regression Analysis Cost Function Gradient Descent Feature Scaling Data Science
6	Polynomial Regression	Fri Feb 27 2026	Understand polynomial regression with practical examples.	Polynomial Regression Bias-Variance Tradeoff Overfitting Underfitting Lasso Regression Ridge Regression L1 Regularization L2 Regularization Machine Learning Model Selection Supervised Learning Data Science
7	Normal Equation in Linear Regression: Formula, Intuition, and Comparison with Gradient Descent	Fri Feb 27 2026	Understand the Normal Equation in linear regression, its closed-form solution, mathematical formula, advantages, limitations, and how it compares to gradient descent for model optimization.	Normal Equation Linear Regression Gradient Descent Machine Learning Closed-Form Solution Cost Function Supervised Learning Data Science Model Optimization
8	Logistic Regression for Classification: Concept, Sigmoid Function, Cost Function, and Implementation	Fri Feb 27 2026	Complete guide to logistic regression for binary classification, including the sigmoid function, hypothesis model, cost function, decision boundary, gradient descent, and practical machine learning implementation.	Logistic Regression Classification Machine Learning Binary Classification Supervised Learning Sigmoid Function Decision Boundary Cost Function Gradient Descent Data Science
9	Logistic Regression for Classification: Concept, Sigmoid Function, Cost Function, and Implementation	Fri Feb 27 2026	Complete guide to logistic regression for binary classification, including the sigmoid function, hypothesis model, cost function, decision boundary, gradient descent, and practical machine learning implementation.	Logistic Regression Classification Machine Learning Binary Classification Supervised Learning Sigmoid Function Decision Boundary Cost Function Gradient Descent Data Science
10	Bias-Variance Dilemma	Fri Feb 27 2026	Understanding the bias-variance tradeoff in machine learning, including the concepts of bias and variance, underfitting and overfitting, and strategies to balance model complexity for better generalization.	Bias-Variance Tradeoff Machine Learning Overfitting Underfitting Regularization Lasso Regression Ridge Regression Model Complexity Supervised Learning Data Science
11	Cost Function Regularization: Balancing Bias and Variance in Machine Learning Models	Fri Feb 27 2026	Learn how cost function regularization helps prevent overfitting in machine learning models by adding a penalty term to the cost function, controlling model complexity, and improving generalization performance.	Regularization Cost Function Bias-Variance Tradeoff Machine Learning Overfitting Underfitting Lasso Regression Ridge Regression Model Complexity Supervised Learning Data Science
12	Regularized Linear Regression	Fri Feb 27 2026	Learn how regularization helps prevent overfitting in linear regression by adding a penalty term to the cost function, modifying the gradient descent update rules, and improving model generalization.	Regularization Cost Function Bias-Variance Tradeoff Machine Learning Overfitting Underfitting Lasso Regression Ridge Regression Model Complexity Supervised Learning Data Science
13	Regularized Logistic Regression	Fri Feb 27 2026	Regularization helps prevent overfitting by penalizing large weights. Compared to the non-regularized model, the regularized version produces smoother decision boundaries.	Regularization Cost Function Bias-Variance Tradeoff Machine Learning Overfitting Underfitting Lasso Regression Ridge Regression Model Complexity Supervised Learning Data Science
14	Recommender Systems: Collaborative Filtering, Content-Based Filtering, and Hybrid Approaches	Fri Feb 27 2026	Comprehensive guide to recommender systems, covering collaborative filtering, content-based filtering, and hybrid approaches, with practical implementation examples and best practices for building effective recommendation engines.	Regularization Cost Function Bias-Variance Tradeoff Machine Learning Overfitting Underfitting Lasso Regression Ridge Regression Model Complexity Supervised Learning Data Science