📙 AI-Machine-Learning Index

📚 22 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	Anomaly Detection: Identifying Rare and Unusual Patterns in Data	Fri Feb 27 2026	Learn how anomaly detection models identify unusual data points using statistical methods such as Gaussian distributions. Understand how to detect fraud, system failures, and rare events in real-world datasets.	Anomaly Detection Outlier Detection Gaussian Distribution Unsupervised Learning Machine Learning Fraud Detection Statistical Modeling Data Science AI
6	Anomaly Detection Using Gaussian Distribution: Detecting Outliers with Probability Models	Fri Feb 27 2026	Learn how anomaly detection works using the Gaussian (normal) distribution. Understand how to model data probabilistically, estimate parameters, compute likelihoods, and identify outliers using threshold-based decision making in machine learning systems.	Anomaly Detection Gaussian Distribution Normal Distribution Outlier Detection Unsupervised Learning Probability Models Machine Learning Data Science Statistical Modeling
7	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
8	Collaborative Filtering: Building Recommender Systems with Feature Learning	Fri Feb 27 2026	Learn how collaborative filtering powers modern recommender systems by simultaneously learning user preferences and item features from rating data. Understand the optimization objective, matrix factorization approach, and how gradient-based methods enable scalable recommendations.	Collaborative Filtering Recommender Systems Matrix Factorization Feature Learning Gradient Descent Machine Learning Personalization Unsupervised Learning Data Science
9	Large Scale Machine Learning: Training Models on Massive Datasets	Fri Feb 27 2026	Explore techniques for scaling machine learning algorithms to large datasets, including stochastic gradient descent and mini-batch gradient descent. Learn how to efficiently train linear models, logistic regression, and neural networks on millions of examples.	Large Scale Machine Learning Stochastic Gradient Descent Mini-Batch Gradient Descent Optimization Big Data Scalable ML Gradient Descent Machine Learning Data Engineering
10	Stochastic Gradient Descent (SGD): Efficient Optimization for Large Datasets	Fri Feb 27 2026	Understand how Stochastic Gradient Descent works and why it is widely used in large-scale machine learning. Learn how SGD updates model parameters using one training example at a time to improve computational efficiency and scalability.	Stochastic Gradient Descent SGD Optimization Large Scale Machine Learning Gradient Descent Machine Learning Big Data Scalable Algorithms Training Algorithms
11	MapReduce for Large-Scale Machine Learning: Distributed Training at Scale	Fri Feb 27 2026	Learn how the MapReduce framework enables distributed computation for large-scale machine learning. Understand how it helps parallelize gradient computation and process massive datasets efficiently across multiple machines.	MapReduce Distributed Computing Large Scale Machine Learning Big Data Parallel Processing Scalable ML Optimization Data Engineering Machine Learning
12	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
13	Evaluating a Hypothesis in Neural Networks	Fri Feb 27 2026	Learn how neural networks evaluate a hypothesis using forward propagation. Understand how inputs pass through layers, weights, and activation functions to produce predictions in machine learning models.	Data Science Machine Learning Deep Learning Neural Networks Artificial Intelligence Forward Propagation Hypothesis Function
14	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
15	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
16	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
17	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
18	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
19	Support Vector Machines (SVM): Maximizing Margins for Robust Machine Learning Models	Fri Feb 27 2026	Learn how Support Vector Machines (SVM) build powerful classification models by finding the optimal separating hyperplane that maximizes the margin between classes. Discover how the margin, regularization parameter C, and kernel functions help SVM handle both linear and non-linear data while improving generalization performance.	Support Vector Machine SVM Maximum Margin Classifier Kernel Trick Machine Learning Classification Regularization Hyperplane Supervised Learning Data Science
20	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
21	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
22	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