Machine-Learning-Resources
Neural Networks
- Regularization with TensorFlow: http://www.ritchieng.com/machine-learning/deep-learning/tensorflow/regularization/
- Keras Simple CNN Starter | Kaggle: https://www.kaggle.com/CVxTz/keras-simple-cnn-starter
- Neural Networks and Toddlers: How Learning Biases Can Improve Word Learning: https://medium.com/center-for-data-science/neural-networks-and-toddlers-how-learning-biases-can-improve-word-learning-56e477dc1ee3
- CNN with Keras | Kaggle: https://www.kaggle.com/bugraokcu/cnn-with-keras
- Credit Card Fraud Detection using Autoencoders in Keras — TensorFlow for Hackers (Part VII): https://medium.com/@curiousily/credit-card-fraud-detection-using-autoencoders-in-keras-tensorflow-for-hackers-part-vii-20e0c85301bd
- Models/object_detection_tutorial.ipynb at master · tensorflow/models · GitHub: https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb
- Model exploration (shallow NN with embeddings) | Kaggle: https://www.kaggle.com/jeremytjordan/model-exploration-shallow-nn-with-embeddings
- Predicting Fraud with TensorFlow | Kaggle: https://www.kaggle.com/currie32/predicting-fraud-with-tensorflow
- Transformers | Harvard NLP, The Annotated Transformer: http://nlp.seas.harvard.edu/2018/04/03/attention.html
Tensorflow
- Using Tensorflow and Support Vector Machine to Create an Image Classifications Engine: https://code.oursky.com/tensorflow-svm-image-classifications-engine/
- A Guide to TF Layers: Building a Convolutional Neural Network | TensorFlow: https://www.tensorflow.org/tutorials/layers
- TensorFlow for R: https://tensorflow.rstudio.com/
- Python TensorFlow Tutorial - Build a Neural Network: http://adventuresinmachinelearning.com/python-tensorflow-tutorial/
- TensorFlow and deep learning, without a PhD: https://codelabs.developers.google.com/codelabs/cloud-tensorflow-mnist/#0
Scikit
- Machine Learning Algorithm Recipes in scikit-learn: https://machinelearningmastery.com/get-your-hands-dirty-with-scikit-learn-now/
- Building Random Forest Classifier with Python Scikit learn: http://dataaspirant.com/2017/06/26/random-forest-classifier-python-scikit-learn/
- Learning Curves and Validation Curves in Scikit-Learn: http://sdsawtelle.github.io/blog/output/week6-andrew-ng-machine-learning-with-python.html
- Validation curves: plotting scores to evaluate models — scikit-learn 0.19.1 documentation: http://scikit-learn.org/stable/modules/learning_curve.html
Ensembling-Stacking
- ML-Ensemble: Scikit-learn style ensemble learning | Kaggle: https://www.kaggle.com/flennerhag/ml-ensemble-scikit-learn-style-ensemble-learning
- Ensemble Machine Learning Algorithms in Python with scikit-learn: https://machinelearningmastery.com/ensemble-machine-learning-algorithms-python-scikit-learn/
- Kaggle Ensembling Guide | MLWave: https://mlwave.com/kaggle-ensembling-guide/
- Stacked Ensembles — H2O 3.18.0.5 documentation: http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/stacked-ensembles.html
- How to Rank 10% in Your First Kaggle Competition | Wille: https://dnc1994.com/2016/05/rank-10-percent-in-first-kaggle-competition-en/
- Ensemble models Berkeley: https://www.stat.berkeley.edu/~ledell/docs/dlab_ensembles.pdf
- h2o-tutorials/ensembles: https://github.com/h2oai/h2o-tutorials/blob/master/tutorials/ensembles-stacking/ensembles-stacking.R
- Stacking models from different packages - Stack Overflow: https://stackoverflow.com/questions/47060233/stacking-models-from-different-packages
- A Brief Introduction to caretEnsemble: https://cran.r-project.org/web/packages/caretEnsemble/vignettes/caretEnsemble-intro.html
- Stacking in Machine Learning: http://supunsetunga.blogspot.com/2016/06/stacking-in-machine-learning.html
- Ensemble Learning to Improve Machine Learning Results: https://blog.statsbot.co/ensemble-learning-d1dcd548e936
- How to Build an Ensemble Of Machine Learning Algorithms in R: https://machinelearningmastery.com/machine-learning-ensembles-with-r/
Tutorials-Courses
- Learn | Kaggle: https://www.kaggle.com/learn/machine-learning
- How to Win a Data Science Competition: Learn from Top Kagglers: https://www.coursera.org/learn/competitive-data-science
- A/B Testing | Udacity: https://www.udacity.com/course/ab-testing--ud257
- 10701 Introduction to Machine Learning: http://www.cs.cmu.edu/%7E./10701/lectures.html
- Statistical Learning | Stanford Lagunita: https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about
- In-depth introduction to machine learning in 15 hours of expert videos: https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/
- Deep Learning For Coders—36 hours of lessons for free: http://course.fast.ai/lessons/lesson1.html
- Your First Machine Learning Project in Python Step-By-Step: https://machinelearningmastery.com/machine-learning-in-python-step-by-step/
- Machine Learning with R: An Irresponsibly Fast Tutorial: http://will-stanton.com/machine-learning-with-r-an-irresponsibly-fast-tutorial/
General Machine Learning
- How to handle Imbalanced Classification Problems in machine learning?: https://www.analyticsvidhya.com/blog/2017/03/imbalanced-classification-problem/
- Learning from Imbalanced Classes: https://svds.com/learning-imbalanced-classes/
- Paper Threshold Unbalanced Data: https://www3.nd.edu/~rjohns15/content/papers/ssci2015_calibrating.pdf
- Approaching (Almost) Any Machine Learning Problem: http://blog.kaggle.com/2016/07/21/approaching-almost-any-machine-learning-problem-abhishek-thakur/
- ROC curves and Area Under the Curve explained (video): http://www.dataschool.io/roc-curves-and-auc-explained/
- Explaining precision and recall – Andreas Klintberg – Medium: https://medium.com/@klintcho/explaining-precision-and-recall-c770eb9c69e9
- Gradient Boosting Explained | GormAnalysis: https://gormanalysis.com/gradient-boosting-explained/
- Gradient Boosting Explained: http://www.ccs.neu.edu/home/vip/teach/MLcourse/4_boosting/slides/gradient_boosting.pdf
- Are categorical variables getting lost in your random forests: http://roamanalytics.com/2016/10/28/are-categorical-variables-getting-lost-in-your-random-forests/
- Which algorithm takes the crown: Light GBM vs XGBOOST?: https://www.analyticsvidhya.com/blog/2017/06/which-algorithm-takes-the-crown-light-gbm-vs-xgboost/
- Artificial Intelligence in Motion: Machine Learning with Python - Logistic Regression: http://aimotion.blogspot.com/2011/11/machine-learning-with-python-logistic.html
- Classification using Decision Trees in R: http://en.proft.me/2016/11/9/classification-using-decision-trees-r/
- Titanic: Getting Started With R - Part 3: Decision Trees: http://trevorstephens.com/kaggle-titanic-tutorial/r-part-3-decision-trees/
- How To Implement The Decision Tree Algorithm From Scratch In Python: https://machinelearningmastery.com/implement-decision-tree-algorithm-scratch-python/
- How to use XGBoost algorithm in R in easy steps: https://www.analyticsvidhya.com/blog/2016/01/xgboost-algorithm-easy-steps/
- Chatbots with Machine Learning: Building Neural Conversational Agents: https://blog.statsbot.co/chatbots-machine-learning-e83698b1a91e
- SVM: http://cs229.stanford.edu/notes/cs229-notes3.pdf
- Understanding Support Vector Machine algorithm: https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/
Encoding
- Categorical_encoding — H2O 3.18.0.5 documentation: http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/categorical_encoding.html
- Impact Encoding: https://www.reddit.com/r/MachineLearning/comments/69txzx/d_high_cardinality_categorical_variable_encoding/
- Python target encoding for categorical features | Kaggle: https://www.kaggle.com/ogrellier/python-target-encoding-for-categorical-features
- Feature Hashing example: Using categorical data in machine learning with python: https://blog.myyellowroad.com/using-categorical-data-in-machine-learning-with-python-from-dummy-variables-to-deep-category-66041f734512
Spark ML
- Multi-Class Text Classification with PySpark: https://datascienceplus.com/multi-class-text-classification-with-pyspark/
- Apache Spark Tutorial: Machine Learning (article): https://www.datacamp.com/community/tutorials/apache-spark-tutorial-machine-learning
- Movie recommender system with Spark: https://datascience.ibm.com/exchange/public/entry/view/99b857815e69353c04d95daefb3b91fa
Time Series
- GitHub - blue-yonder/tsfresh: Automatic extraction of relevant features from time series: https://github.com/blue-yonder/tsfresh
- Features for time series classification: https://stats.stackexchange.com/questions/50807/features-for-time-series-classification
- Complete guide to create a Time Series Forecast (with Codes in Python): https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/
Books
- Bishop-Pattern Recognition: https://pdfs.semanticscholar.org/f9b5/c4fcb8d4f0571f437b001d464c128f24265a.pdf
- Introduction to Statistical Learning: http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf
Datasets
- Datasets | DePaul University - Center for Data Mining & Predictive Analytics: http://dampa.cdm.depaul.edu/resources/datasets/
Miscelaneous
- Tips and tricks to win kaggle data science competitions: https://www.slideshare.net/DariusBaruauskas/tips-and-tricks-to-win-kaggle-data-science-competitions
- How to Rank 10% in Your First Kaggle Competition | Wille: https://dnc1994.com/2016/05/rank-10-percent-in-first-kaggle-competition-en/
- My review of Microsoft’s data science virtual machine (DSVM) for deep learning: https://www.pyimagesearch.com/2018/03/21/my-review-of-microsofts-deep-learning-virtual-machine/
- Onepanel - Machine Learning Platform on Cloud GPUs: https://www.onepanel.io/#
- 18 places to find data sets for data science projects: https://www.dataquest.io/blog/free-datasets-for-projects/
- 120 Machine Learning business ideas from the latest McKinsey report: https://medium.com/@thoszymkowiak/120-machine-learning-business-ideas-from-the-new-mckinsey-report-b81b239f336