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 (+75%)
multiscorerA module for allowing the use of multiple metric functions in scikit's cross_val_score
Stars: ✭ 21 (+31.25%)
pyclustertendA python package to assess cluster tendency
Stars: ✭ 38 (+137.5%)
evernote-backupBackup & export all Evernote notes and notebooks
Stars: ✭ 104 (+550%)
kaggledatasetsCollection of Kaggle Datasets ready to use for Everyone (Looking for contributors)
Stars: ✭ 44 (+175%)
dbt-ml-preprocessingA SQL port of python's scikit-learn preprocessing module, provided as cross-database dbt macros.
Stars: ✭ 128 (+700%)
mlhandbookMy textbook for teaching Machine Learning
Stars: ✭ 23 (+43.75%)
chartsHelm charts for creating reproducible and maintainable deployments of Polyaxon with Kubernetes.
Stars: ✭ 32 (+100%)
nba-analysisUsing machine learning libraries to analyze NBA data
Stars: ✭ 14 (-12.5%)
cliPolyaxon Core Client & CLI to streamline MLOps
Stars: ✭ 18 (+12.5%)
NimbusML-SamplesSamples for NimbusML, a Python machine learning package providing simple interoperability between ML.NET and scikit-learn components.
Stars: ✭ 31 (+93.75%)
rlss-2019Materials for the Practical Sessions of the Reinforcement Learning Summer School 2019: Bandits, RL & Deep RL (PyTorch).
Stars: ✭ 79 (+393.75%)
naas⚙️ Schedule notebooks, run them like APIs, expose securely your assets: Jupyter as a viable ⚡️ Production environment
Stars: ✭ 219 (+1268.75%)
playgroundA Streamlit application to play with machine learning models directly from the browser
Stars: ✭ 48 (+200%)
dlime experimentsIn this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).
Stars: ✭ 21 (+31.25%)
ML-TrackThis repository is a recommended track, designed to get started with Machine Learning.
Stars: ✭ 19 (+18.75%)
audio noise clusteringhttps://dodiku.github.io/audio_noise_clustering/results/ ==> An experiment with a variety of clustering (and clustering-like) techniques to reduce noise on an audio speech recording.
Stars: ✭ 24 (+50%)
machine learningA gentle introduction to machine learning: data handling, linear regression, naive bayes, clustering
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booksA collection of online books for data science, computer science and coding!
Stars: ✭ 29 (+81.25%)
pycobrapython library implementing ensemble methods for regression, classification and visualisation tools including Voronoi tesselations.
Stars: ✭ 111 (+593.75%)
skrobotskrobot is a Python module for designing, running and tracking Machine Learning experiments / tasks. It is built on top of scikit-learn framework.
Stars: ✭ 22 (+37.5%)
rezonanceContent Based Music Recommendation Service
Stars: ✭ 28 (+75%)
handson-ml도서 "핸즈온 머신러닝"의 예제와 연습문제를 담은 주피터 노트북입니다.
Stars: ✭ 285 (+1681.25%)
ml webappExplore machine learning models. Leveraging scikit-learn's models and exposing their behaviour through API
Stars: ✭ 29 (+81.25%)
python-for-excelThis is the companion repo of the O'Reilly book "Python for Excel".
Stars: ✭ 253 (+1481.25%)
ASD-ML-APIThis project has 3 goals: To find out the best machine learning pipeline for predicting ASD cases using genetic algorithms, via the TPOT library. (Classification Problem) Compare the accuracy of the accuracy of the determined pipeline, with a standard Naive-Bayes classifier. Saving the classifier as an external file, and use this file in a Flask…
Stars: ✭ 14 (-12.5%)
emoji-prediction🤓🔮🔬 Emoji prediction from a text using machine learning
Stars: ✭ 41 (+156.25%)
mloperatorMachine Learning Operator & Controller for Kubernetes
Stars: ✭ 85 (+431.25%)
notebooksA collection of Livebook notebooks
Stars: ✭ 40 (+150%)
DBA-MasteryMain repository from contributions from my blog
Stars: ✭ 16 (+0%)
scitimeTraining time estimation for scikit-learn algorithms
Stars: ✭ 119 (+643.75%)
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 (+100%)
sklearn-matlabMachine learning in Matlab using scikit-learn syntax
Stars: ✭ 27 (+68.75%)
ML-For-Beginners12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
Stars: ✭ 40,023 (+250043.75%)
scikit-hyperbandA scikit-learn compatible implementation of hyperband
Stars: ✭ 68 (+325%)
Python-for-Remote-Sensingpython codes for remote sensing applications will be uploaded here. I will try to teach everything I learn during my projects in here.
Stars: ✭ 20 (+25%)
ray tutorialAn introductory tutorial about leveraging Ray core features for distributed patterns.
Stars: ✭ 67 (+318.75%)
dstoolboxTools that make working with scikit-learn and pandas easier.
Stars: ✭ 43 (+168.75%)
xlinesX lines of Python
Stars: ✭ 100 (+525%)
abessFast Best-Subset Selection Library
Stars: ✭ 266 (+1562.5%)
osprey🦅Hyperparameter optimization for machine learning pipelines 🦅
Stars: ✭ 71 (+343.75%)
kaggle-titanicTitanic assignment on Kaggle competition
Stars: ✭ 30 (+87.5%)
PyRCNA Python 3 framework for Reservoir Computing with a scikit-learn-compatible API.
Stars: ✭ 39 (+143.75%)