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ad_examples

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

aima-python

Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"

anomaly-detection-resources

Anomaly detection related books, papers, videos, and toolboxes

awesome-courses

:books: List of awesome university courses for learning Computer Science!

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

complete-python-3-bootcamp

Course Files for Complete Python 3 Bootcamp Course on Udemy

dagmm

My attempt at reproducing the paper Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection

data_preprocessing

how to data preprocessing

data_preprocessing_with_python

datapreprocessing

1.MACHINE LEARNING DATA-PREPROCESSING

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

dive-into-machine-learning

Dive into Machine Learning with Python Jupyter notebook and scikit-learn!

hello-world

Hello World project is a time-honored tradition in computer programming.

kaggle-titanic

A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstrates basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.

linux-command-cheatsheet

Some useful linux commands

machine-learning

Content for Udacity's Machine Learning curriculum

machine-learning-for-software-engineers

A complete daily plan for studying to become a machine learning engineer.

machine-learning-tutorials

machine learning and deep learning tutorials, articles and other resources

machine-learning-with-python

Python code for common Machine Learning Algorithms

machine-learning-workflow-with-python

This is a comprehensive ML techniques with python: Define the Problem- Specify Inputs & Outputs- Data Collection- Exploratory data analysis -Data Preprocessing- Model Design- Training- Evaluation

machine_learning_examples

A collection of machine learning examples and tutorials.

moa

MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.

modal

A modular active learning framework for Python

newgittest

outlier-discovery-distance-based-approach

Outlier detection by determining data which deviates from its cluster. The threshold is defined from constant user input multiplied by mean of cluster-member's distance.

outlier_detection

A comparitive study of different machine and deep learning outlier detection algorithms for credit card fraud detection

outlierdetection

Group project for Statistical Learning in Peking University 2018 Fall. This is a brief survey of outlier detection tasks, including implementation and discussion of several outlier detection algorithms.

parallel-streaming-outlier-detection

Parallel approach on distance based outlier detection on streaming data

practical-machine-learning-with-python

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

pyod

A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)