mayrop/machine-learning-resources

🚀 Where to Start Practicing?

If you can not wait to start practicing, you can start doing so at:

• Titanic: Machine Learning from Disaster (Kaggle). Excellent free challenge to start practicing classification in machine learning.
• Gym: Reinforcement Learning Toolkit Nice toolkit to get started and practice Reinforcement Learning.
• Neural Network Playground Visualization of how Neural Networks work.

📹 Courses

• Machine Learning Crash Course (by Google)
• Machine Learning Problem Framing from Google (by Google)
• Coursera Course: Machine Learning (by Dr. Andrew Ng)
• Start Training on Machine Learning with AWS (by AWS)
• AI For Everyone (by Dr. Andrew Ng)
• Statistics and probability (Khan Academy). Excellent free source to get started on statistics and probablity.
• Linear Algebra (Khan Academy)

YouTube Channels

• StatQuest with Josh Starmer. StatQuest breaks down complicated Statistics and Machine Learning methods into small, bite-sized pieces that are easy to understand.
• Statistics 110 (Course by Harvard University)
• 3Blue1Brown. 3blue1brown, by Grant Sanderson, is some combination of math and entertainment, depending on your disposition.

📚 Books

Introductory level

• A First Course in Machine Learning (Chapman and Hall, by Simon Rogers and Mark Girolami)
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (O'Reilly, by Aurélien Géron)
• Machine Learning For Absolute Beginners: A Plain English Introduction (by Oliver Theobald)

Intermediary/Advanced level

• Pattern Recognition and Machine Learning (by Christopher Bishop)
• Artificial Intelligence: A Modern Approach (Pearson, By Stuart J. Russell, Stuart Jonathan Russell, Peter Norvig, Ernest Davis)
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer, by Trevor Hastie, Robert Tibshirani and Jerome Friedman)
• Advanced R

🏫 Math

• Standard Deviation Visual (Video)
• Random Variables and Distrubutions
  • Find Percentiles for a General Continuous Random Variable (Video)
  • Probability Density Functions / Continuous Random Variables
  • Applied Multivariate Statistical Analysis (Course by PennState Eberly College of Science)
  • Bivariate normal distribution conditional distributions (Video)
  • Poisson Distribution (PDF)
  • Approximating a Binomial Prob Distribution using a Normal Distribution (Video Part 1) (Part 2)
  • Continuity Corrections (Video)
  • How to do Normal Distributions Calculations (Video)
• Fourier Transformation for a Data Scientist

💻 Programming

• Learn R, in R.
• R Tutorial - A Beginner's Guide: Link 1, Link2
• 11 Beginner Tips for Learning Python Programming (Article)
• Python Libraries for Interpretable Machine Learning

💻 Tutorials

• How to Create State and County Maps Easily in R
• Setup-Random-Seeds-on-Caret-Package
• Logistic Regression in R Tutorial
• Cross-Validation for Predictive Analytics Using R
• K-Means Clustering in R Tutorial
• Improve Your Model Performance using Cross Validation (in Python and R)
• Accuracy and Errors for Models
• Penalized Logistic Regression Essentials in R: Ridge, Lasso and Elastic Net

💻 Libraries

• TensorFlow
• Theano
• Scikit-learn

🏫 Schools

México.

• http://inteligenciafutura.mx/
• Maestría en Cómputo Estadístico

Data Privacy

México

• Registro Público de Usuarios (REUS)

Europe

• GDPR

United States

• CCPA California Consumer Privacy Act

Blogs and Articles

• Lexalytics Blog

Publications

Classification

• Norton, M. & Uryasev, S. (2019). Maximization of auc and buffered auc in binary clas- sification. Mathematical Programming.
• Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on computational learning theory.
• Tharwat, A. (2018). Classification assessment methods. Applied Computing and Informatics.
• De Siqueira Santos, S., Takahashi, D. Y., Nakata, A., & Fujita, A. (2013). A comparative study of statistical methods used to identify dependencies between gene expression signals. Briefings in Bioinformatics.
• Dietterich, T. G. (1998). Approximate statistical tests for comparing supervised classification learning algorithms.
• Kotsiantis, S. B. (2007). Supervised machine learning: A review of classification tech- niques.
• Kuncheva, L. I. (2004). Combining pattern classifiers: Methods and algorithms. New York, NY, USA: Wiley-Interscience.

Logistic Regression

• Stoltzfus, J. C. (2011). Logistic regression: A brief primer.
• Lee, S.-I., Lee, H., Abbeel, P., & Ng, A. Y. (2006). Efficient l1 regularized logistic regression.
• Peduzzi, P., Concato, J. P., Kemper, E., Holford, T., & Feinstein, A. R. (1996). A sim- ulation study of the number of events per variable in logistic regression analysis.

Support Vector Machines

• Cortes, C. & Vapnik, V. (1995). Support-vector networks.
• R. T. (2016). Control-group feature normalization for multivariate pattern analysis of structural mri data using the support vector machine.
• Hsu, C.-W., Chang, C.-C., & Lin, C.-J. (2003). A practical guide to support vector clas- sification.

Decision Trees and Random Forests

• Murthy, S. K. (1998). Automatic construction of decision trees from data: A multi- disciplinary survey.
• Rokach, L. & Maimon, O. (2005). Top-down induction of decision trees classifiers - a survey.
• Breiman, L. (2001). Random Forests.
• Cutler, D. R., Edwards Jr., T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J. (2007). Random forests for classification in ecology.
• Louppe, G., Wehenkel, L., Sutera, A., & Geurts, P. (2013). Understanding variable importances in forests of randomized trees.
• Probst, P., Wright, M. N., & Boulesteix, A.-L. (2019). Hyperparameters and tuning strategies for random forest.

Data Imbalance

• Chawla, N. V. (2005). Data mining for imbalanced datasets: An overview. In O. Maimon & L. Rokach (Eds.), Data mining and knowledge discovery handbook. https://doi:10.1007/0-387-25465-X_40
• Ganganwar, V. (2012). An overview of classification algorithms for imbalanced datasets. Retrieved from www.ijetae.com

Collinearity

• Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Lautenbach, S. (2013). Collinearity: A review of methods to deal with it and a simulation study evaluating their performance.

Evaluation

• Raschka, S. (2018). Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning
• Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection.
• Spearman, C. (1904). "General Intelligence," objectively determined and measured.

Spatial Statistics

• Pebesma, E. (2018). Simple Features for R: Standardized Support for Spatial Vector Data.

Other

• Cochran, W. G. (1950). The comparison of percentages in matches samples.
• Hoeffding, W. (1948). A non-parametric test of independence.
• Samuels, M. L. (1993). Simpson’s paradox and related phenomena.
• Edwards, A. L. (1948). Note on the “correction for continuity” in testing the significance of the difference between correlated proportions.
• Downar, L. & Duivesteijn, W. (2017). Exceptionally monotone models - the rank correlation model class for exceptional model mining
• McNemar, Q. (1947). Note on the sampling error of the difference between correlated proportions or percentages.