ray tutorialAn introductory tutorial about leveraging Ray core features for distributed patterns.
Stars: ✭ 67 (-18.29%)
Crowd Behavior AnalysisCrowd behavior analysis is an important field of research in modern world. It has wide applications in surveillance and public safety which are one of the prime social concerns. One way to analyze crowd behavior is obtain crowd movement data and then find out outliers in the individual trajectories to infer any abnormal behavior in the crowd.
Stars: ✭ 31 (-62.2%)
machine-learning-capstone-projectThis is the final project for the Udacity Machine Learning Nanodegree: Predicting article retweets and likes based on the title using Machine Learning
Stars: ✭ 28 (-65.85%)
projection-pursuitAn implementation of multivariate projection pursuit regression and univariate classification
Stars: ✭ 24 (-70.73%)
dbt-ml-preprocessingA SQL port of python's scikit-learn preprocessing module, provided as cross-database dbt macros.
Stars: ✭ 128 (+56.1%)
ECoLExtended Complexity Library in R
Stars: ✭ 45 (-45.12%)
NimbusML-SamplesSamples for NimbusML, a Python machine learning package providing simple interoperability between ML.NET and scikit-learn components.
Stars: ✭ 31 (-62.2%)
vf3libVF3 Algorithm - The fastest algorithm to solve subgraph isomorphism on large and dense graphs
Stars: ✭ 58 (-29.27%)
MetaboverseVisualizing and Analyzing Metabolic Networks with Reaction Pattern Recognition
Stars: ✭ 17 (-79.27%)
Python-Machine-Learning-FundamentalsD-Lab's 6 hour introduction to machine learning in Python. Learn how to perform classification, regression, clustering, and do model selection using scikit-learn and TPOT.
Stars: ✭ 46 (-43.9%)
mloperatorMachine Learning Operator & Controller for Kubernetes
Stars: ✭ 85 (+3.66%)
omegamlPython analytics made easy - an open source DataOps, MLOps platform for humans
Stars: ✭ 74 (-9.76%)
ML-For-Beginners12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
Stars: ✭ 40,023 (+48708.54%)
ICC-2019-WC-predictionPredicting the winner of 2019 cricket world cup using random forest algorithm
Stars: ✭ 41 (-50%)
pyclustertendA python package to assess cluster tendency
Stars: ✭ 38 (-53.66%)
Competitive-Feature-LearningOnline feature-extraction and classification algorithm that learns representations of input patterns.
Stars: ✭ 32 (-60.98%)
calcipherCalculates the best possible answer for multiple-choice questions using techniques to maximize accuracy without any other outside resources or knowledge.
Stars: ✭ 15 (-81.71%)
DataSciPyData Science with Python
Stars: ✭ 15 (-81.71%)
topometryA comprehensive dimensional reduction framework to recover the latent topology from high-dimensional data.
Stars: ✭ 64 (-21.95%)
object detectorObject detector from HOG + Linear SVM framework
Stars: ✭ 24 (-70.73%)
Quora question pairs NLP KaggleQuora Kaggle Competition : Natural Language Processing using word2vec embeddings, scikit-learn and xgboost for training
Stars: ✭ 17 (-79.27%)
adaptAwesome Domain Adaptation Python Toolbox
Stars: ✭ 46 (-43.9%)
datascienvdatascienv is package that helps you to setup your environment in single line of code with all dependency and it is also include pyforest that provide single line of import all required ml libraries
Stars: ✭ 53 (-35.37%)
MomentToolboxMatlab code for the paper "A survey of orthogonal moments for image representation: Theory, implementation, and evaluation"
Stars: ✭ 13 (-84.15%)
machine learningA gentle introduction to machine learning: data handling, linear regression, naive bayes, clustering
Stars: ✭ 22 (-73.17%)
pyconvsegnetSemantic Segmentation PyTorch code for our paper: Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition (https://arxiv.org/pdf/2006.11538.pdf)
Stars: ✭ 32 (-60.98%)
Quadcopter SimConQuadcopter Simulation and Control. Dynamics generated with PyDy.
Stars: ✭ 84 (+2.44%)
kdd99-scikitSolutions to kdd99 dataset with Decision tree and Neural network by scikit-learn
Stars: ✭ 50 (-39.02%)
clinicaSoftware platform for clinical neuroimaging studies
Stars: ✭ 153 (+86.59%)
euro2016predictorSoccer Matches Predictor using Machine Learning
Stars: ✭ 38 (-53.66%)
vector space modellingNLP in python Vector Space Modelling and document classification NLP
Stars: ✭ 16 (-80.49%)
scikit-minescikit-mine : pattern mining in Python
Stars: ✭ 45 (-45.12%)
data-science-learning📊 All of courses, assignments, exercises, mini-projects and books that I've done so far in the process of learning by myself Machine Learning and Data Science.
Stars: ✭ 32 (-60.98%)
SigThe most powerful and customizable binary pattern scanner
Stars: ✭ 131 (+59.76%)
linear-treeA python library to build Model Trees with Linear Models at the leaves.
Stars: ✭ 128 (+56.1%)
do-it-dl<Do it! 딥러닝 입문> 도서의 주피터 노트북
Stars: ✭ 77 (-6.1%)
booksA collection of online books for data science, computer science and coding!
Stars: ✭ 29 (-64.63%)
BM25Transformer(Python) transform a document-term matrix to an Okapi/BM25 representation
Stars: ✭ 50 (-39.02%)
Kaio-machine-learning-human-face-detectionMachine Learning project a case study focused on the interaction with digital characters, using a character called "Kaio", which, based on the automatic detection of facial expressions and classification of emotions, interacts with humans by classifying emotions and imitating expressions
Stars: ✭ 18 (-78.05%)
CrabNetPredict materials properties using only the composition information!
Stars: ✭ 57 (-30.49%)
A-Detector⭐ An anomaly-based intrusion detection system.
Stars: ✭ 69 (-15.85%)
KMeans elbowCode for determining optimal number of clusters for K-means algorithm using the 'elbow criterion'
Stars: ✭ 35 (-57.32%)
RecipeAutomated machine learning (AutoML) with grammar-based genetic programming
Stars: ✭ 42 (-48.78%)
CROHME extractorCROHME dataset extractor for OFFLINE-text-recognition task.
Stars: ✭ 77 (-6.1%)
feature engineFeature engineering package with sklearn like functionality
Stars: ✭ 758 (+824.39%)
AIPortfolioUse AI to generate a optimized stock portfolio
Stars: ✭ 28 (-65.85%)
face-recognitionA GPU-accelerated real-time face recognition system based on classical machine learning algorithms
Stars: ✭ 24 (-70.73%)