insomniac-klutz/Machine-Learning-Resources

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

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