Machine Learning And Data ScienceThis is a repository which contains all my work related Machine Learning, AI and Data Science. This includes my graduate projects, machine learning competition codes, algorithm implementations and reading material.
Stars: ✭ 137 (-59.1%)
Kaggle CompetitionsThere are plenty of courses and tutorials that can help you learn machine learning from scratch but here in GitHub, I want to solve some Kaggle competitions as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template.
Stars: ✭ 86 (-74.33%)
Deep Learning BookRepository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"
Stars: ✭ 2,705 (+707.46%)
Data Science CompetitionsGoal of this repo is to provide the solutions of all Data Science Competitions(Kaggle, Data Hack, Machine Hack, Driven Data etc...).
Stars: ✭ 572 (+70.75%)
Unet TgsApplying UNET Model on TGS Salt Identification Challenge hosted on Kaggle
Stars: ✭ 81 (-75.82%)
Machine Learning Workflow With PythonThis is a comprehensive ML techniques with python: Define the Problem- Specify Inputs & Outputs- Data Collection- Exploratory data analysis -Data Preprocessing- Model Design- Training- Evaluation
Stars: ✭ 157 (-53.13%)
Cikm 2019 Analyticup1st Solution for 2019-CIKM-Analyticup, Efficient and Novel Item Retrieval for Large-scale Online Shopping Recommendation
Stars: ✭ 173 (-48.36%)
Gwu data miningMaterials for GWU DNSC 6279 and DNSC 6290.
Stars: ✭ 217 (-35.22%)
Amazing Feature EngineeringFeature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.
Stars: ✭ 218 (-34.93%)
AlphatoolsQuantitative finance research tools in Python
Stars: ✭ 226 (-32.54%)
Data Science Bowl 2018DATA-SCIENCE-BOWL-2018 Find the nuclei in divergent images to advance medical discovery
Stars: ✭ 76 (-77.31%)
Kaggle TitanicA 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.
Stars: ✭ 709 (+111.64%)
Data Science HacksData Science Hacks consists of tips, tricks to help you become a better data scientist. Data science hacks are for all - beginner to advanced. Data science hacks consist of python, jupyter notebook, pandas hacks and so on.
Stars: ✭ 273 (-18.51%)
Data Science LearningRepository of code and resources related to different data science and machine learning topics. For learning, practice and teaching purposes.
Stars: ✭ 273 (-18.51%)
CodeCompilation of R and Python programming codes on the Data Professor YouTube channel.
Stars: ✭ 287 (-14.33%)
Sklearn EvaluationMachine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.
Stars: ✭ 294 (-12.24%)
CardioCardIO is a library for data science research of heart signals
Stars: ✭ 218 (-34.93%)
TutorialsAI-related tutorials. Access any of them for free → https://towardsai.net/editorial
Stars: ✭ 204 (-39.1%)
DatascienceprojectsThe code repository for projects and tutorials in R and Python that covers a variety of topics in data visualization, statistics sports analytics and general application of probability theory.
Stars: ✭ 223 (-33.43%)
Sc17SuperComputing 2017 Deep Learning Tutorial
Stars: ✭ 211 (-37.01%)
MydatascienceportfolioApplying Data Science and Machine Learning to Solve Real World Business Problems
Stars: ✭ 227 (-32.24%)
Amazon Forest Computer VisionAmazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks
Stars: ✭ 346 (+3.28%)
CartoframesCARTO Python package for data scientists
Stars: ✭ 208 (-37.91%)
KaggleMy kaggle competition solution and notebook
Stars: ✭ 14 (-95.82%)
Pydataroadopen source for wechat-official-account (ID: PyDataLab)
Stars: ✭ 302 (-9.85%)
Python SeminarPython for Data Science (Seminar Course at UC Berkeley; AY 250)
Stars: ✭ 302 (-9.85%)
CartolaExtração de dados da API do CartolaFC, análise exploratória dos dados e modelos preditivos em R e Python - 2014-20. [EN] Data munging, analysis and modeling of CartolaFC - the most popular fantasy football game in Brazil and maybe in the world. Data cover years 2014-19.
Stars: ✭ 304 (-9.25%)
Covid19zaCoronavirus COVID-19 (2019-nCoV) Data Repository and Dashboard for South Africa
Stars: ✭ 208 (-37.91%)
GophernotesThe Go kernel for Jupyter notebooks and nteract.
Stars: ✭ 3,100 (+825.37%)
FacetHuman-explainable AI.
Stars: ✭ 269 (-19.7%)
Python Is CoolCool Python features for machine learning that I used to be too afraid to use. Will be updated as I have more time / learn more.
Stars: ✭ 2,962 (+784.18%)
ProbabilityProbabilistic reasoning and statistical analysis in TensorFlow
Stars: ✭ 3,550 (+959.7%)
Scikit Learn VideosJupyter notebooks from the scikit-learn video series
Stars: ✭ 3,254 (+871.34%)
PycaretAn open-source, low-code machine learning library in Python
Stars: ✭ 4,594 (+1271.34%)
Eli5A library for debugging/inspecting machine learning classifiers and explaining their predictions
Stars: ✭ 2,477 (+639.4%)
FlamlA fast and lightweight AutoML library.
Stars: ✭ 205 (-38.81%)
Course NlpA Code-First Introduction to NLP course
Stars: ✭ 3,029 (+804.18%)
TensorwatchDebugging, monitoring and visualization for Python Machine Learning and Data Science
Stars: ✭ 3,191 (+852.54%)
Apricotapricot implements submodular optimization for the purpose of selecting subsets of massive data sets to train machine learning models quickly. See the documentation page: https://apricot-select.readthedocs.io/en/latest/index.html
Stars: ✭ 306 (-8.66%)
EvidentlyInteractive reports to analyze machine learning models during validation or production monitoring.
Stars: ✭ 304 (-9.25%)