AI-Machine-Learning Index

📙 AI-Machine-Learning Index

📚 27 Posts
🕒 Last Updated: Sun Aug 23 2026

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

#	Blog Link	Date	Excerpt	Tags
1	AI-Machine-Learning Index	Sun Aug 23 2026	📙 Index of AI-Machine-Learning posts
2	Machine Learning Learning Path	Fri Feb 27 2026	A structured learning path through machine learning fundamentals, covering supervised learning (regression and classification), neural networks, model evaluation and system design, unsupervised learning, recommenders, and reinforcement learning — based on the Coursera Machine Learning course and specialization.	Machine Learning Data Science Supervised Learning Unsupervised Learning Neural Networks Reinforcement Learning Coursera Learning Path
3	Stanford AI Scientist Roadmap 2026	Sat Jun 20 2026	A complete self-study roadmap built entirely from Stanford University's publicly available AI courses. Learn mathematics, machine learning, deep learning, reinforcement learning, large language models, AI systems, RAG, agentic AI, and production deployment through a structured path from foundations to real-world AI engineering.	Stanford Artificial Intelligence AI Engineering Machine Learning Deep Learning Reinforcement Learning Large Language Models LLM Engineering Generative AI Natural Language Processing Computer Vision AI Systems MLOps RAG Agentic AI Stanford CS229 Stanford CS230 Stanford CS224N Stanford CS336 Stanford CS329T Learning Roadmap
4	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
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	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
7	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
8	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
9	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
10	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
11	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
12	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
13	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
14	XGBoost (Extreme Gradient Boosting) Explained	Tue May 26 2026	Learn how XGBoost works, including gradient boosting, decision trees, residual learning, regularization, and why XGBoost is one of the most powerful machine learning algorithms for structured and tabular data.	AI Machine Learning XGBoost Gradient Boosting Decision Trees Ensemble Learning Supervised Learning Classification Regression Data Science Feature Engineering Predictive Modeling Kaggle MLOps
15	Dimensionality Reduction in Machine Learning	Fri Feb 27 2026	Learn how dimensionality reduction simplifies high-dimensional data while preserving important patterns. Explore techniques like PCA and understand how reducing features improves model performance, visualization, and computational efficiency.	Data Science Machine Learning Deep Learning Dimensionality Reduction Feature Engineering Principal Component Analysis Artificial Intelligence
16	Principal Component Analysis (PCA) Explained	Fri Feb 27 2026	Learn how Principal Component Analysis (PCA) reduces the dimensionality of datasets while preserving important information. Understand the intuition, mathematics, and practical uses of PCA in machine learning and data science.	Data Science Machine Learning Deep Learning Principal Component Analysis Dimensionality Reduction Feature Engineering Artificial Intelligence
17	t-SNE (t-distributed Stochastic Neighbor Embedding) Explained	Tue May 26 2026	Learn how t-SNE works for dimensionality reduction and data visualization, including high-dimensional embeddings, neighborhood preservation, probability distributions, KL divergence, and clustering visualization.	AI Machine Learning Deep Learning Data Visualization Dimensionality Reduction t-SNE Embeddings Feature Engineering Clustering Data Science NLP Computer Vision Unsupervised Learning Visualization
18	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.	K-Means Clustering Unsupervised Learning Centroids Machine Learning Distortion Cost Function Random Initialization Data Science
19	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
20	Anomaly Detection Using Gaussian Distribution in Machine Learning	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
21	Anomaly Detection Using Multivariate Gaussian Distribution	Fri Feb 27 2026	Learn anomaly detection using multivariate Gaussian distribution to identify unusual patterns and correlated outliers in datasets. Understand covariance matrices, parameter estimation, probability density functions, and threshold-based anomaly detection techniques used in machine learning systems.	Anomaly Detection Gaussian Distribution Normal Distribution Outlier Detection Unsupervised Learning Probability Models Machine Learning Data Science Statistical Modeling
22	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.	Recommender Systems Collaborative Filtering Content-Based Filtering Machine Learning Hybrid Recommendation Cost Function Data Science
23	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
24	Photo OCR: Sliding Window Detection, Character Segmentation and Recognition	Fri Feb 27 2026	Learn how classical Photo OCR pipelines detect and read text in images using sliding window text detection, character segmentation, character recognition classifiers, and artificial data synthesis — plus how modern computer vision (CNNs, YOLO, Vision Transformers) has superseded this approach.	Optical Character Recognition OCR Sliding Window Computer Vision Image Classification Machine Learning Object Detection Data Science AI
25	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
26	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
27	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