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Machine Learning

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Jupyter Notebook

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  1. Timing and Profiling in IPython - PyNash

Devop

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  1. Managing Containerized Data Pipeline Dependencies With Luigi — AdRoll
  2. Containerized Data Science and Engineering - Part 2, Dockerized Data Science
  3. 深度 | 从硬件到软件：OpenAI 解读自家的深度学习基础架构

Model Interpretability

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  1. Ideas on interpreting machine learning - O'Reilly Media
  2. marcotcr/lime: Lime: Explaining the predictions of any machine learning classifier

Sentiment Analysis

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  1. monkeylearn/hotel-review-analysis: Sentiment analysis and aspect classification for hot...

Model Evaluation

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  1. 分类之性能评估指标 – 我爱机器学习
  2. 1606.01885v1.pdf
  3. Lift analysis - A data scientist's secret weapon
  4. Evaluation Metrics - MLlib - Spark 1.5.2 Documentation
  5. Assessing and Comparing Classifier Performance with ROC Curves - Machine Learning Mastery
  6. Classification Accuracy is Not Enough: More Performance Measures You Can Use - Machine ...
  7. 3 more

Feature Engineering

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  1. (1) Predictive Modeling: How can you perform accurate feature engineering/extraction? -...
  2. Santander Customer Satisfaction | Kaggle
  3. Discover Feature Engineering, How to Engineer Features and How to Get Good at It - Mach...
  4. ŷhat | Data Normalization in Python

KNN

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  1. Machine Learning in Action(1) K-近邻 - kobeshow - 博客园

Clustering

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  1. gallmerci/bico: BICO is a fast streaming algorithm and reduction technique for the k-me...
  2. data visualization - How to interpret mean of Silhouette plot? - Cross Validated
  3. pyclustering/pyclustering/cluster/examples at master · annoviko/pyclustering
  4. bayesian - PyMC for nonparametric clustering: Dirichlet process to estimate Gaussian mi...
  5. clustering - K-means incoherent behaviour choosing K with Elbow method, BIC, variance e...
  6. Finding the K in K-Means Clustering | The Data Science Lab
  7. 12 more

Logistic Regression

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  1. Logistic Regression Theory | D.W's Notes - Machine Learning
  2. scala - Spark: How to run logistic regression using only some features from LabeledPoin...
  3. spark/LogisticRegressionExample.scala at master · apache/spark
  4. Logistic Regression Analysis – Welcome | LogisticRegressionAnalysis.com | Fast, easy gu...

Maths

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  1. Correlation and coherence, what’s the difference? | CHRIS HOLDGRAF
  2. Applications of Linear Algebra Part 2 | edX

Bayesian

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  1. Agile Java Man: Probabilistic Programming - Part 1
  2. markdregan/Bayesian-Modelling-in-Python: A python tutorial on bayesian modeling techniq...
  3. GitHub - blei-lab/edward: A library for probabilistic modeling, inference, and criticis...
  4. 粗糙的贝叶斯转化概率预测模型
  5. Bayesian network 与python概率编程实战入门 | 好东西论坛
  6. justmarkham/kaggle-pycon-2015
  7. 10 more

Recommnder

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  1. a1-koren.pdf
  2. Netflix推荐系统的最新解读：算法、商业价值与创新 [1]
  3. Cosine similarity, Pearson correlation, and OLS coefficients | AI and Social Science – ...
  4. Algorithsm :: Item-based collaborative filtering
  5. Similarity Functions for User-User Collaborative Filtering | GroupLens
  6. www.mmds.org/mmds/v2.1/ch09-recsys2.pdf
  7. 5 more

Time Series

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  1. Time Series
  2. Time series cross-validation: an R example | Hyndsight
  3. nbviewer.ipython.org/gist/twiecki/3962843
  4. nbviewer.ipython.org/github/changhiskhan/talks/blob/master/pydata2012/pandas_timeseries...
  5. am207.org

Learning

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  1. Scikit_Learn_Cheat_Sheet_Python.pdf
  2. Chris Albon
  3. 讲你肯定能懂的机器学习多维极值求解
  4. 机器学习代码心得之​有监督学习的模块 - 文章
  5. GitHub - ujjwalkarn/DataSciencePython: common data analysis and machine learning tasks ...
  6. The Best Machine Learning Libraries in Python
  7. 17 more

Model Ensemble

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  1. 5 Easy Questions on Ensemble Modeling everyone should know
  2. ensemble - Bagging, boosting and stacking in machine learning - Cross Validated
  3. Kaggle Ensembling Guide | MLWave
  4. Using Ensembles in Kaggle Data Science Competitions- Part 3
  5. kaggle_pbr/blend.py at master · emanuele/kaggle_pbr
  6. Want to Win at Kaggle? Pay Attention to Your Ensembles. - Data Science Central
  7. 1 more

Dimension Reduce

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  1. Visualising high dimensional data
  2. Feature Reduction using SVD
  3. 浅谈高维数据可视化中的降维方法 | Chih-Hao's Blog
  4. Some useful resources on understanding PCA | CHRIS HOLDGRAF

Linear Regression

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  1. Combining linear models | CHRIS HOLDGRAF
  2. people.duke.edu/~rnau/Notes_on_linear_regression_analysis--Robert_Nau.pdf
  3. people.duke.edu/~rnau/Review_of_basic_statistics_and_the_mean_model_for_forecasting--Ro...
  4. nbviewer.ipython.org/github/jdwittenauer/ipython-notebooks/blob/master/ML-Exercise1.ipynb
  5. nbviewer.ipython.org/github/nborwankar/LearnDataScience/blob/master/notebooks/A3. Linea...
  6. nbviewer.ipython.org/github/justmarkham/DAT4/blob/master/notebooks/08_linear_regression...
  7. 4 more

Data Pipeline

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  1. skutil/skutil grid demo.ipynb at master · tgsmith61591/skutil
  2. Using FunctionTransformer to select columns — scikit-learn 0.17.1 documentation
  3. sklearn-pandas 1.1.0 : Python Package Index
  4. Creating your own estimator in scikit-learn
  5. Building a custom Python scikit-learn transformer for machine learning. | Open Device, ...
  6. Using scikit-learn Pipelines and FeatureUnions | zacstewart.com
  7. 3 more

Decision Tree

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  1. spark/DecisionTreeExample.scala at master · apache/spark
  2. Gradient Boosting: Analysis of LendingClub’s Data – Kevin Davenport

xgboost

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  1. Complete Guide to Parameter Tuning in XGBoost (with codes in Python)
  2. xgboost/demo/guide-python at master · dmlc/xgboost · GitHub
  3. Santander Customer Satisfaction | Kaggle

PFA

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  1. PluginSDK/alpine-model-pack/src/test/scala/com/alpine/model/export/pfa/modelconverters ...
  2. How to Lower the Cost of Deploying Analytics: An Introduction to the …
  3. titus 0.8.4 : Python Package Index
  4. Home · opendatagroup/hadrian Wiki
  5. adp0884-pivarskiAcb.pdf

Survival Analysis

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  1. Survival Analysis as a Data Science Project (pt 4)
  2. Jupyter Notebook Viewer
  3. Thesis Ramon Man.pdf
  4. A Comprehensive guide to Parametric Survival Analysis
  5. Kaplan-Meier Curve in Python - ElasticMining
  6. Survival analysis in general data science projects
  7. 6 more

Pandas

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  1. DatasFrame
  2. Pandas Categoricals

Learning Guide

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  1. 分布式机器学习故事分享
  2. A Complete Guide on Getting Started with Deep Learning in Python
  3. An overview of gradient descent optimization algorithms
  4. Machine Learning Done Wrong - ML in the Valley
  5. Building a data science portfolio: Storytelling with data
  6. NSSD Podcast | Free Listening on SoundCloud
  7. 2 more

Regression

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  1. Example of logistic regression in Python using scikit-learn
  2. Logistic Regression 模型简介 | 数盟

Feature Selection

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  1. Feature Selection with scikit-learn | Data Science, Python, Games
  2. Selecting good features – Part I: univariate selection | Diving into data
  3. Algorithms | Feature Selection @ ASU
  4. Selecting good features – Part III: random forests | Diving into data
  5. Selecting good features – Part II: linear models and regularization | Diving into data
  6. Selecting good features – Part IV: stability selection, RFE and everything side by side...
  7. 2 more

Generate Fake Data

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  1. Academic Torrents
  2. RANDOM.ORG - True Random Number Service
  3. tdunning/log-synth: Generates more or less realistic log data for testing simple aggreg...
  4. 4. Fake Data Gives Real Answers - Sharing Big Data Safely [Book]
  5. Welcome to Faker’s documentation! — Faker 0.5.3 documentation
  6. fake-data-generator/src at master · thinkh/fake-data-generator
  7. 2 more
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