PCA and DBSCAN based anomaly and outlier detection method for time series data.

# Machine Learning (Coursera) This is my solution to all the programming assignments and quizzes of Machine-Learning (Coursera) taught by Andrew Ng. After completing this course you will get a broad idea of Machine learning algorithms. Try to solve all the assignments by yourself first, but if you get stuck somewhere then feel free to browse the code. ## Contents * Lectures Slides * Solution to programming assignment * Solution to Quizzes by Andrew Ng, Stanford University, [Coursera](https://www.coursera.org/learn/machine-learning/home/welcome) ### Week 1 - [X] Videos: Introduction - [X] Quiz: Introduction - [X] Videos: Linear Regression with One Variable - [X] Quiz: Linear Regression with One Variable ### Week 2 - [X] Videos: Linear Regression with Multiple Variables - [X] Quiz: Linear Regression with Multiple Variables - [X] Videos: Octave/Matlab Tutorial - [X] Quiz: Octave/Matlab Tutorial - [X] Programming Assignment: Linear Regression ### Week 3 - [X] Videos: Logistic Regression - [X] Quiz: Logistic Regression - [X] Videos: Regularization - [X] Quiz: Regularization - [X] Programming Assignment: Logistic Regression ### Week 4 - [X] Videos: Neural Networks: Representation - [X] Quiz: Neural Networks: Representation - [X] Programming Assignment: Multi-class Classification and Neural Networks ### Week 5 - [X] Videos: Neural Networks: Learning - [X] Quiz: Neural Networks: Learning - [X] Programming Assignment: Neural Network Learning ### Week 6 - [X] Videos: Advice for Applying Machine Learning - [X] Quiz: Advice for Applying Machine Learning - [X] Videos: Programming Assignment: Regularized Linear Regression and Bias/Variance - [X] Machine Learning System Design - [X] Quiz: Machine Learning System Design ### Week 7 - [X] Videos: Support Vector Machines - [X] Quiz: Support Vector Machines - [X] Programming Assignment: Support Vector Machines ### Week 8 - [X] Videos: Unsupervised Learning - [X] Quiz: Unsupervised Learning - [X] Videos: Dimensionality Reduction - [X] Quiz: Principal Component Analysis - [X] Programming Assignment: K-Means Clustering and PCA ### Week 9 - [X] Videos: Anomaly Detection - [X] Quiz: Anomaly Detection - [X] Videos: Recommender Systems - [X] Quiz: Recommender Systems - [X] Programming Assignment: Anomaly Detection and Recommender Systems ### Week 10 - [X] Videos: Large Scale Machine Learning - [X] Quiz: Large Scale Machine Learning ### Week 11 - [X] Videos: Application Example: Photo OCR - [X] Quiz: Application: Photo OCR ## Certificate * [Verified Certificate]() ## References [[1] Machine Learning - Stanford University](https://www.coursera.org/learn/machine-learning)

Healthcare fraud detection using PCA based anomaly detection on multivariate data.

About the code release for "Federated PCA on Grassmann Manifold for IoT Anomaly Detection" ToN2024

This is an experimental of anomalies detection by applying different approach to the problem. PCA component regularization method, K-Mean Clustering, SVM and Gausian Distribution models has been used to detect anomalies on time series data.

Anomaly Detection using PCA and BiGAN

Anomaly detection on a production line using principal component analysis (PCA) and kernel principal component analysis (KPCA) *from scratch*.

MATLAB Code for Linear & Logistic Regression, SVM, K Means and PCA, Neural Networks Learning, Multiclass Classification, Anomaly Detection and Recommender systems.

This project uses unsupervised clustering via DBSCAN to detect fraudulent transactions without any prior labeling. By leveraging Z-score standardization, PCA for visualization, and cluster evaluation metrics, we create an effective and interpretable anomaly detection pipeline.

Linear regression, logistic regression, polynomial regression, multiclass classification, neural networks, KMeans, Principle Component Analysis (PCA), and Support Vector Machine (SVM). Fun machine learning applications: hand-written digit recognition model, spam email filter, image compression, anomaly detection model, and movie recommendation system.

Practical Machine Learning : Machine Learning in Nut shell, Supervised Learning, Unsupervised Learning, ML applications in the real world. Introduction to Feature engineering and Data Pre-processing: Data Preparation, Feature creation, Data cleaning & transformation, Data Validation & Modelling, Feature selection Techniques, Dimensionality reduction, Recommendation Systems and anomaly detection, PCA ML Algorithms: Decision Trees, Oblique trees, Random forest, Bayesian analysis and Naïve bayes classifier, Support vector Machines, KNN, Gradient boosting, Ensemble methods, Bagging & Boosting, Association rules learning, Apriori and FP growth algorithms, Linear and Nonlinear classification, Regression Techniques, Clustering, K-means, Overview of Factor Analysis, ARIMA, ML in real time, Algorithm performance metrics, ROC, AOC, Confusion matrix, F1score, MSE, MAE, DBSCAN Clustering in ML, Anomaly Detection, Recommender System Self-Study: • Usage of ML algorithms, Algorithm performance metrics (confusion matrix sensitivity, Specificity, ROC, AOC, F1score, Precision, Recall, MSE, MAE) • Credit Card Fraud Analysis, Intrusion Detection system

About the code release for "Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks" INFOCOM2023

ML / Unsupervised / Anomaly Detection / PCA+IsolationForest.

Anomaly Detection in OT datasets through machine learning (AE/VAE/PCA)

Machine learning techniques, such as Linear Regression, Logistic Regression, Neural Networks (feedforward propagation, backpropagation algorithms), Diagnosing Bias/Variance, Evaluating a Hypothesis, Learning Curves, Error Analysis, Support Vector Machines, K-Means Clustering, PCA, Anomaly Detection System, and Recommender System.

This repository consists of machine learning algorithm such as : Regression, Classification, Neural network, SVM, PCA, Clustering, Anomaly detection.

UnifyID Fellowship project - Anomaly detection with inverted PCA

Anomalies detection in multivariant sensor data comparing PCA and LSTM Autoencoder for predictive maintenance applications

A dashboard build for anomaly detection in time series using Principal Component Analysis (PCA) and Bayesian Online Change Point Detection (BOCPD).

A notebook using many unsupervised learning techniques. PCA, K-means, Gaussian Mixtures. Clustering, dimensionality reduction, anomaly detection

Machine Learning models on Anomaly detection, Recommender system on movies based on IMDB dataset, Digit Identification using Logistic regression, Neural network based facial feature recognition, PCA, SVM based Spam filter, Logistic Regression - Nelder Mead

This repository contains code for paper "Federated PCA on Grassmann Manifold: Convergence Analysis and Advancements in IoT Anomaly Detection"

Network Traffic Anomaly Detection using PCA and Isolation Forest This project demonstrates using PCA for dimensionality reduction and Isolation Forest for detecting anomalies in network traffic data. It includes data simulation, model training, anomaly prediction, and visualization to identify suspicious activities in network traffic.

Anomaly Detection 🕵🏻 on Three Diverse Multivariate Time-Series datasets in 🩺 Health Care, 🏨 Tourism, & 🚦 Transportation Sectors using optimal techniques including ANN, Isolation Forest, SVM, PCA, K-Means, VAR, and 3-D Clustering.

matlab machine learning projects: including linear regression, logistic regression, neural networks, svm, k-means clustering, pca, anomaly detection and some special applications such as recommender systems and some tricks in building a machine learning system.

@CoditEU Project to train a real time anomaly detection model. MelSpectrogram extraction from .wav -> Data augmentation (rolling, stretching, etc...) -> Generating multi class label (specific failure) with PCA+KMeans -> CNN training on multiclass labeled MelSpectrograms -> Configuring HTTP endpoint with Flask & deploying with Docker -> Uploading to ACR (Azure Container Registry) & testing with ACI (Azure Container Instance)

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explores anomaly detection in lithium-ion battery material data using PCA & Isolation Forest

fraud detection

thesis - anomaly detection pca and lstm modules