machine_learning.md

Machine Learning

• Awesome production machine learning
  • awesome-production-machine-learning
• Awesome-Mobile-Machine-Learning
• Awesome Quantum Machine Learning
• VSBE2020: OAQA 양자 머신러닝 알고리즘의 기반 - Hyun Byung Park (박현병), 양자오리 at KAITA - YouTube
• Awesome-Mobility-Machine-Learning-Contents
• applied-ml: 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production
• Machine Learning Roadmap 2020 mindmap
• ml-roadmap
• ml-engineer-roadmap: WIP: Roadmap to becoming a machine learning engineer in 2020
• 머신러닝 엔지니어 커리어 로드맵. 송호연 - PyCon Korea 2021 - YouTube
• How to Become a Machine Learning Engineer in 2022? Step-by-Step
• OpenAI 그렉 브록먼 "나는 어떻게 머신러닝 전문가가 됐나" | 요즘IT
• Essential Guide to keep up with AI/ML/CV
• 머신러닝
• reddit.com/r/learnmachinelearning
• reddit.com/r/LearnMachineLearning/wiki/reference
• My Curated List of AI and Machine Learning Resources from Around the Web
• The 25 Best Data Science and Machine Learning GitHub Repositories from 2018
• 수학을 포기한 직업 프로그래머가 머신러닝 학습을 시작하기위한 학습법 소개
• 머신러닝 공부 순서, 방법 및 강의 정리
• 딥러닝 공부 가이드 (HW / SW 준비편)
• How to Get Hired as a Machine Learning Engineer
• Ask HN: 머신 러닝 엔지니어들은 업무시간에 어떤 일 하나요? | GeekNews
• AI, ML and Data Engineering InfoQ Trends Report - August 2021
• A Peek at Trends in Machine Learning
  • #20. 딥러닝 핫 키워드의 변화
• Top 5 Machine Learning Trends For 2022 | by Suzzy Writes | Jul, 2022 | Heartbeat
• ML엔지니어가 데이터 엔지니어링으로 커리어를 시작하면 좋은 이유 - YouTube data engineering
• 어떻게 머신러닝 엔지니어로 피봇할 수 있을까요? | GeekNews
• Machine Learning is Fun!
  • The world’s easiest introduction to Machine Learning
  • Part 2 Using Machine Learning to generate Super Mario Maker levels
  • Part 3: Deep Learning and Convolutional Neural Networks
  • Part 4: Modern Face Recognition with Deep Learning
  • Part 5: Language Translation with Deep Learning and the Magic of Sequences
  • Part 6: How to do Speech Recognition with Deep Learning
  • Part 7: Abusing Generative Adversarial Networks to Make 8-bit Pixel Art
  • Part 8: How to Intentionally Trick Neural Networks
• 기계 학습(Machine Learning, 머신 러닝)은 즐겁다!
  • Part 1
  • Part 2 기계 학습을 사용해서 Super Mario Maker의 레벨 제작하기
  • Part 3 딥러닝(Deep Learning)과 컨볼루션 신경망(Convolutional Neural Network 또는 CNN)
  • Part 4 딥러닝(Deep Learning)을 사용한 최신 얼굴 인식(Face Recognition)
  • Part 5 딥러닝(Deep Learning)과 시퀀스(Sequence)의 마법을 사용한 언어 번역(Language Translation)
  • Part 6 딥러닝(Deep Learning)을 이용한 음성 인식(Speech Recognition)
  • Part 7 생성적 적대 신경망(Generative Adversarial Networks)을 활용해 8비트 픽셀 아트(Pixel Art) 만들기
  • Part 8 신경망을 의도적으로 속이는 방법
• 머신러닝의 소개 (ML Zero to Hero 1부)
• Introduction to Machine Learning
• Visual Intro to Machine Learning and Deep Learning
• 'Machine Learning' 카테고리의 글 목록
• An annotated path to start with Machine Learning
• How to get started with Machine Learning in about 10 minutes
• 통계 & 기계학습 기본 개념
• 기계학습에서 사용되는 수학 공식
• 머신러닝 속 수학
• Probability Distribution 기계학습을 위한 수학
• How do I learn mathematics for machine learning?
• An end to end implementation of a Machine Learning pipeline
• How to Code Neat Machine Learning Pipelines
• 머신 러닝 파이프라인 | Pega Devlog
• semantic-segmentation-ml-pipeline
  • End to End ML Pipeline for Semantic Segmentation Task
• Machine Learning Pipeline As A Language by Daniel Berecz - YouTube scala
• 머신러닝 강의 모음
• 머신러닝, 제대로 배우는 법
• 정규강의 : 기계 학습
• 기계학습 여름캠프
• 기계학습 (Machine Learning) 입문하기
• 알파고? 정말쉬운 머신러닝 기초개념
• 기본적인 머신러닝과 딥러닝 강좌
• 2016 여름 머신러닝 워크샵 1일차 강의 (KAIST 오혜연 교수님)
• Machine Learning 스터디 (1) Machine Learning이란?
• The Data Fabric for Machine Learning. Part 1. | by Favio Vázquez | Towards Data Science
• The Data Fabric for Machine Learning. Part 1-b: Deep Learning on Graphs. | by Favio Vázquez | Towards Data Science
• The Data Fabric for Machine Learning. Part 2: Building a Knowledge-Graph. | by Favio Vázquez | Towards Data Science
• 기계 학습 기초
• 통계 학습이란
• 기본적인 머신러닝과 딥러닝 강의
  • TensorFlow-Tutorials
  • Lec 00 - Machine/Deep learning 수업의 개요와 일정
  • Learning Machine Learning with TensorFlow
  • 머신러닝 목차
• 인공지능을 위한 머신러닝 알고리즘
• 기계학습의 발전 동향, 산업화 사례 및 활성화 정책 방향 – 딥러닝 기술을 중심으로
• 세상에 있는 (거의) 모든 머신러닝 문제 공략법
• Machine Learning - A New Programming Paradigm
• Traditional Programming versus Machine Learning, in One Picture
• software.intel.com/en-us/ai-academy/students/kits
• Machine learning by sogood
• 스탠포드 Cousera
  • class.coursera.org/ml-003/lecture
    • CourseraML
  • Stanford Machine Learning
  • repository for programming assignments of Machine Learning course, which is taught by Prof. Andrew Ng
  • Machine Learning Foundations : Regression ( 1. 이론)
  • Machine Learning Foundations 강의 노트 : Regression ( 2. 실습)
  • Machine Learning Foundations 강의 노트 : Classification ( 1. 이론)
  • Coursera Machine Learning으로 기계학습 배우기
    • week1
    • week1
    • week2
    • week3
    • week4
    • week5
    • week6
    • week7
• 기계 학습 이론 및 그 응용에 대한 소개: 예제를 포함한 시각적인 학습서
• Machine Learning Crash Course
  • Part 1
  • Part 2
  • Part 3
  • Part 4 - The Bias-Variance Dilemma
• Machine Learning From Scratch
  • Part 1 Attributes and patterns
  • Part 2 Collections and data
  • Part 3 Arrays and representations
  • Part 4 Functions and classification
• My Journey into Machine Learning
  • Class 1
  • Class 2
  • Class 3
  • Class 4
• Week 1 머신러닝? 선형 회귀, 비용함수는 뭐고 경사하강법은 무엇인가요?
• Week 2 다변량 선형회귀와 효과적인 경사하강법을 위한 방법들
• Week 3 로지스틱 회귀와 결정 경계, 그리고 오버피팅을 막기 위한 정칙화
• Week 4 신경망이란?
• Week 5 순전파와 역전파, 그리고 신경망 학습 프로세스
• Week 6 머신러닝 알고리즘을 효율적으로 다루기 위한 몇 가지 팁
• Data Science and Machine Learning with Python
• Machine Learning for Product Managers Part I — Problem Mapping
• Classifiers 간단 정리 ( Single, Hybrid, Ensemble)
• Gradient Boosting from scratch
  • Bagging; 서로 연관성이 없거나 사전지식이 없는 predictor들 여럿을 섞어서 결과를 내는 것. 제일 유명한 것이 random forest
  • Gradient boosting(GBM); 이전 predictor가 이후 predictor에 영향을 미치거나 관계를 알고 그 연쇄를 이용하면 보다 효율적인 결정을 할 수 있음
  • 각각 오버피팅과 variance/bias balance와 관련해 장단점이 있어서 도메인에 맞게 잘 선택하는 것이 중요
• Awesome Gradient Boosting Research Papers
• How to use GradientBoosting Classifier and Regressor in Python
• 친절한 그래디언트 부스팅A Gentle Introduction to Gradient Boosting | Metamath
• Noisy Gradient 다루기 | MakinaRocks Tech Blog
• Machine Learning Algorithms In Layman’s Terms, Part 1
• Google Developers Machine Learning Recipes with Josh Gordon
  • Hello World - Machine Learning Recipes #1
  • Visualizing a Decision Tree - Machine Learning Recipes #2
  • What Makes a Good Feature? - Machine Learning Recipes #3
  • Let’s Write a Pipeline - Machine Learning Recipes #4
  • Writing Our First Classifier - Machine Learning Recipes #5
  • Train an Image Classifier with TensorFlow for Poets - Machine Learning Recipes #6
  • Classifying Handwritten Digits with TF.Learn - Machine Learning Recipes #7
• Google Tutorial on Machine Learning
• Making Friends with ML Tutorial
• Mahcine Learning 101
• 엑셀의 분석도구를 이용한 간단한 선형 회귀분석 실습
• 머신러닝의 기초 - 선형 회귀 한 번에 제대로 이해하기 (30분만 투자해봐요!)
• My playlist – Top YouTube Videos on Machine Learning, Neural Network & Deep Learning
• 손고리즘ML
• 기계학습/머신러닝 기초(Machine Learning Basics)
• Setup Ubuntu 16.04 for ML Research
• Intro to Machine Learning
• A Tour of Machine Learning Algorithms
• A Tour of Machine Learning Algorithms
• machine learning a brief breakdown
• Essentials of Machine Learning Algorithms (with Python and R Codes)
• A Tour of The Top 10 Algorithms for Machine Learning Newbies
• 10 Machine Learning Algorithms You Should Know in 2018
• Ten Machine Learning Algorithms You Should Know to Become a Data Scientist
• TOP 10 Machine Learning Algorithms
• 101 Machine Learning Algorithms for Data Science with Cheat Sheets
• Do you know how to choose the right machine learning algorithm among 7 different types?(KR)
• Top 9 Algorithms for a Machine Learning Beginner
• 11 Most Common Machine Learning Algorithms Explained in a Nutshell | by Soner Yıldırım | Towards Data Science
• 10 Best Machine Learning Algorithms (2022) - Unite.AI
  • 최고의 머신러닝 알고리듬들 | GeekNews
• Machine Learning Resources for Beginners and Beginners++
• Simple ML explanations by MIT PhD students (ML-Tidbits)
• Machine Learning Tutorials
  • Machine Learning Tutorials
• 7 Free Machine Learning Courses
• A Visual Introduction to Machine Learning
  • Part 1
  • Part 2 Model Tuning and the Bias-Variance Tradeoff
• GitHub Special: Data Scientists to Follow & Best Tutorials on GitHub
• Machine Learning Tutorial Videos | Simplilearn
• 머피의 머신러닝 : Gaussian Processes
• 머피의 머신러닝 13 Sparse Linear Model
• Data-Analysis-and-Machine-Learning-Projects/example-data-science-notebook/Example Machine Learning Notebook.ipynb
• The Current State of Machine Intelligence
• deepdive.stanford.edu
  • 애플, ‘다크 데이터’ 처리 기업 인수···‘시리’ 더 똑똑해질까
• MACHINES human conversation about machine learning
• A First Encounter with Machine Learning
• 코넬 대학 강의
• Oxford Machine Learning 2014-2015
• 머신 러닝에 대해서 잘 정리해놓은 자료
• Understanding Machine Learning: From Theory to Algorithms
• Machine Learning & Computer Vision Talks
• deepcumen.com
• 오픈소스 matlab Octave
• Octave 기본 사용법
• 로보틱스와 머신 러닝/인공지능 무료 교재 추천 15권
• e커머스 위한 기계학습 기술, 오픈소스로 공개
• 베이지안 네트워크
• The State of Probabilistic Programming
• Deep Learning vs Probabilistic Graphical Models vs Logic
• Probabilistic machine learning and artificial intelligence
• Introduction To Probabilistic Modeling and Machine Learning
• pml-book | “Probabilistic Machine Learning” - a book series by Kevin Murphy
• 머신러닝(Machine Learning)과 확률(Probability)
• github.com/nyu-dl/Intro_to_ML_Lecture_Note
• Machine Learning Done Wrong
• UFLDL Tutorial
• Deep Learning Tutorials
• Machine learning basics
• How does a beginner best spend one week of learning machine learning?
• Machine Learning Done Wrong
• Emojineering Part 1: Machine Learning for Emoji Trends
• Great Innovative Idea- Machine Teaching
• 나는 머쉰이다
• 조지아테크 머쉰러닝
• bcho.tistory.com/category/빅데이타/머신러닝
  • 머신 러닝 프레임웍에 대한 간단 정리
• Machine Learning Wars: Amazon vs Google vs BigML vs PredicSis
• Machine Learning with Knowledge Graphs
• Machine-Learning Algorithm Calculates Fair Distance for a Race Between Usain Bolt and Long-Distance Runner Mo Farah
• Some Studies in Machine Learning Using the Game of Checkers
• How to Evaluate Machine Learning Models, Part 4: Hyperparameter Tuning
• How to Build a Machine Learning Model | by Chanin Nantasenamat | Jul, 2020 | Towards Data Science
• A Global Optimization Algorithm Worth Using
  • Global minimum을 찾기 위한 Hyperparmeter를 정하는 방법 5가지의 C++ library, python api
  • Guess and Check, Grid Search, Random Search, Bayesian Optimization, Local Optimization With a Good Initial Guess
• Hyper-parameters in Action! Part I — Activation Functions
• Importance of Choosing the Correct Hyper-parameters While Defining a Model
• 파라미터와 하이퍼 파라미터의 차이 | 프로그래밍 공부법 | 소프트웨어 개발자 | 데이터 분석가 | 머신러닝 - YouTube
• 내가 만든 모델이 잘 작동하는건가?
• High Performance Machine Learning through Codesign and Rooflining
• CHALLENGES TO MACHINE LEARNING: Relations between reality and appearance
• colah's blog
• ScalaML
  • 01 Getting Started
• MarI/O - Machine Learning for Video Games
• Information Theoretic Metrics for Multi-Class Predictor Evaluation
• 기계학습, 딥러닝, 그리고 모션분석
• 의사결정트리
• 의사결정RL 의사결정심리 + 강화학습 + 파이썬웹 + 온라인액션
• How do machines learn meaning?
• Algorithmic Trading of Futures via Machine Learning
• 데이터는 모든 것을 알고 있다
• 알고리즘 사회와 노동의 미래 (전체 요약)
• 알고리즘 사회, 새로운 사회계약이 필요하다 (강정수 발제 전문)
• 로봇, 인공지능 그리고 노동의 미래 (엄태웅 발제 전문)
• 알고리즘 사회와 노동의 미래 (패널 토론)
• Machines learn to play Tabla
• Machine Learning: The High-Interest Credit Card of Technical Debt
• Curse of Dimensionality
• Machine Learning
• Machine Learning: Zero to Hero
• An Introduction to Distributed Machine Learning
• Common Design for Distributed Machine Learning
• Why a Mathematician, Statistician, & Machine Learner Solve the Same Problem Differently
• The Mathematics of Machine Learning
• Hogwild
  • Parallel Machine Learning with Hogwild!
• Machine Learning and Cognitive Computing
• Cross-validation for detecting and preventing overfitting
• 18. 모델의 과최적화를 피하는 방법 (overfitting, regularization)
• Overcoming overfitting a model in Machine Learning | by Abhishek Shah | Jun, 2022 | Medium
• L2 Regularization
• #Python | Reduce Overfitting: 1) Model Capacity 2) Regularization 3) Dropout | #Regularization - YouTube
• Label Smoothing – Passion is like genius; a miracle
• Train/Test Split and Cross Validation in Python
• The Secret behind Train and Test Split in Machine Learning Process - Data Science Central
• Sergey Feldman: You Should Probably Be Doing Nested Cross-Validation | PyData Miami 2019
• 크로스 밸리데이션(cross validation, 교차 검증)의 개념, 의미 - 로스카츠의 AI 머신러닝
• 딥러닝 필수 파이썬 지식: 이미지 파일 확장자 검증
• More-flexible machine learning
• What to do with “small” data?
• 머신러닝 특집
  • 지금 왜 다시 딥러닝인가
  • 비즈니스가 머신러닝을 만났을 때...이시영 MS 수석 인터뷰
  • 강남역 3번 자판기가 일주일 뒤 고장납니다
  • 딥러닝, 인공지능의 르네상스를 이끌다
• Weak Learning, Boosting, and the AdaBoost algorithm
• What is the difference between Bagging and Boosting?
• 군중은 똑똑하다2 — AdaBoost
• How to use AdaBoost Classifier and Regressor in Python
• Clearing air around “Boosting” Understanding the current go-to algorithm for best results
• 머신 러닝의 기본적인 이해 : 뇌와의 비교
• IBM ‘왓슨’, 인지컴퓨팅 서비스로 업그레이드
• Machine Learning for indentify the author of an email
• 집단지성 프로그래밍 1(Machine Learning, Euclidean Distance, Pearson Correlation Coefficient)
• 연애에서 결혼까지 (기계학습에서 딥러닝까지) OneQ
  • 연애에서 결혼까지 (기계학습에서 딥러닝까지) OneQ
• The Three Cultures of Machine Learning
• github.com/ml-ko/ml
• Learning with Maximum Likelihood
• (ML 4.1) Maximum Likelihood Estimation (MLE) (part 1)
• 분포를 왜 추정하는걸까? MLE vs MAP - 분포를 추정해서 손글씨를 만들어보자(1)
• Is Maximum Likelihood Useful for Representation Learning?
• Minimizing the Negative Log-Likelihood, in Korean
  • (1)
  • (2)
  • (3)
• topdata.news
• www.rightrelevance.com
• SigOpt for ML: Unsupervised Learning with Even Less Supervision Using Bayesian Optimization
• A Conceptual Explanation of Bayesian Hyperparameter Optimization for Machine Learning
• Machine Learning - Bayesian Learning
• NDC 2016 김정주 - 기계학습을 활용한 게임 어뷰징 검출
• Spring XD: The Foundation for Real-time Streaming and Machine Learning Systems
• Introduction to streaming for data scientists
• 로보어드바이져를 개발하면서 알게 된 것들 1 – 알고리즘에 대한 환상
• Principal Components Regression, Pt.1: The Standard Method
• Machine Learning - Authors and titles for recent submissions
• Stock inference engine using Spring XD, Apache Geode / GemFire and Spark ML Lib. http://pivotal-open-source-hub.github.io/StockInference-Spark
• github.com/showcases/machine-learning
• 머신 러닝
• 머신러닝 Class
• Machine Learning Tutorial
• Logistic Regression Tutorial
• Logistic regression
• Logistic Regression
• Solving Logistic Regression with Newton's Method
• Deployment of Web App + ML Model + APIs — Tutorial - Learn how to deploy a web app served by a Logistic Regression Model. We will create an app to predict the probability of a Data Scientist earn more than USD 100k per year
• Putting Machine Learning Models into Production
• Building A Logistic Regression in Python, Step by Step
• Logistic Regression — Detailed Overview
• Logistic Regression from scratch - Philipp Muens
• 머신러닝 - 로지스틱 리그레션(Logistic Regression) - YouTube
• 로지스틱회귀 Logistic Regression - 시그모이드 함수
• Multiclass Regression Tutorial
• 5 Types of Regression and their properties
• Simple and Multiple Linear Regression in Python
• Simple Linear Regression Tutorial for Machine Learning (ML) | by Towards AI Team | Towards AI | Medium
• Drawing the Multiple Regression Models
• multi-linear-regression
  • Multiple Regression Analysis in Python | Part 1 데이터 로딩 및 클리닝. 모델의 일반적인 개념
  • Multiple Regression Analysis in Python | Part 2 모델의 가정, 다중공선성 확인. Variance Inflation factor 다중공선성 확인 & 제거
  • Multiple Regression Analysis in Python | Part 3 이상점 확인, 다중 회귀 분석 모델에 미치는 영향
  • Multiple Regression Analysis in Python | Part 4 모델 진단 및 모델 가정 검증
  • Multiple Regression Analysis in Python | Part 5 모델 구축 및 활용
• Linear Regression — Python Implementation
• How to Build a Linear Regression Model in Python
  • Part 1
  • Part 2
  • Part 3
  • Part 4
• Proof of the F Test for Linear Regression
• ML From Scratch, Part 1: Linear Regression
• 선형회귀 Linear Regression - 최소제곱법, 평균제곱오차, 경사하강법
• 6 TYPES OF REGRESSIONS, DID YOU KNOW?
• Fully Explained PolyFit Method for Machine Learning Predictions | by Amit Chauhan | Level Up Coding
• Python, Machine Learning, and Language Wars
• AI, Deep Learning, and Machine Learning: A Primer
• 만약 타이타닉에 내가 탔더라면 생존할 수 있었을까?
• Approaching (Almost) Any Machine Learning Problem | Abhishek Thakur
• Generate Rap Lyrics - Fresh Machine Learning #4
• Approaching (Almost) Any Machine Learning Problem
• 정보 이론: Information Theory 1편
• 정보 이론 2편: KL-Divergence
• 가장 쉬운 KL Divergence 완전정복!
• 크로스 엔트로피와 KL 발산에 대하여 | LinkedIn
• 초보를 위한 정보이론 안내서 - Entropy란 무엇일까
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  • 5. Convolutional Neural Network (CNN)
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• Using Machine Learning to Predict Value of Homes On Airbnb | by Robert Chang | Airbnb Engineering & Data Science | Medium 에어비엔비에서의 가격 예측
• Using Machine Learning to Improve Streaming Quality at Netflix | by Netflix Technology Blog | Netflix TechBlog 넷플릭스에서 스트리밍 퀄리티 실시간 개선
• How we grew from 0 to 4 million women on our fashion app, with a vertical machine learning approach | by Gabriel Aldamiz... | HackerNoon.com | Medium 패션 앱에서 소비자 경험 개선
• Tuning Model Performance | Uber Engineering Blog
• 9 Deadly Sins of Dataset Selection in ML | by Sandeep Uttamchandani | Jun, 2021 | Towards Data Science
• Real Estate pricing with Machine Learning & non-traditional data sources | Tryolabs Blog
• Bootstrapping Labels via ___ Supervision & Human-In-The-Loop active learning
• 머신러닝과 정보이론: 작동원리의 이해 – 과학의 지평
• 실험없이 이벤트 효과를 추정할 수 있을까? - DIFFERENCE IN DIFFERENCES
• Spotify의 오디오 분석을 사용한 일렉트로닉 댄스 음악의 장르 분류
• Cracking Random Number Generators using Machine Learning – Part 1: xorshift128 – NCC Group Research
• A Tour of End-to-End Machine Learning Platforms | Ian Hellström
• 머신러닝 모델로 동네생활 신고 업무 자동화하기. 2021년에 동네생활 글을 필터링(신고)하는 새로운 모델을 개발하고… | by Jaeyoon Chun(Aio) | 당근마켓 팀블로그 | Jan, 2022 | Medium
• Why 90 percent of all machine learning models never make it into production | by Ari Joury | Towards Data Science
• 이진 분류를 위한 머신러닝 예측 성능 비교 - 로지스틱 회귀, 랜덤포레스트, 부스팅, 신경망
• 구글 리서치(제프 딘)의 미래 ML 테마들 | GeekNews
• The First Rule of Machine Learning: Start without Machine Learning
• Robust Foundation for Data Pipelines at Scale - Lessons from Netflix - YouTube
• Agile Data Science - How to Implement Agile Workflows For Analytics and Machine Learning - YouTube
• 머신러닝, 딥러닝 대체 뭘 공부하라는거야?
• Machine Learning is Still Too Hard for Software Engineers – Nyckel – Add custom machine learning to your application in minutes
• Two Types of Information Leakage In ML Systems
• Real-time machine learning: challenges and solutions
• PolyCoder is an open source AI code-generator that researchers claim trumps Codex | VentureBeat
• How to Improve Any ML/DL Performance by 10% Easily | by Gurami Keretchashvili | Apr, 2022 | Towards Data Science
• 멘토특강 클라우드 Big Data & Machine Learning 기초 - YouTube
• Evolution of ML Fact Store. by Vivek Kaushal | by Netflix Technology Blog | Apr, 2022 | Netflix TechBlog
• Supporting Diverse ML Systems : Netflix Tech Blog | Netflix TechBlog
• Fraud Detection Using Random Forest, Neural Autoencoder, and Isolation Forest Techniques
• 머신러닝 어플리케이션을 위한 데이터 저장소 기술 | Hyperconnect Tech Blog
• What is SciML? – SciML Research Group
• SHIELD: LINE VOOM을 어뷰저로부터 보호하기
• Data Round Table - YouTube
• ML Latency No More. Common Ways to Reduce ML Prediction Latency. | by Moussa Taifi PhD | Apr, 2022 | Medium | Towards Data Science
  • ML Latency No More- Useful Patterns to Reduce ML Prediction Latency to Sub X ms | PyData NYC 2022 - YouTube
• Vincent Warmerdam: Winning with Simple, even Linear, Models | PyData London 2018 - YouTube
• 기계 학습 모델을 활용한 물류 입고 프로세스 최적화. 쿠팡 풀필먼트 센터로 제품 입고 시 필요한 운송 트럭의 적정 수량을… | by 쿠팡 엔지니어링 | Coupang Engineering Blog | Mar, 2023 | Medium
• 쿠팡이 '기계학습'으로 물류 입고 프로세스 개선한 방법 | 요즘IT
• Self paced learning in ML – Passion is like genius; a miracle.
• 머신러닝 규칙:  |  Machine Learning  |  Google for Developers
• Practical advice for analysis of large, complex data sets
  • 복잡한 대규모 데이터 세트의 분석에 대한 실무 조언
• 확실히 알아두면 만사가 편해지는 머신러닝 10가지 알고리즘 | 요즘IT
• Back to Basics: Scalable, Portable ML in Pure SQL - YouTube
• 모델 평가하는 방법(혼동행렬, confusion matrix, 특이도, 민감도, 재현율, 정확도, 정밀도)
• ML 예측 결과를 다운스트림 애플리케이션에 사용할 수 있을까?
• How Google Maps quietly allocates survival across London’s restaurants - and how I built a dashboard to see through it
  • 런던의 식당 생태계를 조용히 재편하는 구글 지도, 그리고 그 구조를 들여다 본 대시보드 만 | GeekNews

Amazon AWS

• Amazon Machine Learning
  • Amazon Machine Learning – Make Data-Driven Decisions at Scale
  • AWS, 머신러닝 서비스 출시
• 아마존 웹 서비스를 다루는 기술
• Experimenting with AWS Machine Learning for Classification
• 데이터에 숨은 통찰력을 발견하라
• mlstudy2019 2019년 머신러닝 스터디 모임
• Develop and sell a Machine Learning app — from start to end tutorial | by Daniel Deutsch | Sep, 2020 | Towards Data Science

AutoML

• Io17ex automl & autodraw
• Introducing AdaNet: Fast and Flexible AutoML with Learning Guarantees
• AdaNet 소개: 학습 보장을 이용한 빠르고 유연한 AutoML
• An Opinionated Introduction to AutoML and Neural Architecture Search
• AutoML Automatic Machine Learning Challenge & Lessons
• AutoML
• How to Beat Google’s AutoML - Hyperparameter Optimisation with Flair
• automl-gs Provide an input CSV and a target field to predict, generate a model + code to run it
• 이미지 분류 모델 AutoML 파이프라인. 이미지 분류 모델 AutoML 활용하기 | by Yoo Gyoungyoon | 당근마켓 팀블로그 | Dec, 2020 | Medium
• Auto ML in Python — An Overview of the MLBox Package | by Ahmed Besbes | Apr, 2022 | Towards Data Science
• Continuous-Adaptation-with-VertexAI-AutoML-Pipeline
• evalml: EvalML is an AutoML library written in python
  • Home — EvalML 0.23.0 documentation
• mindsdb - Open Source AutoML for Developers
  • mindsdb: In-Database Machine Learning

Book

• 패턴 인식 - 오일석
• 머신러닝에서 딥러닝까지
• Machine Learning 강의노트
• 머신러닝/딥러닝 실전 입문
• 모두를 위한 컨벡스 최적화
• 더북(TheBook): 머신 러닝 워크북 4~8장만
• 더북(TheBook): 머신 러닝을 다루는 기술 with 파이썬, 사이킷런 1~5장만
• 더북(TheBook): 머신 러닝을 위한 수학 with 파이썬, R
• 더북(TheBook): 머신 러닝 교과서 with 파이썬, 사이킷런, 텐서플로 1~4장만
• 5 EBooks to Read Before Getting into A Machine Learning Career
• 12 Best Free Ebooks for Machine Learning
• 16 Free Machine Learning Books
• 10 Free Machine Learning books
• 10 Free Must-Read Books for Machine Learning and Data Science
• List of Free Must-Read Books for Machine Learning
• List of Free Must-Read Machine Learning Books
• List of Free Must-Read Machine Learning Books
• Subsets of Machine Learning Cookbook
• 50 external machine learning / data science resources and articles
• A high-bias, low-variance introduction to Machine Learning for physicists
• 자료 모음 1
• 자료 모음 2
• 24 Best (and Free) Books To Understand Machine Learning
• 9 Best Machine Learning & Artificial Intelligence Books To Read In 2020
• 존헐 교수 머신러닝 인 비지니스
  • www-2.rotman.utoronto.ca/~hull/Second edition Online Files/index2ndEd.html
• 책 & 강의 직접 보고 추천하는 머신러닝 & 딥러닝 & 관련 수학 총.정.리.
• Best Machine Learning Books — Free and Paid — Editorial Recommendations | Towards AI
• A Beginner Mathematics Book For Machine Learning
• A Comprehensive Guide to Machine Learning (Berkeley University) - Data Science Central
• CS229T/STAT231: Statistical Learning Theory (Winter 2015)
• Deep Learning
• dmls-book: Companion repo for Designing Machine Learning Systems by Chip Huyen
• Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition
• Interpretable Machine Learning - A Guide for Making Black Box Models Explainable
  • iml: Interpretable Machine Learning r package
• Machine Learning: a Probabilistic Perspective
  • 캐빈머피 머신러닝 Kevin Murphy Machine Learning Statistic
• Machine Learning by Abdelhamid Mellouk and Abdennacer Chebira
• Machine Learning Engineering book by Andriy Burkov
• Machine Learning Systems 1400페이지에 이르는 방대한 양, machine learning 영역이 넓어지며 system 구축이 중요한 일이 되었는데, 이 분야에 대한 책
  • cs249r_book: Introduction to Machine Learning Systems
• Machine Learning with TensorFlow
  • Machine Learning with TensorFlow
• 'Machine Learning Yearning' 책의 한국어 번역
• Math for Machine Learning
• Mathematics for Machine Learning
  • mml-book.github.io
• Mathematical Foundations of Machine Learning
  • mathTalent - YouTube
• Model-Based Machine Learning
• PRML(Pattern Recognition & Machien Learning, Bishop) 정리
  • Pattern Recognition and Machine Learning
  • PRML Python codes implementing algorithms described in Bishop's book "Pattern Recognition and Machine Learning"
• Probabilistic Machine Learning: Advanced Topics
• Python
  • Python Data Science Handbook github
    • IPython: Beyond Normal Python
    • Introduction to NumPy
    • Data Manipulation with Pandas
    • Visualization with Matplotlib
    • Machine Learning
  • Python Machine Learning book code repository
  • Open Content for self-directed learning in data science
  • machine_learning - a collection of IPython notebooks detailing various machine learning algorithims
  • Meetup-Fall-2013 - Slides, code, and other information relating to the Fall 2013 Meetups
  • Practical Machine Learning with Python
  • The "Python Machine Learning" book code repository and info resource
• Understanding machine learning from theory to algorithms

C++

• mlpack: a scalable C++ machine learning library
  • Mlpack 3.0 Released: A Fast, Flexible Machine Learning Library

Classify

• Machine Learning classifier comparison
• Choosing a Machine Learning Classifier
• Online Category-learning and Classification
• Naive Bayes classification 알고리즘에 대한 하호진님의 글
• bench-ml - Simple/limited/incomplete benchmark for scalability/speed and accuracy of machine learning libraries for classification
• Free Code Friday - Better and Faster Machine Learning Classifiers in Python
• Solving A Simple Classification Problem with Python — Fruits Lovers’ Edition
• Types of classification algorithms in Machine Learning
• ML Practicum: Image Classification
• Music Genre Classification with Python A Guide to analysing Audio/Music signals in Python
• Machine Learning Classifier evaluation using ROC and CAP Curves sklearn
• 머신러닝을 활용한 제품 카테고리 분류하기
• Smarter Ways to Encode Categorical Data for Machine Learning
• Classification & Clustering 모델 평가
• Python Voting Classifiers 다수결 분류의 정의와 구현
• Machine Learning - four layer 1000 neuron bidiectional lstm classifier - YouTube
• Topaz Gilad - Classification Through Regression:Unlock the True Potential of Your Labels - YouTube
• dirty_cat: machine learning on dirty categories — Dirty cat
  • Dirty categories: learning with non normalized strings — Dirty data science
• Lime: Explaining the predictions of any machine learning classifier

Clojure

• Clojure Machine Learning, Math & Statistical Libraries Collection

Clustering

• Clustering Based Unsupervised Learning
• Three Popular Clustering Methods and When to Use Each
• Assessment Metrics for Clustering Algorithms
• Fantastic drivers and how to find them
• Grouping Soccer Players with Similar Skillsets in FIFA 20 | Part 2: Hierarchical Clustering - YouTube
• Feature Clustering: A Simple Solution to Many Machine Learning Problems - Machine Learning Techniques

Clustering DBSCAN

• DBSCAN (밀도 기반 클러스터링)
• 밀도기반 클러스터링 - DBSCAN
• Understanding DBSCAN Algorithm and Implementation from Scratch

Clustering K-means

• Finding Topics in Harry Potter using K-Means Clustering
• k-means++ Silhouettes
• Density-Based Clustering
• Machine Learning 스터디 (13) Clustering (K-means, Gaussian Mixture Model)
• K-means 알고리즘 구현하기
• Hands-On with Unsupervised Learning
• Understanding K-means Clustering in Machine Learning
• Introduction to K-Means Clustering | Pinecone
• K-means Clustering From Scratch in Python Beginner Tutorial - YouTube

Clustering Python

• RFMT Segmentation Using K-Means Clustering
• 10 Clustering Algorithms With Python
• K-Means Python script to Flask application (step by step) - YouTube
• OpenFace - This is a Python and Torch implementation of the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google using publicly available libraries and datasets
  • openface

Conference

• Lablup Conf 1st - YouTube
• O'reilly Strata Data Conference New York 2019 후기
• Deploying ML in an Old-fashioned Heavy Industry - Robert Meyer | PyData Global 2021 - YouTube
• Machine Learning for Developer Self-Care • Erik Meijer • GOTO 2021 - YouTube
• Reactive Machine Learning & Functional Programming • Jeffrey Smith • YOW! 2015 - YouTube
• Dean Pleban - ML in Production–What does “Production” even mean? | PyData Global 2022 - YouTube
• (1) Alejandro Saucedo - Metadata Systems for End-to-End Data & Machine Learning | PyData Global 2022 - YouTube
• The Importance of Forgetting for Both Humans & Machines • Robert Crowe • GOTO 2022 - YouTube
• Teddy Lazebnik: Admiral-Driven ML Framework for Marine Operations (HE) | PyData Tel Aviv 2025 - YouTube 결국 RL
  • 해군 작전을 위한 Admiral 기반 머신러닝 프레임워크 | PyData Tel Aviv 2025

Course MOOC Lecture

• 머신러닝 괜찮은 강의들 리스트 1
• 머신러닝 딥러닝 바로가기
• 인공지능과 기계학습
• (인공지능, 기계학습, 머신러닝) KOOC 인공지능및기계학습개론 문일철교수님
• 하버드, 스탠포드, AI 온라인 강의 쏟아진다
• (기계학습,인공지능,머신러닝) 한국어 기계 학습 강좌 심화 1
• 머신러닝(기계학습) 이해하기 scikit-learn
• 아빠가 들려주는 머신 러닝
  • 다양한 머신러닝 방법들
• 포브스가 추천하는 AIㆍML 무료 강좌 '베스트 9'
• 머신러닝1 - 생활코딩
• 동영상 강좌 파이썬 데이터과학, 머신러닝∙딥러닝
• Every single Machine Learning course on the internet, ranked by your reviews
• Machine Learning Video Library
• A List Of Top 10 Free Machine Learning Online Courses and Tutorials
• 10 Best Online Courses for Machine Learning with Python-Bestseller 2021
• 20 Best Online Courses On Machine Learning You Must Know in 2021
• 60 Best Free Online Courses for Machine Learning & AI in 2021
• 8 Best Machine Learning Courses for Finance You Must Know- 2021
• 12 Best Online Courses for Machine Learning with Python-Bestseller 2022
• 45 Udacity FREE Courses on Machine Learning and Data Science- 2022
• Machine Learning Summer School 2017 Tübingen
• Best Coursera Free Courses Machine Learning for Everyone in 2022
• Top Python Libraries for Data Science, Data Visualization & Machine Learning - KDnuggets
• Be Future Ready | Day - 1 | AI And Machine Learning Full Course | AI & ML Tutorial | Simplilearn - YouTube
• Andrew Ng / Coursera
  • Machine Learning Exercises In Python, Part 1 python exercises of Andrew Ng's maching learning class on Coursera
  • Python programming assignments for Machine Learning by Prof. Andrew Ng in Coursera
  • Python Implementation of Andrew Ng’s Machine Learning Course
    • (Part 1)
    • (Part 2.1)
    • (Part 2.2)
    • A Guide to using Logistic Regression for Digit Recognition (with Python codes)
    • Learn how to Build Neural Networks from Scratch in Python for Digit Recognition
  • Machine Learning 정리
• Best Coursera Free Courses Machine Learning for Everyone in 2021
• Learning from Data - Yaser S. Abu-MostafaYaser S. Abu-Mostafa Machine Learning course - recorded at a live broadcast from Caltech
• 6 Best Machine Learning and Data Interview Courses for Experienced Professionals | by javinpaul | Javarevisited | Sep, 2022 | Medium
• CS7641 at Georgia Tech / Udacity
• CS156 at Caltech
• CS3244 Machine Learning Tutorial Material in python
• CS 329S | Syllabus
  • Stanford MLSys Seminars - YouTube
• sentdex, Machine Learning with Python
• Deep Learning TV
• Demis Hassabis, CEO, DeepMind Technologies - The Theory of Everything
• Accelerating Understanding: Deep Learning, Intelligent Applications, and GPUs
• Sirajology
• Two+ Minute papers
• geoffrey hinton
• Machine Learning Engineer Nanodegree by google
• Learn Machine learning online – List of machine learning courses available online
• PhD level ML Course
• Course on Information Theory, Pattern Recognition, and Neural Networks
• machinelearningmastery.com/start-here
• What are Some ‘Advanced ‘ AI and Machine Learning Online Courses?
• Top 5 Machine Learning Courses for 2019
• A practical approach to machine learning
• 10 Free Top Notch Machine Learning Courses
• Emil’s Story as a Self-Taught AI Researcher
• 머신러닝/딥러닝 공부 첫걸음 :: 시작은 미약한 프로그래밍
  • 머신러닝 입문 영상 - YouTube
  • The Simplest Introduction to Basic Machine Learning
• How I am learning machine learning Series' Articles - DEV Community 👩💻👨💻
• Learn Machine Learning: - machinelearning.to
• Machine learning course: the overfitting-underfitting tradeoff in a model that generalizes - YouTube
• Machine Learning Full Course | Learn Machine Learning | Machine Learning Tutorial | Simplilearn - YouTube
• Machine Learning Fundamentals - YouTube
• Machine Learning University - YouTube
• ML-For-Beginners: 12 weeks, 24 lessons, classic Machine Learning for all
• (ML Lecture 01) Regression Case Study
• ML-YouTube-Courses: A repository to index and organize the latest machine learning courses found on YouTube
• Best Machine Learning Courses for Finance You Must Know in 2021
• Best Resources to Learn Machine Learning Online in 2021
• 6 Best Websites to learn Data Science and Machine Learning in 2022 | by javinpaul | Javarevisited | Feb, 2022 | Medium
• 10 Best Advanced Machine Learning Courses You Must Know in 2022
• learning-machine: A handbook for ML built on answers
  • A handbook for machine learning with python
• scikit-learn MOOC

C#

• C# 으로 Machine Learning ML.net - 1

Feature Selection

• Understanding Feature Engineering
  • (Part 1) — Continuous Numeric Data Strategies for working with continuous, numerical data
  • (Part 2) — Categorical Data Strategies for working with discrete, categorical data
  • (Part 3) — Traditional Methods for Text Data Traditional strategies for taming unstructured, textual data
  • (Part 4) — Deep Learning Methods for Text Data Newer, advanced strategies for taming unstructured, textual data
  • feature engineering text data
• Automated Feature Engineering in Python
• Fundamental Techniques of Feature Engineering for Machine Learning
• 피처 엔지니어링 #1 - 피처 크로스
• Automated Feature Engineering for Predictive Modeling
• Machine Learning의 Feature Store란?
• Feature Store - why?
• Feature store - 핵심 개념
• Feature store - Basic Architecture
• Feature Crossing
• Feature Selection Technical Report
  • Feature Selection: A Data Perspective
  • Feature Selection for Classification: A Review
• What is Dim Reduction & Feature Selection
  • 차원축소와 특징선택이란 무엇인가
• [A Feature Selection Tool for Machine Learning in Python](A Feature Selection Tool for Machine Learning in Python)
• Effective Data Preprocessing and Feature Engineering
• 추천 시스템의 심장, Feature Store 이야기 (1)—혼란 속의 질서 찾기: Feature Store를 구축하다 | by mingrammer | 당근 테크 블로그 | Aug, 2024 | Medium
• Data Feast — A Highly Scalable Feature Store at Dream11 | by Dream11 Engineering | Feb, 2022 | Dream11 Engineering
• 토스가 다양한 ML 모델을 만드는 법: Feature Store & Trainkit
• feast - Feature Store for Machine Learning
  • Introducing Feast: an open source feature store for machine learning
• feathr: Feathr – An Enterprise-Grade, High Performance Feature Store
  • Open sourcing Feathr – LinkedIn’s feature store for productive machine learning | LinkedIn Engineering
• mRMR (minimum-Redundancy-Maximum-Relevance) for automatic feature selection at scale

Go

• Gorgonia
• Goro: A High-level Machine Learning Library for Go
• Real-time Machine Learning in Go (for smart energy)
  • Goml
  • Golearn
  • Others
  • Tensorflow
  • Caffe
• Machine Learning and Go

Google

• ICML 2015 and Machine Learning Research at Google
  • ICML 2015 Papers
  • Brief Summary of the Panel Discussion at DL Workshop @ICML 2015
• Two big challenges in machine learning
• A Real World Use Case of Google Machine Learning APIs - Hyperconnect
• 구글의 머신러닝 비전: TPU부터 모바일까지 (Google I/O Extended Seoul 2017)
• 구글 머신러닝 스터디잼 가이드라인 Qwik Lab
• 구글 클라우드 스터디잼 (심화) 가이드라인

Google Prediction

• Learn from your data Make real-time predictions Online machine learning system. Build learning models and predict future values
• Build a Prediction Engine Using Spark, Kudu, and Impala
• An Incomplete list of Market-Prediction Projects in Andrew Ng's CS229 Course
• cloud.google.com/prediction

Java

• ML in Java, YES it's possible! by Mohammed Aboullaite - YouTube
• Java Machine Learning Library
• SystemML
• Weka 3 - Data Mining with Open Source Machine Learning Software in Java

JavaScript

• 11 Javascript Machine Learning Libraries To Use In Your App

Kaggle

• Kaggle: Your Home for Data Science
• A simple model for Kaggle Bike Sharing
• KaggleBreak/problems
• Part 1: For Beginners - Bag of Words 캐글뽀개기 6월 이상열
• KAGGLE ENSEMBLING GUIDE
• My Kaggle Learning Curve: Artificial Stupidity
• Bag of Words Meet Bags of Popcorn - (1) Part 1: Bag of Words
• DRIVENDATA Competitions
• Kaggle Solution: What’s Cooking ? (Text Mining Competition)
• 스터디 뽀개기 2015년
• 캐글즐기기 (2017년 새시작)
• Winning Tips on Machine Learning Competitions by Kazanova, Current Kaggle #3
• How to Win Machine Learning Competitions ?
• Kaggle Data Science Bowl 2017 참가기
• Deeplunch팀의 Kaggle Data Science Bowl 도전기(1) - 케글 도전 팁
• 2nd place solution for the 2017 national datascience bowl
  • Kaggle national datascience bowl 2017 2nd place code
  • 2nd Place Solution To the 2017 National Data Science Bowl
• KaggleZeroToAll
• [Kaggle Past Solutions(http://ndres.me/kaggle-past-solutions/)
• First Exploratory Analysis
• First Exploratory Analysis (2)
• A Kaggler's Guide to Model Stacking in Practice
  • Stacking(메타 앙상블이라고도 함)은 여러 모델의 정보를 결합하여 새로운 모델을 생성하는 데 사용되는 모델 앙상블 기술
    • 종종 누적 모델 (2차 모델이라고도 함)은 매끄러운 성격과 성능이 가장 좋은 각 기본 모델을 강조 표시하고 성능이 좋지 않은 각 기본 모델의 신뢰도를 떨어 뜨리기 때문에 각 개별 모델의 성능을 능가
    • 이러한 이유로 스태킹은 기본 모델이 크게 다를 때 가장 효과적
  • 간단한 예제와 스태킹이 실제로 구현되는 방법에 대한 가이드를 제공
  • 네명이 보드에 187개의 다트를 던진다고 가정
    • 150개의 다트점을 우리는 누가 다트를 던졌는지, 어디에 도착했는지 볼 수 있습니다
    • 나머지의 경우, 우리는 다트가 도착한 곳만 볼 수 있습니다
    • 우리의 임무는 착륙 지점을 기준으로 각 레이블이없는 다트를 던진 사람을 추측
  • K-Nearest Neighbors (Base Model1)
  • Support Vector Machine (Base Model2)
  • Stacking (Meta Ensembling)
    • Stacked Model Hyper Parameter Tuning
    • Stacking Model Selection and Features
  • Stacking in Practice
  • Classify Dart Throwers
• Kaggle Python Tutorial on Machine Learning
• The Nature Conservancy Fisheries Monitoring Competition, 1st Place Winner's Interview: Team 'Towards Robust-Optimal Learning of Learning'
• PYCON KOREA 2017 Python 입문자의 Data Science(Kaggle) 도전
  • pycon_korea_2017_Kaggle_tutorial.ipynb
  • pycon_korea_2017_aws_tutorial.ipynb
  • ‘얼리또라이’의 데이터 공부 도전기
• grt123's winning submission for the Kaggle Data Science Bowl 2017
• mlchallenge2017.com
• 캐글 (Kaggle) 소개 - 데이터 과학 (머신러닝) 실전 예제 다루기
• 데이터 보는 마케터가 꼭 해봐야 할 캐글 경진대회
• Who is the "Kaggler"? Let's see them with survey!
• Kaggle-knowhow
• 캐글 뽀개기
• For Beginner To Learn Basic Statistical Analysis
• Item Condition & Brand Effect to Price
• Ideas for 9th Kaggle TensorFlow Speech Recognition Challenge
• Machine Learning Zero-to-Hero: Everything you need in order to compete on Kaggle for the first time, step-by-step!
• Feature Launch: Kaggle API
• 캐글 공식 API를 통해 다양한 머신러닝/딥러닝 데이터셋을 내려받고 서브미션 파일을 제출하는 과정을 알아봅니다
• 자전거 수요 예측
  • 1 캐글 머신러닝 탐색적 데이터 분석
  • 2 캐글 머신러닝 사이킷런 랜덤포레스트 RMSLE 소개
  • 3 캐글 머신러닝 랜덤포레스트만으로 경진대회에 참여하기
  • 4 캐글 머신러닝 선형회귀, 릿지, 라쏘, 랜덤포레스트, 그라디언트 부스팅 모델을 사용하여 상위 5% 점수얻기
• 오늘의 캐글
• Home Depot Product Search Relevance, 승자의 논평: 1등 | Alex, Andreas, & Nurlan
• Creating your AI projects on Kaggle
• How I got in the top 1 % on Kaggle
• Deep Learning Practitioner의 캐글 2회 참가기
• Pycon korea 2018 kaggle tutorial(kaggle break)
• 1.Titanic_EDA_And_Baseline_Model
• 3.Titanic_Model_Tuning_Final
• TITANIC: MACHINE LEARNING FROM DISASTER (THE KAGGLE CHALLENGE)
  • Titanic - The most famous competition over the kaggle
  • Titanic machine learning from disaster solution: (The Kaggle Challange)
• Introduction to Decision Trees (Titanic dataset)
• Decision Trees in Machine Learning (ML) with Python Tutorial | by Towards AI Team | Towards AI | Nov, 2020 | Medium
• Know How to Create and Visualize a Decision Tree with Python
• 트리 모델 | Pega Devlog
• Tabular data는 딥러닝보다 트리 모델
  • 2110.01889.pdf
• Start Here: A Gentle Introduction
• A Complete Introduction and Walkthrough
• Work like a Pro with Pipelines and Feature Unions
• Kaggle_NLP_Song
• Why Kaggle will NOT make you a great data-scientist
• I did a Kaggle competition as a semester project at uni. Here’s what I learned
• Feature Selection with Null Importances(KR)
• DataBreak2018: Hello Kaggler -Keynote2 (이정윤 님, data scientist, Microsoft)
• 사이먼킴의 데이터과학 1화 - 데이터 살펴보기
• 사이먼킴의 데이터과학 2화 - 데이터 살펴보기(2부)
• 실습으로 배우는 데이터 사이언스
• 30분이면 하는 캐글 대회 제출 (데이터사이언스 일단 따라하기) 튜토리얼
• 타이타닉 튜토리얼 - Exploratory data analysis, visualization, and machine learning ipynb
• 수비니움의 캐글 따라하기 1강. 타이타닉 (1)
• 수비니움의 kaggle 따라하기 Titanic 2강 - YouTube
• 수비니움의 kaggle 따라하기 Titanic 3강 - YouTube
• 캐글 타이타닉 따라해보기 #1
• 캐글 타이타닉 따라해보기 #2
• 캐글 타이타닉1 - 생존자 예측 모델 만들기 - RomanticQ의 머신러닝
• 캐글 타이타닉2 - 모델 튜닝을 통한 점수 높이기 - RomanticQ의 머신러닝
• 캐글 타이타닉3 - 핸즈온 머신러닝 챕터2 적용 - RomanticQ의 머신러닝
• 캐글 타이타닉4 - 핸즈온 머신러닝 챕터6 적용 - RomanticQ의 머신러닝
• 캐글 타이타닉5 - 핸즈온 머신러닝 챕터7 적용 - RomanticQ의 머신러닝
• 박피디의 게임 개발 이야기 : Python 으로 타이타닉 승객의 생존율 구하기
• Kaggle Winning Solutions - Sortable and searchable compilation of solutions to past Kaggle competitions
• Better Models Faster with Weights & Biases
  • Why Weights & Biases Gives You An Edge On Kaggle Competitions
• 캐글 대회 메달따는 팁. 2. Stochastic weight averaging
• If I had to start learning Data Science again, how would I do it?
• 데이터 과학자의 놀이터이자 배움터··· ‘캐글’ 탐험 안내서 - CIO Korea
• Google Research Football with Manchester City F.C. | Kaggle
• Hello-Kaggle: Kaggle을 처음 접하는 사람들을 위한 문서
• 캐글 넷플릭스(netflix) 데이터를 이용한 데이터 분석 실습 (1)
• 캐글 넷플릭스(netflix) 데이터를 이용한 데이터 분석 실습 (2)
• 캐글 데이터 시각화 넷플릭스(netflix) 데이터를 이용한 데이터 분석 실습 (3)
• 영상 캐글 4그랜드마스터 데이콘 초청 한글 Abhishek Webinar2 Q/A - DACON
• How to break a model in 20 days. A tutorial on production model analytics
• Shopee - Price Match Guarantee | Kaggle
• 캐글 집 가격 예측 대회 탐색적 데이터 분석 - 캐글 스터디 2기 - YouTube
• Upstage Talks 학부 문과생이 세계 랭킹 24위 캐글 그랜드 마스터가 되기까지 - YouTube
• 2020 캐글 설문조사 분석 - YouTube
• Kaggle 30 Days of ML Learning - YouTube
• AI Factory
• kaggle-study: TIA(Today I Analyzed) - Data Analysis, Kaggle Study
• morty: 🔬 Lightweight experiment and configuration manager for small ML/DL projects and Kaggling
• Numerai

K nearest neighbors

• Learn By Implementation – K-Nearest Neighbor
• KNN 추천엔진을 만들자!
• Tutorial To Implement k-Nearest Neighbors in Python From Scratch
• K Nearest Neighbors Application - Practical Machine Learning Tutorial with Python p.14
• 머신러닝 개념 소개 및 kNN 알고리즘 소개
• 파이썬 코딩으로 말하는 데이터 분석 - 9. k-NN (최근접이웃,분류문제)
• k-nearest neighbor algorithm using Python
• A Simple Guide to K-Nearest Neighbor Classification using Python
• Join Us: An Introduction to Using k-NN in Production
• A Summary of Using k-NN in Production
• Introduction to k-Nearest-Neighbors
• Machine Learning Basics with the K-Nearest Neighbors Algorithm
• k-Nearest Neighbors: Who are close to you?
• Simplified KNN Algorithm using Python with coding explanation | by Yogesh Chauhan | Towards Data Science
• ML from Scratch: K-Nearest Neighbors Classifier | by Aman Sharma | Sep, 2020 | Towards Data Science
• Machine Learning Nuggets: Introduction to K-NN Algorithm | by Venkat | Jun, 2021 | Medium
• Create a K-Nearest Neighbors Algorithm from Scratch in Python | by Turner Luke | Apr, 2022 | Towards Data Science
• TOROS N2 - lightweight approximate Nearest Neighbor library which runs faster even with large datasets python
  • TOROS N2

Labeling

• Pseudo-Labeling to deal with small datasets — What, Why & How?
• Introducing Label Studio, a swiss army knife of data labeling
• Hand Labeling Considered Harmful – O’Reilly
• awesome-open-data-annotation: Open Source Data Annotation & Labelling Tools

Library

• 머신러닝과 예측 알고리즘에 '안성맞춤' 데이터 과학 툴 10선
• The 6 most useful Machine Learning projects of the past year (2018)
  • fast.ai Detectron FastText AutoKeras Dopamine vid2vid
• Top 20 Python Machine Learning Open Source Projects, updated
• Highest Rated ML Projects on Github
• A Tour of End-to-End Machine Learning Platforms – Databaseline by Ian Hellström
• ‘머신러닝을 쉽게’··· 추천 오픈소스 도구 14선 - CIO Korea
• '내부 머신러닝 지원' 데이터베이스 8종 - CIO Korea
• 10 Best Data Analysis and Machine Learning Libraries/Tools - DZone AI
• aerosolve
  • aerosolve
  • 에어비앤비, 머신러닝 데이터 분석도구 오픈소스로 공개
• Alibi - an open source Python library aimed at machine learning model inspection and interpretation
• awesome-machine-learning-on-source-code: Cool links & research papers related to Machine Learning applied to source code (MLonCode)
• backend.ai
  • docs.backend.ai
  • lablup/backend.ai: Backend.AI is a streamlined, container-based computing cluster orchestrator that hosts diverse programming languages and popular computing/ML frameworks, with pluggable heterogeneous accelerator support including CUDA and ROCM
  • Backend.AI 오픈소스 기여 가이드 | Lablup Blog
  • 백엔드닷에이아이(Backend.AI)로 시작해 보는 AI 오픈소스 소개 및 실습 - 01 - YouTube
  • 포크리프트 활용하기 | Lablup Blog
  • Backend.AI MLOps 플랫폼 FastTrack을 소개합니다. | Lablup Blog
• cortex: Model serving at scale
  • We tried to build an end-to-end ML platform. Here’s why it failed. | by Caleb Kaiser | Towards Data Science
• cuml: cuML - RAPIDS Machine Learning Library
• A-Curated-List-of-ML-System-Design-Case-Studies: This repository contains a curated collection of 300+ case studies from over 80 companies, detailing practical applications and insights into machine learning (ML) system design. The contents are organized to help you easily find relevant case studies based on industry or specific ML use cases.
  • 이상선 - 실무형 머신러닝 사례 아카이브 공개! 이 GitHub 저장소에는 80개 이상의 기업에서 실제로 구축한... | Facebook
• Dlib - C++ machine learning library
• DL-Learner - a framework for supervised Machine Learning in OWL, RDF and Description Logics
  • A tool for supervised Machine Learning in OWL and Description Logics
• DVC - Open-source Version Control System for Machine Learning Projects git과 비슷한 명령어를 통해 data version 관리
  • DVC - Data Version Control | Git for Data & Models
  • Data version control with DVC. What do the authors have to say?
  • DVC(Data Version Control)
  • Data Version Control With Python and DVC – Real Python
  • Hongjoo Lee - Automating machine learning workflow with DVC - YouTube
• Food data set for machine learning
• Gradio - a free, open-source python library that helps machine learning researchers interact with and share their machine learning models with collaborators and clients with only a few lines of extra code
  • Building A Visual Interface for Your ML Model with Gradio - Abubakar Abid | PyData Riyadh - YouTube
    • Introduction to Gradio Notebook (Academic).ipynb - Colaboratory
  • Gradio Course - Create User Interfaces for Machine Learning Models - YouTube
  • voice-pro: Gradio WebUI for creators and developers, featuring key TTS (Edge-TTS, kokoro) and zero-shot Voice Cloning (E2 & F5-TTS, CosyVoice), with Whisper audio processing, YouTube download, Demucs vocal isolation, and multilingual translation
• h2o.ai h2o, sparkling water, steam
  • StackNet is a computational, scalable and analytical Meta modelling framework
  • H2O for Inexperienced Users
  • Democratising Machine learning with H2O
• HAXPlaybook: The HAX Playbook is an interactive tool for generating interaction scenarios to test when designing user-facing AI systems
  • '책임 있는 AI' 설계 가이드… 마이크로소프트 HAX의 이해 - ITWorld Korea
• hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python
• Impyute - a library of missing data imputation algorithms written in Python 3
• InterpretML
  • interpret: Fit interpretable models. Explain blackbox machine learning.
• interviews.ai: It is my belief that you, the postgraduate students and job-seekers for whom the book is primarily meant will benefit from reading it; however, it is my hope that even the most experienced researchers will find it fascinating as well
  • 딥러닝 인터뷰 북 401P e북 PDF | GeekNews
• Kobra | A visual programming language for machine learning (in beta)
• LambdaML: Pros and Cons of Serverless for Deep Network Training
• lance: Modern columnar data format for ML implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, with more integrations coming
• Leaf is a open source framework for machine intelligence, sharing concepts from TensorFlow and Caffe
• LightGBM - table data에 강하다고?
  • Welcome to LightGBM’s documentation! — LightGBM 2.3.2 documentation
  • Ensemble Colab에서 LightGBM 사용하기!
  • 토스ㅣSLASH 22 - Data Scientist는 어떻게 비즈니스에 기여할 수 있을까? - YouTube
  • lightgbm-serving: A lightweight server for LightGBM
• Machine Learning - Java collection that provides Java packages for developing a machine learning algorithm
• Machine-Unlearning-Comparator: A Visual Analytics System for Comparative Evaluation of Machine Unlearning Methods
• MediaPipe - a cross-platform framework for building multimodal applied machine learning pipelines https://mediapipe.dev
  • MediaPipe to openFrameworks
  • 가위바위보를 인식하는 Mediapipe hand Python 예제 - 멈춤보단 천천히라도
  • TF Tutorial On-device Real-time Hand Tracking - 공부 기록장
  • 손쉽게 컴퓨터비전 서비스를 만들어보자! - 1. 소개
• metarank: A low code Machine Learning tool that personalizes product listings, articles, recommendations, and search results in order to boost sales. A friendly Learn-to-Rank engine
• ML-airport-taxi-out: The ML-airport-taxi-out software is developed to provide a reference implementation to serve as a research example how to train and register Machine Learning (ML) models intended for four distinct use cases: 1) unimpeded AMA taxi out, 2) unimpeded ramp taxi out, 3) impeded AMA taxi out, and 4) impeded ramp taxi out. The software is designed to point to databases which are not provided as part of the software release and thus this software is only intended to serve as an example of best practices. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides examples how to build three distinct pipelines for data query and save, data engineering, and data science. These pipelines enable scalable, repeatable, and maintainable development of ML models
• MLDB - the Machine Learning Database
  • Technology Fridays: MLDB is the Database Every Data Scientist Dreams Of
• mlflow - Open source platform for the machine learning lifecycle https://mlflow.org
  • Getting started with mlFlow
  • How to Display Model Evaluation Metrics Using MLflow Search API
  • MLflow - An open platform for the machine learning lifecycle - Abdulrahman Alfozan | PyData Riyadh - YouTube
  • How to Utilize MLflow and Kubernetes to Build an Enterprise ML Platform - Databricks
  • Combining Machine Learning and MLflow with your Lakehouse. Data Brew | Episode 5 - YouTube
  • mlflow를 활용한 pipeline 시스템 구축
  • MLflow를 이용한 머신러닝 프로젝트 관리. 박선준- PyCon Korea 2021 - YouTube
  • Theodore Meynard - MLflow workshop | PyData London 2023 - YouTube
  • Samsung Tech Blog - AI를 지탱하는 MLOps: Kubeflow와 MLflow를 활용한 AI 개발 시스템 구축
  • Building Trustworthy, High-Quality AI Agents with MLflow - YouTube (Databricks)
    • MLflow tracing으로 에이전트 단계별 실행 분석/디버깅, 자동 LLM-as-a-Judge 평가로 전문가 피드백 스케일, prompt registry 버저닝/최적화, AI gateway 거버넌스(권한·레이트 리밋·input guard)로 비용·프라이버시 관리. 에이전트 라이프사이클 통합 플랫폼
• ML Kit
  • 모바일 개발자를 위한 ML Kit: Machine Learning SDK 소개
  • iOS에서 머신러닝
  • ML Kit와 ARKit가 함께 작동하는 방식을 알아보세요
  • iOS 앱에서 머신러닝이 해결 할 수 있는 문제들
• ml-workspace - All-in-one web-based IDE specialized for machine learning and data science
  • 머신러닝 워크스페이스가 이미 구축된 도커 이미지를 제공하는 프로젝트. 모든것이 웹기반
    • 라이브러리: Sklearn, TensorFlow, PyTorch, Keras 등등
    • IDE: 주피터 노트북, 주피터 랩, VSCode
    • 모니터링: TensorBoard, Netdata, Glances
    • 버전컨트롤: git, Ungit
    • 기타: 리모트 액세스, 리눅스 데스크탑 GUI 액세스, 다중 사용자 액세스, 포트 커스터마이징, 유용한 주피터노트북 확장
  • 도커 이미지 하나로 모두 실행(GPU버전 기준, 단 현재는 CUDA10 만을 지원) docker run -p 8080:8080 --gpus all mltooling/ml-workspace-gpu:latest
    • GPU 이외의 CPU 버전, R 특화 버전, 최소화 버전, 가벼운 버전등 도커 이미지 제공
  • 위의 나열된 도구들이 제각각 노는것이 아님. 주피터 노트북과, 주피터 랩에는 유용한 확장이 설치
    • 예를 들어, 원하는 디렉토리로 이동 후 git 버튼의 클릭으로 버전 컨트롤을 하거나, tensorboard 버튼의 클릭으로 텐서보드를 해당 폴더의 로그 파일을 기반으로 실행하거나, vscode 버튼의 클릭으로 선택한 파일을 VSCode에서 곧바로 편집이 가능
• OpenML Home
  • OpenML 2019
• Petuum - a distributed machine learning framework
• PlaidML.pdf at main · ConstantPark/DL_Compiler
• PostgresML - an end-to-end machine learning solution
  • PostgresML - an end-to-end machine learning system. It enables you to train models and make online predictions using only SQL, without your data ever leaving your favorite database.
• Predict - see who will convert, before they do
  • Mixpanel launches predict; what does this predict tool mean?
• QLattice
  • The QLattice: A new machine learning model you didn’t know you needed | by Valdemar Stentoft-Hansen | Abzu | Medium
  • Abzu’s QLattice, A New Explainable AI • Jaan Kasak • GOTO 2021 - YouTube
• rc-data - Question answering dataset featured in "Teaching Machines to Read and Comprehend
• replicate: Version control for machine learning
  • Replicate – Version control for machine learning
• SimpleML for Sheets documentation Machine Learning (ML) in Google Sheets
• SynapseML: Simple and Distributed Machine Learning
  • SynapseML - 머신러닝 파이프라인 생성용 오픈소스 라이브러리 | GeekNews
• SystemML - IBM's SystemML Machine Learning - Now Apache SystemML http://systemml.apache.org
• tensor-house: A collection of reference machine learning and optimization models for enterprise operations: marketing, pricing, supply chain
• traingenerator: 🧙 A web app to generate template code for machine learning
• Tribuo - A Java machine learning library
  • 오라클, 자바 머신러닝 라이브러리 ‘트리뷰오’ 오픈소스로 공개 - 인공지능신문
• WSO2 Machine Learner: Why would You care?

LinkedIn

• FeatureFu
  • 링크드인, 자체 머신러닝 기술 오픈소스로 공개

Microsoft

• Distributed Machine Learning Toolkit
• Azure
• Oxford 프로젝트 옥스포드(Oxford), 마이크로소프트가 만든 머신러닝 프로젝트

MLOps

• SCALABLE MACHINE LEARNING - THE STATE OF DATAOPS / MLOPS IN 2018
• 머신러닝 오퍼레이션 자동화, MLOps
• MLOps 1. 실험 관리 AI 혁신 속도는 모델 학습에서 배포까지 걸리는 속도에 비례한다
• MLOps: Continuous delivery and automation pipelines in machine learning 번역
• Introducing MLOps - 차문수(Superb AI) :: 제33회 AWSKRUG DataScience모임 - YouTube
• What is MLOps? Machine Learning Operations Explained
• A Chat with Andrew on MLOps: From Model-centric to Data-centric AI - YouTube
  • AI뉴스 AI계의 사대천왕, 앤드류 응 교수님의 주옥같은 인사이트가 우루루~데이터 중심 AI시대 그리고 MLOps의 급부상? 놓치면 안 될 응 교수님의 라이브 강좌 핵심 요약본! - YouTube
  • 더 많은 데이터 중심으로, 덜 적은 모델로!
• 앤드류 응 교수가 말하는 MLOps
• AI는 '빅데이터(Big Data)'보다 '굿데이터(Good Data)'를 좋아합니다 - 쉽고 재미있는 IT뉴스, 아웃스탠딩!
• 칼럼ㅣAI 프로젝트 악순환 고리 끊어라··· 'ML옵스' 마인드셋이 필요한 이유 - CIO Korea
• MLOps And Machine Learning Roadmap - KDnuggets
• 커피고래가 생각하는 MLOps | 커피고래의 노트
• 하나의 코드 베이스 & 파이프라인으로 여러 도메인에 “코드 수정 없이” AI 모델들을 배포할 수 있을까. 박중배 - PyCon Korea 2021 - YouTube
• Welcome to MLOps for ALL! - 모두의 MLOps
  • MLOps를 공부하고자 하는 사람들의 지침서로 만든 프로젝트
  • MLOps는 머신러닝을 서비스에 적용하기 더 쉽도록 파이프라인을 통해 머신러닝 엔지니어와 소프트웨어 엔지니어가 소통할 수 있게 함
  • 개념 설명뿐 아니라 설정 방법과 Kubeflow에 대한 설명이 포함되어 있어서 MLOps에 관심 있거나 공부해 보고자 하면 아주 좋은 문서
• MLOps: Big Picture in GCP. “Why do we need different CI/CD for… | by Park Chansung | Google Developers Experts | Medium
• ML Ops with Dagster: 5 Key Features for Developing a Continuous Training Pipeline - Thinking Machines Data Science
• What is Continuous Training (CT) in MLOps and what steps are needed to achieve it?
• MODUCON 2021 글로벌 넘버원이 되기위한 MLOps Learning Path-박찬성Development Enviroment - 개발환경 - YouTube
• 모두의 MLOps (1) MLOps에 첫걸음을 내딛는 분들을 위한 지침서 | by Jongseob Jeon | We’re Team MakinaRocks! | Jan, 2022 | Medium
• 모두의 MLOps (2) MLOps의 단계와 단계별 핵심 기능 | by Jongseob Jeon | We’re Team MakinaRocks! | Mar, 2022 | Medium
• 모두의 MLOps (3) 머신러닝에서의 파이프라인이란. 모두의 MLOps 시리즈 3편. 이번 포스팅에서는 파이프라인이란… | by Jongseob Jeon | We’re Team MakinaRocks! | May, 2022 | Medium
• MLOps정의와 다양한 도구들-1 | Lablup Blog
• MLOps정의와 다양한 도구들-2 | Lablup Blog
• Why data scientists shouldn’t need to know Kubernetes
• MLOps란? MLOps 로드맵 및 MLOps 취업에 대한 생각 | MLOps 강의 추천 있음 - YouTube
• MLOps and DevOps: Why Data Makes It Different – O’Reilly
• Production ML: Getting Started with MLOps | by Hajar Khizou | Towards Data Science
• MLOps or How to Deploy Data Science at Scale | Towards Data Science
• MLOps 생태계 2022년 전망과 Backend.AI로 가속하는 하이퍼스케일 AI 실전로드맵 토크아이티 웨비나, 래블업 - YouTube
• MLOps : 딥랩 세미나 요약- 8회차. 💡 딥랩 세미나 요약은 발표자인 모더레이터의 발표를 듣고 사회자인… | by Eunsoo Park | 모두의연구소 기술 블로그 | May, 2022 | Medium
• INNOPOLIS AI SPACE-S 인공지능 세미나 - MLOps와 높은 진입 장벽 - YouTube
• FastAPI 및 TFServing을 이용한 ML 배포
  • FastAPI 및 TFServing 으로 모델을 k8s(GKE) 환경에 배포하여, 몇 가지 성능을 확인해보는 간단한 프로젝트
  • ml-deployment-k8s-fastapi: This project shows how to serve an ONNX-optimized image classification model as a web service with FastAPI, Docker, and Kubernetes
  • ml-deployment-k8s-tfserving: This project shows how to serve an TF based image classification model as a web service with TFServing, Docker, and Kubernetes(GKE)
  • FastAPI의 경우에 고려된 파라미터
    • 단순 로컬에서 TF 모델과 TF -> ONNX 변환 모델 추론 속도
    • FastAPI에는 ONNX로 변환된 모델을 넣었습니다
    • uvicorn, gunicorn worker 의 개수
  • TFServing의 경우에 고려된 파라미터
    • 로컬/리모트 단일 추론에서 RestAPI 보다 gRPC가 뛰어남을 확인
    • 커스텀 TFServing 이미지 빌드시, CPU 최적화된 TF를 컴파일
    • TFServing 에 일부 CPU 코어 및 쓰레드 수에 따라 변경해볼만한 파라미터( --inter_op_parallelism_threads, --itra_op_parallelism_threads), TFServing 의 동적 배치 추론 기능 활성화 파라미터( --enable_batching 및 config 파일 작성)
  • 두 경우에 모두 공통적으로 고려된 사항
    • 서버를 구동하는 k8s 클러스터의 Pod 개수 (노드마다 여러 Pod이 있을 수 있으나, 각 노드별 하나의 Pod만 프로비져닝)
    • 클러스터 Node 개수가 많아질 수록 Node의 사양은 ⬇, 클러스터 Node 개수가 적어질 수록 Node의 사양은 ⬆
  • FastAPI로 ML 추론 서버를 구축하는 예제가 상당히 많이 존재하지만, 현실적으로는 상용 수준으로 쓰기에는 "어렵다"로 생각
    • 불안정한 부분이 많고, 기본적으로 ML 추론용 서버가 갖춰야 할 다양한 기능이 결여되어 있기 때문에, 직접 이를 모두 구현해야 하는 골치아픈 작업이 발생(동적 배치 추론, 모델 버전 관리, 다중 버전의 다중 모델 동시 추론 기능 등)
  • 두 저장소 모두 푸쉬, 새로운 모델 릴리즈에 따라 자동으로 GKE 클러스터로 도커 이미지를 빌드한 뒤 배포하는 GitHub Action 도 함께 작성
• nvidia-ml-py를 사용해서 kubernetes에 배포되어 있는 인스턴스에서 MIG 메모리 사용량 체크하기 | by Ryan Kim | Jun, 2022 | Medium
• 머신러닝 시스템 디자인 패턴
• 나도 모르는 새 이미 쓰고 있던, 머신러닝 시스템 디자인 패턴 1. - 모두의연구소
• Design Patterns in Machine Learning Code and Systems
• 2205.02302 Machine Learning Operations (MLOps): Overview, Definition, and Architecture
• PyData Chicago: Building an MLOps platform at HelloFresh, by Dr. Erik Widman - YouTube
• AI 인프라 스케일 아웃을 고려하는 스타트업을 위하여 (FEAT. MLOps) | by Ryan Kim | Nov, 2022 | Medium
• 리디의 머신러닝 파이프라인 톺아보기 - 리디주식회사 RIDI Corporation
• if kakao 2022 ML 모델 학습 파이프라인 설계 (feat. MLOps 플랫폼) | 카카오페이 기술 블로그
• Data Pipelining for Real-time ML Models | PyData London 2022 - YouTube
• “반복 속도가 AI의 성패를 결정한다” 산업/제조 현장 AI를 위한 MLOps 전략 - Tech Summary - ITWorld Korea
• MLOps for MLE | ML Engineer를 위한 MLOps
• AI Fintech 스타트업의 IDC 네트워크 인프라를 구성하는 기술 (FEAT. Qraft) | by Ryan Kim | Dec, 2022 | Medium MLOps는 아니고 인프라 이야기지만 내용 흐름상 여기에 입력
• Self-serve feature platforms: architectures and APIs
  • MLOps KR | 제가 애정하는 Chip Huyen이 MLOps 피쳐 플랫폼에 대한 글([https://huyenchip.com//2023/01/08/self-serve-feature-platforms.html](https://h... | Facebook
• ML Ops 궁극 가이드: MLOps 및 DevOps의 개념과 차이점 이해
• Duarte Carmo - MLOps for the rest of us- A poor man's guide to putting models in production - YouTube
• Subscribe : MLOps Insight
• LINE의 엔드 투 엔드 MLOps 플랫폼, MLU
• 배민 앱에도 AI 서비스가? AI 서비스와 MLOps 도입기 🦾 | 우아한형제들 기술블로그
• Bridging the Chasm Between Research & Software Development • Linda Stougaard Nielsen • GOTO 2022 - YouTube
• MLOps 구축 가이드 - 정의, 프로세스, 비교, Use case - 데이터헌트
• Cole Bailey: Cooking up a ML Platform - Growing pains and lessons learned - YouTube
• MLOps 비교 분석 - SageMaker, Kubeflow, MLflow, WandB, Tensorflow Extended - 데이터헌트
• ML model server gpu -> cpu 전환 경험 공유 | by o00o0oo | 네이버 플레이스 개발 블로그 | Jul, 2023 | Medium
• 우아한테크세미나 MLOps를 활용한 AI 서비스 개발 스토리 - YouTube
• The State of Production Machine Learning in 2023 — Alejandro Saucedo - YouTube
• What are the four Machine Learning Model Deployment Types? Batch, Emebedded in a Stream Application, Real Time, Edge
• AIQC: End-to-end deep learning on your desktop or server
  • AIQC; Deep Learning Experiment | PyData Global 2021 - YouTube
• awesome-mlops: A curated list of references for MLOps
• BentoML - A platform for serving and deploying machine learning models http://BentoML.ai
  • Machine Learning Serving - BentoML 사용법 · 어쩐지 오늘은
  • 데이터야놀자2021 머신러닝 유니버스를 여행하는 히치하이커를 위한 데이터 팀이 AWS 위에서 Kubernetes 를 활용하는 법 - 박훈님 - YouTube
  • MLOps를 위한 BentoML 기능 및 성능 테스트 결과 공유 - 1 - LINE ENGINEERING
  • MLOps를 위한 BentoML 기능 및 성능 테스트 결과 공유 – 2 - LINE ENGINEERING
  • 코드로 이해하는 BentoML
  • Sean Sheng - Building an ML Application Platform from the Ground Up | PyData Global 2022 - YouTube
  • PyCon KR 2023 MLOps Model Serving Architecture with BentoML 김성렬 - YouTube
  • bento
    • Announcing Bento, the open source fork of the project formerly known as Benthos - WarpStream - Stream More, Manage Less
      • Bento 발표, Benthos의 오픈소스 포크 버전 | GeekNews
• dbt Labs | Transform Data in Your Warehouse
  • Dorian Van den Heede - Your best Bet: Effortless MLOps with Python Models in dbt - YouTube
  • mixpanel_ml
• Flyte
  • ML Pipeline에 Flyte 도입하기. ML 오픈 소스의 망망대해 항해하며, ML Pipeline 구성해… | by Ryan Kim | Jun, 2022 | Medium
  • Flyte MLOps Pipeline 특징 정리. Kubeflow, Airflow와 비교해보는 Flyte 특징 분석과… | by Ryan Kim | Medium
  • nvidia-ml-py를 사용해서 kubernetes에 배포되어 있는 인스턴스에서 MIG 메모리 사용량 체크하기 | by Ryan Kim | Medium
  • Github CI와 Flyte를 사용하여 리서치 엔지니어를 위한 ML 환경 구성해주기 | by Ryan Kim | Nov, 2022 | Medium
  • MODUPOP | 글로벌 MLOps 트렌드와 한국형 MLOps 전략 - YouTube
  • Why We Switched Our Data Orchestration Service - Spotify Engineering : Spotify Engineering
• JupyterFlow - Run your workflow on JupyterHub
  • 번역 데이터 과학자들은 쿠버네티스에 관심이 없습니다 - MLOps | 커피고래의 노트
  • 데이터 과학자를 위한 ML툴 | 커피고래의 노트
• MadeWithML: Learn how to responsibly deliver value with ML
  • Home - Made With ML 애플 ML 개발자였던 Goku Mohandas가 만든 MLOps 코스. 처음부터 쉽고 간결하게 가르치는 것이 특징
• **MLOps-Basics
  • MLOps Basics Week 0: Project Setup – Raviraja's Blog 프로젝트 설정
  • MLOps Basics Week 1: Model Monitoring - Weights and Bias – Raviraja's Blog 모델 모니터링 - 웨이트와 편견
  • MLOps Basics Week 2: Configurations - Hydra – Raviraja's Blog 구성 - 하이드라
  • MLOps Basics Week 3: Data Version Control - DVC – Raviraja's Blog 데이터 버전 제어 - DVC
  • MLOps Basics Week 4: Model Packaging - ONNX – Raviraja's Blog 모델 포장 - ONX
  • MLOps Basics Week 5: Model Packaging - Docker – Raviraja's Blog 모델 포장 - Docker
  • MLOps Basics Week 6: CI/CD - GitHub Actions – Raviraja's Blog CI / CD - GitHub 액션
  • MLOps Basics Week 7: Container Registry - AWS ECR – Raviraja's Blog 컨테이너 레지스트리 - AWS ECR
  • MLOps Basics Week 8: Serverless Deployment - AWS Lambda – Raviraja's Blog 서버리스 배치 - AWS Lambda
  • MLOps Basics Week 9: Prediction Monitoring - Kibana – Raviraja's Blog 예측 모니터링 - 키바나
  • MLOps Basics Week 10: Summary – Raviraja's Blog 요약**
• MLOps NYC
  • MLOps Tooling
• mlops-project
  • Insurance Cross Sell Prediction
  • Machine Learning Operations (MLOps) For Beginners | by Prasad Mahamulkar | Aug, 2024 | Medium
• UnionML: Build and Deploy Machine Learning Microservices
  • UnionML: the easiest way to build and deploy machine learning microservices
  • Shivay Lamba - Tour of UnionML: An Open Source framework for Building Machine Learn Microservices - YouTube
• wandb.ai Weights & Biases – Developer tools for ML MLOps 인프라
  • Better tracking for your deep learning training - Wandb.ai (Weights & Biases) - YouTube
  • Why Everyone Is Migrating from MLflow to Weights & Biases (W&B) in 2025 | by Weights & Biases Korea | Jul, 2025 | Medium

Paper

• Nested Learning: The Illusion of Deep Learning Architecture
  • Introducing Nested Learning: A new ML paradigm for continual learning
  • Nested_Learning_A_New_Paradigm.pdf - Google Drive
  • 기존의 역전파 , 모멘텀 , 트랜스포머등 기본적인 딥러닝의 고효율 방법론을 재해석한 연속 메모리 시스템을 기반으로한 Hope 라는 알고리즘으로 치명적 망각 문제와 모델의 정확도를 올리는 성과

Python

• Bare bones Python implementations of Machine Learning models and algorithms. Aims to cover everything from Data Mining techniques to Deep Learning
• Try Deep Learning in Python now with a fully pre-configured VM
• Simple Machine Learning Model in Python in 5 lines of code
• Your First Machine Learning Project in Python Step-By-Step
• 7 Steps to Mastering Machine Learning With Python
  • 7 Steps to Mastering Machine Learning With Python
• TOP 5 ESSENTIAL BOOKS FOR PYTHON MACHINE LEARNING
• Machine learning with Python: A Tutorial
• facebook 바벨피쉬 middle learning 파이썬을 이용한 기계학습 알고리즘
• Ask HN: What is the best way to learn Machine Learning in Python?
• Searching for Approximate Nearest Neighbours
• Python, Machine Learning, and Language Wars. A Highly Subjective Point of View
• Example Machine Learning Notebook.ipynb
• linux machine learning
• Tutorial – Getting Started with GraphLab For Machine Learning in Python
• Machine Learning in Python has never been easier
• Essentials of Machine Learning Algorithms (with Python and R Codes)
• K Nearest Neighbors Application - Practical Machine Learning Tutorial with Python p.14
• Pycon2016_ML(DL)
• Machine Learning with Python
• Keynote: Machine Learning for Social Science | SciPy 2016 | Hanna Wallach
• Machine Learning: Python and the Power of Ensembles by Bargava Raman Subramanian
• Python Machine Learning Tutorial, Scikit-Learn: Wine Snob Edition
• Introduction To Machine Learning
• An Introduction to Python for Machine Learning with VS Code and Azure - YouTube
• An Introduction to Python Machine Learning with Perceptrons
• The Perceptron Algorithm explained with Python code
• 퍼셉트론 강의
• Simple Softmax Regression in Python — Tutorial
• 토끼와 거북이가 알려주는 회귀(Regression)
• Simple Linear Regression Tutorial for Machine Learning (ML) | by Towards AI Team | Towards AI | Medium
• Python ML guided project Level 1, 24min - simple linear regression house price prediction
• Python Machine Learning Guided Project - Spaceship Titanic part 2 ML Predict, Level 8, 49 min
• 인공지능 컴퓨팅을 위한 파이썬 강좌
• Machine Learning Exercises In Python - Part 1
• Machine Learning Exercises In Python, Part 2
• Clustering with Scikit with GIFs
• Advice for applying Machine Learning
• Unsupervised Machine Learning for Fun & Profit with Basket Clusters
• Data Science – Deep Learning in Python
• 22 must watch talks on Python for Deep Learning, Machine Learning & Data Science (from PyData 2017, Amsterdam)
• Pattern Designing in Python | Pattern Matching in Machine Learning
• Some Essential Hacks and Tricks for Machine Learning with Python
• Supervised Learning with Python
• Unsupervised Learning with Python
• Machine Learning with Python: Easy and robust method to fit nonlinear data
• A Complete Machine Learning Project Walk-Through in Python
  • Part One Putting the machine learning pieces together
  • Part Two Model Selection, Hyperparameter Tuning, and Evaluation
  • Part Three Interpreting a machine learning model and presenting results
• A Complete Machine Learning Project Walk-Through in Python
• A “Data Science for Good“ Machine Learning Project Walk-Through in Python: Part One
• 285+ Machine Learning Projects with Python | by Aman Kharwal | Coders Camp | Medium
• 파이썬으로 머신러닝 배우기 (1/3)
• 파이썬으로 머신러닝 배우기 (2/3)
• 파이썬으로 머신러닝 배우기 (3/3)
• TEAMLAB X Inflearn | 파이썬 머신러닝 입문 강좌
• 100 Days Of ML Code
• Deploying a Machine Learning Model as a REST API
• An Essential Guide to Numpy for Machine Learning in Python
• 4 Machine Learning Techniques with Python
• setscholars.net Applied Data Science in Business & Biological Sciences: Python, R & MATLAB Codes for Beginners
• Custom Transformers and ML Data Pipelines with Python
• How to Transform Research Oriented Code into Machine Learning APIs with Python ML에서의 refactoring에 대한 이야기
• 파이썬과 SQL을 활용한 패혈증(Sepsis) 분석
• 라이브러리 없이 단층 퍼셉트론 구하기 ML with pYTHON
• 사이킷 런의 svm.SVC 사용 및 흉내내어 만들어보기 ML with pYTHON
• Interpretable Machine Learning | LIME In Machine Learning
• LIME: Local Interpretable Model-Agnostic Explanations
• Gradient Descent for Machine Learning (ML) 101 with Python Tutorial | Towards AI
• Math-free, Parameter-free Gradient Descent in Python - Machine Learning Techniques
• best-of-ml-python: 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly
• 8 Basic Easy to Follow Steps to Learn Machine Learning with Python
• 파이썬, ML - SLiPP 스터디 - SLiPP::위키
• Start Asking Your Data “Why?” - A Gentle Introduction To Causal Inference | PyData Global 2021 - YouTube
• Sliding into Causal Inference, with Python! - Alon Nir | PyData Global 2021 - YouTube
• Kevin Klein - Causal Inference Libraries: What They Do, What I'd Like Them To Do | PD Amsterdam 2023 - YouTube
• Causal Machine Learning for Creative Insights | by Netflix Technology Blog | Netflix TechBlog
• Building a Machine Learning Web Application Using Flask | by Gerry Christian Ongko | Feb, 2022 | Towards Data Science
• Christian Hundt & Miguel Martínez - Machine Learning Frameworks Interoperability - YouTube
• JIm Dowling - Build a production ML system with only Python on free serverless services - YouTube
• Scaling Python for Machine Learning: Beyond Data Parallelism • Holden Karau • GOTO 2023 - YouTube
• 쿠팡의 머신러닝 플랫폼을 통한 ML 개발 가속화. 쿠팡의 머신러닝 개발 속도를 높이는 쿠팡만의 ML 플랫폼에 대하여 | by 쿠팡 엔지니어링 | Coupang Engineering Blog | Nov, 2023 | Medium
• Jim Dowling - From zero to a working ML system with Python, free serverless services + FTI pipelines - YouTube
• Natan Mish - Event Driven Machine Learning | PyData London 2023 - YouTube

Python Library

• Top 20 Python Machine Learning Open Source Projects, updated
• Top 20 Python 머신러닝 오픈소스 프로젝트
• 10 Python Machine Learning Projects on GitHub
• 9 Python Analytics Libraries
• Top 20 Python Machine Learning Open Source Projects
• The Best Machine Learning Libraries in Python
• ann-writer - An artificial machine learning program that attempts to impersonate the writing style of any given text training set
• baal - Using approximate bayesian posteriors in deep nets for active learning
• Causal ML: A Python Package for Uplift Modeling and Causal Inference with ML
• dowhy: DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks
  • Patrick Blöbaum: Performing Root Cause Analysis with DoWhy, a Causal Machine-Learning Library - YouTube
• easyopt: zero-code hyperparameters optimization framework
• edenai-python: The best AI engines in one API: vision, text, speech, translation, OCR, machine learning, etc. SDK and examples for Python developers
• ggml: Tensor library for machine learning
  • ggml-python: Python bindings for ggml. Fork of the original project to use the latest version of ggml
• Gradient-Free-Optimizers: Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces
• facet: Human-explainable AI
  • Jan Ittner & Mateusz Sokół - Exploring Feature Redundancy and Synergy with FACET 2.0 - YouTube
• Hopsworks - Batch and Real-time ML Platform
  • Build a Production ML System with only Python on Free Serverless Services - YouTube
    • Serverless ML Workshop with Hopsworks at PyData Seattle
    • air_quality: Air Quality Predictions for Cities using Weather
• igel: a machine learning tool that allows to train, test and use models without writing code
• JAX: Autograd and XLA
  • ML 최적화 1. JIT & google JAX
  • JAX KR 문서 번역 프로젝트 — JAX-KR 문서
  • ECE1513H: Introduction to Machine Learning Winter 2020 - LEC0101
    • ECE1513_Introduction_to_JAX .ipynb
    • ECE1513_Introduction_to_JAX_Student_Solutions .ipynb
  • Intro to JAX: Accelerating Machine Learning research - YouTube
  • Engineering Trade-Offs in Automatic Differentiation: from TensorFlow and PyTorch to Jax and Julia - Stochastic Lifestyle
  • 잭스로 구현하는 딥러닝 - YouTube
  • 코파일럿과 함께 구현하는 딥러닝. 깃허브가 제공하는 코파일럿을 사용하면 편리하게 코드를 구현할 수 있다… | by Sungjin (James) Kim, Ph.D. | Jul, 2022 | Medium
  • "가속기를 단 넘파이" 구글 JAX 시작하기 - ITWorld Korea
  • Unfolding the paper windmills - presented by Mai Giménez - YouTube
  • Simon Pressler: Getting started with JAX - YouTube
  • Keras 에서 Jax, Flax 로 코드를 옮겨본 소감 – Passion is like genius; a miracle.
  • Google JAX 소개
  • PyTorch is dead. Long live JAX. | Neel Gupta
    • PyTorch는 죽었다. JAX 만세 | GeekNews
  • JAX와 TPU를 이용한 Decoder-based Tiny LLM 사전학습 | Sungmin Han
    • JAX와 TPU를 활용한 작은 디코더 기반 LLM 학습과 추론 환경 구축 과정 설명
  • Keras-community-week_Flax_and_Keras.pptx
  • Flax: A neural network library for JAX designed for flexibility
  • Haiku - a simple neural network library for JAX developed by some of the authors of Sonnet, a neural network library for TensorFlow
  • haiku-baseline
  • RLax (pronounced "relax") - a library built on top of JAX that exposes useful building blocks for implementing reinforcement learning agents
  • tunix: A JAX-native LLM Post-Training Library
    • Introducing Tunix: A JAX-Native Library for LLM Post-Training - Google Developers Blog
    • Introducing Tunix: A JAX-Native Library for LLM Post-Training- Google Developers Blog | Abheesht Sharma
• lazypredict: Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning
• mango: Parallel Hyperparameter Tuning in Python
  • Mango: A new way to do Bayesian optimization in Python | by Carmen Adriana Martinez Barbosa | Aug, 2022 | Towards Data Science
• merlin: Kubernetes-friendly ML model management, deployment, and serving
  • Machine Learning Models as Tables - ClickHouse Software And Services | Altinity
• mljar-supervised: Automated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning
  • P MLJAR + Optuna = simple tuning interface (for tabular data) : MachineLearning
• Mlxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks
• modal - Modular Active Learning framework for Python3
• NapkinML - Pocket-sized implementations of machine learning models in NumPy
• Neptune
  • 머신러닝 Experiment Management 쉽게 하기(feat. neptune ai)
  • Neptune...? 은 또 뭐여.. · Jerry's Blog
• optuna: A hyperparameter optimization framework
  • Nas from scratch using Optuna - Woongjoon_AI
  • Optuna를 사용하여 XGBoost 최적 하이퍼 파라미터 구하는 예제코드 - 멈춤보단 천천히라도
• Orange - Open source machine learning and data visualization for novice and expert. Interactive data analysis workflows with a large toolbox
  • Orange3 지도학습 - 생활코딩
  • Orange3
  • Orange3 안에서 Python & Pandas 사용하기 - YouTube
• PHOTONAI
  • 21 Techniques to Write Better Code - The Startup - Medium
• PyCaret - an open source, low-code machine learning library
  • Announcing PyCaret 1.0.0 An open source low-code machine learning library in Python
  • PyCaret튜토리얼-회귀.ipynb
  • Introduction to Clustering in Python with PyCaret | by Moez Ali | Dec, 2021 | Towards Data Science
  • A Practical Guide to ARIMA Models using PyCaret — Part 1 | by Nikhil Gupta | Nov, 2021 | Towards Data Science
  • A Practical Guide to ARIMA Models using PyCaret — Part 2 | by Nikhil Gupta | Nov, 2021 | Towards Data Science
  • A Practical Guide to ARIMA Models using PyCaret — Part 3 | by Nikhil Gupta | Nov, 2021 | Towards Data Science
  • PyCaret 2.3.5 Is Here! Learn What’s New | by Moez Ali | Nov, 2021 | Towards Data Science
  • Machine Learning Made Easy With PyCaret • Moez Ali • GOTO 2022 - YouTube
• PyCM - Multi-class confusion matrix library in Python http://pycm.ir
  • PyCM 2.5 released : Multi-class confusion matrix library in Python
• PyGAD Welcome to PyGAD’s documentation! — PyGAD 2.9.0 documentation
  • GeneticAlgorithmPython: Build the Genetic Algorithm Implementation in Python 3 using NumPy
• PyLops — PyLops
  • Solving large scale inverse problems in Python with PyLops - M. Ravasi, I. Vasconcelos and D. Vargas - YouTube
• PyML - Python을 이용한 머신러닝 (코세리 인증과정 도전 스터디)
• pymoo: Multi-objective Optimization in Python
  • Solving Multi-Objective Constrained Optimisation Problems using Pymoo — Pranjal Biyani - YouTube
• Sacred - a tool to help you configure, organize, log and reproduce experiments
  • Welcome to Sacred’s documentation!
  • 머신러닝 실험을 도와줄 Python Sacred 소개)
  • Sacred와 Omniboard를 활용한 실험 및 로그 모니터링
  • Sacred Example Code Pytorch
  • Sacred_Deep_Learning
• scikit-image: Image processing in Python — scikit-image
  • Auto Contrast on SEM image | Pega Devlog
• scikit-learn
  • Dive into Machine Learning with ipython notebook and scikit-learn
  • Scikit Flow: Easy Deep Learning with TensorFlow and Scikit-learn
  • Machine Learning with Scikit Learn | SciPy 2015 Tutorial | Andreas Mueller & Kyle Kastner Part I
  • Machine Learning with Scikit Learn | SciPy 2015 Tutorial | Andreas Mueller & Kyle Kastner Part II
  • Introduction to Machine Learning in Python with scikit-learn
  • Scikit-Learn Cheat Sheet: Python Machine Learning
  • Data science in Python: pandas, seaborn, scikit-learn
    • Jupyter notebooks from the scikit-learn video series http://bit.ly/scikit-learn-videos
  • Python Machine Learning: Scikit-Learn Tutorial
  • Scikit-Learn Cheat Sheet: Python Machine Learning
  • Learning to rank with Python scikit-learn
  • Scikit-Learn Tutorial: Baseball Analytics in Python Pt 1
  • Scikit-Learn Tutorial: Baseball Analytics in Python Pt 2
  • Scikit-learn Tutorial
  • Managing Machine Learning Workflows with Scikit-learn Pipelines Part 1: A Gentle Introduction
  • Machine Learning with Text in scikit-learn (PyCon 2016)
  • Scikit Learn Multiclass Learning
  • Hacking Scikit-Learn’s Vectorizers
  • 8 ways to perform simple linear regression and measure their speed using Python
  • Linear Regression Machine Learning Method Using Scikit-learn & Pandas in Python - Tutorial 30
  • A beginner’s guide to Linear Regression in Python with Scikit-Learn
  • 선형 회귀 모델에서 '선형'이 의미하는 것은 무엇인가?
  • “파이썬 라이브러리를 활용한 머신러닝” 사이킷런 0.20 업데이트
  • Scikit-Learn: A silver bullet for basic machine learning
  • Scikit-Learn Library for Machine Learning in a Nutshell
  • 파이썬 사이킷런(sklearn) 패키지에서 ROC곡선 쉽게 그리는 방법!
  • Multiple Linear Regression and Visualization in Python 3D linear regression model 시각화
  • PCA using Python (scikit-learn)
  • 주성분 분석(PCA)의 이해와 실제 데이터(IRIS)에 적용
  • 붓꽃 데이터는 이제 그만 - 차원축소 쉽게 이해하기 (PCA, LDA)
  • ML impossible: Train 1 billion samples in 5 minutes on your laptop using Vaex and Scikit-Learn
  • 파이썬(sklearn) 사이킷런(sklearn) 기초
  • Scikit Learn을 이용한 분류와 회귀 머신러닝 with python - YouTube
  • Deep Neural Multilayer Perceptron (MLP) with Scikit-learn | by Kaushik Choudhury | Sep, 2020 | Towards Data Science
  • 사이킷런 0.24 맛보기! | 텐서 플로우 블로그 (Tensor ≈ Blog)
  • Machine learning made easy with Python | Opensource.com
  • 김도형: 파이썬 데이터 분석 3종 세트 - statsmodels, scikit-learn, theano - PyCon APAC 2016
  • 사이킷런 해부학
  • Intro — Scikit-learn course
  • Scikit-Learn Course - Machine Learning in Python Tutorial - YouTube
  • Scikit-learn Crash Course - Machine Learning Library for Python - YouTube
  • Machine learning with missing values — Dirty data science
  • How to Build Machine Learning Pipeline with Scikit-Learn? And Why is it essential? – Life With Data
  • Adrin Jalali - Custom Scikit-learn Estimators - YouTube
  • BayBiggies March 2021 Online Meeting: How to Reduce Scikit-Learn Training Time - YouTube
  • scikit-learn with GPU! | 텐서 플로우 블로그 (Tensor ≈ Blog)
  • gaussian process | Pega Devlog
  • SciPy Korea 2023 - 머신러닝을 위한 정형 데이터 사이킷런 쓸까? 판다스 쓸까? - YouTube
  • Random Forest Ensemble Method with Sklearn in Python
  • Dynamically generated methods with a non-generic signature — Adrin Jalali - YouTube
  • gauss-rank-scaler: Scikit-learn compatible implementation of the Gauss Rank scaling method
    • Gauss Rank Scaler 파이썬 패키지 릴리즈
  • scikit-learn-intelex Intel® Extension for Scikit-learn — Intel(R) Extension for Scikit-learn 2021.4 documentation
  • scikit-llm: Seamlessly integrate LLMs into scikit-learn
    • LLM으로 NLP 분석을 더욱 쉽게, Scikit-LLM - YouTube
  • scikit-survival — scikit-survival 0.22.2
    • Olivier Grisel - Predictive survival analysis with scikit-learn, scikit-survival and lifelines - YouTube
  • skops: skops is a Python library helping you share your scikit-learn based models and put them in production
    • Merve Noyan - Improving production workflows for scikit-learn models with skops | PyData Global 2022 - YouTube
• scikit-multilearn: Multi-Label Classification in Python — Multi-Label Classification for Python
  • A Python module capable of performing multi-label learning tasks
• shap: A game theoretic approach to explain the output of any machine learning model
  • Welcome to the SHAP documentation — SHAP latest documentation
  • Interpret Any Machine Learning Model With Shapley Values - YouTube
• shapash: Shapash makes Machine Learning models transparent and understandable by everyone
  • P Introducing Shapash, a new Python library : makes Machine Learning models transparent and understandable by everyone : MachineLearning
• shrynk - Using Machine Learning to learn how to Compress
• sklearn
  • IACS_ComputeFest_sklearn
  • hyperopt-sklearn Hyper-parameter optimization for scikit-learn
  • Python: Implementing a k-means algorithm with sklearn
  • Speeding Up Sklearn’s kNN Algorithms With Custom Distance Metrics Written In Cython
  • Implementing a Neural Network from Scratch in Python – An Introduction
  • Building Prediction APIs in Python (Part 1): Series Introduction & Basic Example
  • Finding the right model parameters
  • Feature Selection with sklearn and Pandas
  • SMOTE를 통한 데이터 불균형 처리 oversampling 기법의 하나
  • Two hours later and still running? How to keep your sklearn.fit under control
  • sklearn의 train_test_split() 사용법
  • from sklearn import *
  • Grid search for parameter tuning. Learn this easy and simple technique to… | by Magdalena Konkiewicz | Oct, 2020 | Towards Data Science
  • Feature engineering package with sklearn like functionality
    • Feature-engine
    • 장점
      • 사용 방법 자체는 sklearn과 같이 fit(), transform()으로 사용 가능
      • 캐글에서 사용하는 다양한 테크닉이 포함되어 있어 Pandas에서 Scratch로 구현하기 어려운 분들에게 도움
      • sckit-learn에서는 data transform을 하기 위해
        • as-is: pd.Series로 전달하여 np.array로 결과를 반환하기에 다시 table에 합치는 과정이 일부 번거로움
        • to-be: 이 라이브러리는 pd.DataFrame을 전달, pd.DataFrame을 반환해주어 번거로움 감소
    • 아쉬운 점
      • 파라미터명이 기존 라이브러리와 매칭되지 않는 부분
      • 기존 pandas는 coloums 또는 cols로 열을 표현한다면 이 라이브러리에서는 variables라는 명칭 사용
      • Feature Creation에서 combination할때 커스텀 함수 기능 부재. 현재는 사칙연산 정도만 제공
      • time-series data / text data feature engineering이 추가된다면 확실히 범용적인 툴이 될 수 있을 것 같다는 기대
  • Random Forest Ensemble Method with Sklearn in Python
• slr - Simple linear regression with confidence intervals on parameters and prediction
• smalltrain
• smile - Statistical Machine Intelligence & Learning Engine http://haifengl.github.io/smile
  • Neural network classification of data using Smile
• Solara documentation
  • solara: A Pure Python, React-style Framework for Scaling Your Jupyter and Web Apps
• Streamlit - the first app framework specifically for Machine Learning and Data Science teams
  • awesome-streamlit: The purpose of this project is to share knowledge on how awesome Streamlit is and can be
  • Streamlit - 파이썬 코드를 커스텀ML도구로 쉽게 만들기 | GeekNews
  • Streamlit 101: An in-depth introduction
  • KR 파이썬 웹어플리케이션 맛보기 (feat. Streamlit) | by SEO, Wonyoung | Medium
  • How to Build a Simple Machine Learning Web App in Python
  • NotebookToWebApp/article.md at master · ChristianFJung/NotebookToWebApp
  • 멍개의 퇴근후 공부 파이썬 streamlit 다뤄보기 - YouTube
  • Build 12 Data Science Apps with Python and Streamlit - Full Course - YouTube
    • streamlit_freecodecamp: Build 12 Data Apps in Python with Streamlit
    • penguins-heroku
  • Python Streamlit 사용법 - 프로토타입 만들기 · 어쩐지 오늘은
  • 스트림릿, 머신러닝 웹개발을 쉽고 빠르게 – techNeedle 테크니들
  • Deploying a Python machine learning app on Kubernetes - Ketch
  • Getting Started with Steamlit for Python - Build a Functioning Web App in Minutes - YouTube
  • Streamlit Tutorials - YouTube
  • streamlit #shorts - YouTube
  • How to build dashboard apps with Streamlit Python (quick Tutorial) - YouTube
  • Streamlit Tutorials - YouTube
  • How to create a Streamlit Multi-Page Web App - YouTube
  • Turn An Excel Sheet Into An Interactive Dashboard Using Python (Streamlit) - YouTube
  • How to Add a User Authentication Service (Login Form) in Streamlit + Database - YouTube
  • Build A Beautiful Machine Learning Web App With Streamlit And Scikit-learn | Python Tutorial - YouTube
  • Building A Simple SQL Playground App with Streamlit & Python - YouTube
  • Building a Machine Learning API in 15 Minutes | Coding Challenge - YouTube
  • 허깅페이스와 스트림릿으로 데이터 시각화 웹서비스 간단히 만들기
  • Streamlit - HTML, CSS, JS 없이 Python으로 웹사이트 만들기 - YouTube
  • Build A Digital Resume Using Python & Streamlit - YouTube
  • STREAMLIT Python TRICKS - make your WEB APP look BETTER in 6 Minutes |Machine Learning| Data Science - YouTube
  • Building a Dashboard web app in Python - Full Streamlit Tutorial - YouTube
  • 파이썬 스트림릿으로 데이터 대시보드 만들기 | 요즘IT
  • Introducing two new caching commands to replace st.cache!
  • streamlit-google-oauth: An example Streamlit application that incorporates Google OAuth 2.0
• TabPFN - One Model, Infinite Predictions | Prior Labs
  • TabPFN: Foundation Model for Tabular Data
  • TabPFN - 테이블 데이터를 위한 파운데이션 모델 | GeekNews
  • Tabular 데이터 파운데이션 모델. scikit-learn fit/predict 인터페이스, 분류·회귀. 합성 데이터로만 학습, 첫 사용 시 체크포인트 자동 다운로드—별도 학습 파이프라인 불필요. 전처리 없이 raw 데이터(스케일링·원핫 X), 결측치 내부 처리. GPU ~8GB VRAM+, CPU는 ~1,000 샘플 한정, TabPFN Client 클라우드 추론 제공. 배치 예측 필수(개별 호출 ~100배 느림). 최적: 100K 샘플·2K feature 미만. SHAP·이상치·합성 데이터·임베딩 추출 등 Extensions, large features/samples/small samples 특화 체크포인트. Enterprise 1천만 행·저레이턴시 distilled 추론·노코드 UX. Nature 게재, 7K stars, 3M 다운로드, TabArena 93% win rate. SAP €1B+ 4년 인수 합의. TabPFN-3는 1M rows/0.2s + Thinking mode. API/VPC/Databricks/SageMaker/Azure AI Foundry/MCP 배포. 코드 Prior Labs License(Apache 2.0+attribution), 가중치 비상업
• TensorDash A Must-Have Tool for Every Data Scientist | by Arun | Oct, 2020 | Towards Data Science
• tpot - A Python tool that automatically creates and optimizes machine learning pipelines using genetic programming. http://rhiever.github.io/tpot
• Uber
  • Meet Michelangelo: Uber’s Machine Learning Platform
  • 우버의 머신러닝 플랫폼 '미켈란젤로(Michelangelo)'의 진화
  • Productionizing Distributed XGBoost to Train Deep Tree Models with Large Data Sets at Uber

R

• Machine Learning with R: An Irresponsibly Fast Tutorial
• Essentials of Machine Learning Algorithms (with Python and R Codes)
• Image Manipulation for Machine Learning in R

Random Forests

• glouppe/phd-thesis - Repository of my thesis "Understanding Random Forests"
• Categorical Variable Encoding and Feature Importance Bias with Random Forests
• Random Forests - Leo Breiman and Adele Cutler
• The Unreasonable Effectiveness of Random Forests
• 군중은 똑똑하다 — Random Forest
• Random Forest
• The Random Forest Algorithm
• Improving the Random Forest in Python Part 1
• Hyperparameter Tuning the Random Forest in Python
• Random Forest in Python - A Practical End-to-End Machine Learning Example
• An Introduction to Random Forest using the fastai Library (Machine Learning for Programmers — Part 1)
• An Intuitive Guide to Interpret a Random Forest Model using fastai library (Machine Learning for Programmers – Part 2)
• How to use RandomForest Classifier and Regressor in Python
• An Implementation and Explanation of the Random Forest in Python
• 랜덤포레스트가 뭐길래? 회의에서 당당하게, 수식없이 알아보자
• 3.3 1/4 뉴스 토픽 분류 랜덤포레스트 교차검증 학습예측데이터셋 전처리와 나누기 - YouTube
• 3.3 2/4 뉴스 토픽 분류 랜덤포레스트 교차검증 TF-IDF 로 벡터화 하기 - YouTube
• 3.3 3/4 뉴스 토픽 분류 랜덤포레스트 교차검증 데이콘에 제출에 보기 전에 점수를 미리 알아보는 방법 - YouTube
• 3.3 4/4 뉴스 토픽 분류 랜덤포레스트 교차검증 데이콘에 제출하기 - YouTube

Reinforcement Learning

• OpenAI Gym BETA - A toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games like Pong or Go
• REINFORCEMENT LEARNING PART 1: Q-LEARNING AND EXPLORATION
• Reinforcement Learning 1 - Expected Values
• Deep Reinforcement Learning: Pong from Pixels
• Reinforcement Learning Neural Turing Machines
• Language Understanding for Text-based Games Using Deep Reinforcement Learning
• Giraffe: Using Deep Reinforcement Learning to Play Chess
• Guest Post (Part I): Demystifying Deep Reinforcement Learning
• 강화학습 튜토리알 - 인공 신경망으로 '퐁' 게임을 학습시키자 (Andrej Karpathy 포스트 번역)
• Reinforcement Learning 그리고 OpenAI - 1: OpenAI를 위한 개발환경 구축
• Reinforcement Learning 그리고 OpenAI - 2: CartPole예제 이해하기
• Reinforcement Learning 그리고 OpenAI - 3: CartPole and Deep Q Learning (1) DQN(Deep Q-Networks)
• Reinforcement Learning
• RLlib: Scalable Reinforcement Learning — Ray v1.5.2
  • 오픈소스(RLlib) 문제 발견부터 컨트리뷰션 까지 | MakinaRocks Tech Blog

Spark

• KeystoneML - Machine Learning Pipeline
• Feature Engineering at Scale With Spark
• Audience Modeling With Spark ML Pipelines
• Apache Spark로 시작하는 머신러닝 입문

SVM Support Vector Machine

• Kernel-SVM
• Support Vector Machine (SVM) Classification
• SVM: The go-to method machine learning algorithm
• Detection of Leaf and Weed using SVM | Machine Learning | Ruchi Mehra
• ThunderSVM: A Fast SVM Library on GPUs and CPUs
• 초짜 대학원생의 입장에서 이해하는 Support Vector Machine (1)
• 서포트벡터머신Support Vector Machine을 위한 비선형 계획 문제의 쌍대정리
• Support Vector Machine — Introduction to Machine Learning Algorithms
• 서포트 벡터 머신(support vector machine) 개념 정리 - 로스카츠의 AI 머신러닝
• 비지도학습 - One Class SVM - 로스카츠의 AI 머신러닝

TDD, Test

• How to unit test machine learning code
• Effective testing for machine learning systems
• Effective Testing for Machine Learning Projects - Eduardo Blancas | PyData Global 2021 - YouTube
• Shir Chorev - How to Properly Test ML Models & Data | PyData Global 2022 - YouTube
• drifter_ml - ML Testing

Theano

• Tutorial
• Practical Theano Tutorial
• brutally short intro to theano word embeddings
• practice - 윈도우에서 Theano 설치하기
• Deep Learning, Jumpto with Theano
• jaeho-kang/deep-learning
• Faster deep learning with GPUs and Theano
• Computation Graph Toolkit - I would like to announce a library that I have been working on with a few collaborators1, called the Computation Graph Toolkit (CGT)
• SymPy and Theano -- Code Generation
• SymPy and Theano -- Scalar Simplification
• SymPy and Theano -- Matrix Expressions
• Recurrent Neural Networks Tutorial, Part 2 – Implementing a RNN with Python, Numpy and Theano
• Practical Theano Tutorial
• Linear Regression In Theano
• Linear Regression(with c++)
• Logistic Regression(1)
• Multivariable Linear Regression(with c++)
• Linear Regression
• Theano-Example-James-010.ipynb
• IPython Theano Tutorials
• SPEEDING UP YOUR NEURAL NETWORK WITH THEANO AND THE GPU
• Installation of Theano on Windows
• Theano Tutorials - Ian Goodfellow
• Theano Yellow Fin
• theano-tutorial
• IPython Theano Tutorials

Torch

• Torch
• Awesome Torch - A curated list of awesome Torch tutorials, projects and communities
• A DSL for deep neural networks, supporting Caffe and Torch http://ajtulloch.github.io/dnngraph
• neural-style - Torch implementation of neural style algorithm
• Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch
• Recurrent Model of Visual Attention
• 토치(Torch7) 시작하기
• Torch 7 Deep Learning Installation (Ubuntu 14.04)
• Deep Learning with Torch The Long way of Deep Learning with Torch
• Understanding Natural Language with Deep Neural Networks Using Torch
• Generating Faces with Torch
• Lighting the way to deep machine learning
• github.com/torchnet/torchnet
• TorchCraft - Connecting Torch to StarCraft
• RNN(Recurrent Neural Network)과 Torch로 발라드곡 작사하기
• High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis
• 토치 학습 자료 한국어 번역본
• Facebook AI Research Sequence-to-Sequence Toolkit

Transfer Learning

• Transfer Learning - Machine Learning's Next Frontier
• Transfer Learning - Machine Learning's Next Frontier
• Paper is out; Transfer learning for music classification and regression tasks, and behind the scene, negative results, etc
• youtube.com/user/dvbuntu/featured Self-drinving car Transfer Learning Model
• "Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping"
  • code & data
• Transfer Learning is the new frontier. TensorFlow might help implement transfer learning
• Domain Adaptation for Visual Applications: A Comprehensive Survey
  • Transfer Learning 의 일종인 Domain Adaptation 에 대한 방대한 리뷰
• TRANSFER LEARNING FOR SOUND CLASSIFICATION
• Inception v3를 활용한 Transfer Learning
• Transfer Learning using differential learning rates
• Taskonomy: Disentangling Task Transfer Learning 리뷰
• cs231n.github.io/transfer-learning
• Hands-On Transfer Learning with Python
  • 191027 딥러닝 전이학습 발표자료
  • 전이학습

Tutorial

• Machine Learning Tutorials - YouTube
• Getting machine learning to production · Vicki Boykis
• Markus Loning - Introduction to Machine Learning with Time Series | PyData Fest Amsterdam 2020 - YouTube
• Machine Learning Algorithms For Beginners with Code Examples in Python
• Machine Learning by Analogy
• 40+ Modern Tutorials Covering All Aspects of Machine Learning - Data Science Central
• How to Learn Machine Learning Online Free in 2021?- Free Resources
• 문제해결 관점으로 머신러닝 이해하기 - YouTube
  • 유클리드소프트, '인공지능팩토리'와 '언택트 콘퍼런스' 대전러닝데이-DLD 개최
• 독학으로 머신러닝 개발자로 입문하기 완벽 정리 - YouTube
• Transitioning from Software Engineering to Machine Learning Engineering - YouTube
• No Black Box Machine Learning Course – Learn Without Libraries - YouTube
• 1시간 만에 머신 러닝 개념 따라 잡기 - YouTube
• 머신러닝야학
• AI-ML-Roadmap-from-scratch: Become skilled in Artificial Intelligence, Machine Learning, Generative AI, Deep Learning, Data Science, Natural Language Processing, Reinforcement Learning and more with this complete 0 to 100 repository
• The Complete Course Catalog | End to End Machine Learning
• EfficientML.ai Lecture, Fall 2023, MIT 6.5940 - YouTube
• machine-learning
• Machine Learning Field Guide
• Stanford CS329H: Machine Learning from Human Preferences I Autumn 2024 - YouTube