Machine Learning

Machine Learning is a relatively new branch of Statistics (at least by name), and focuses on using statisical patterns and probabilities to infer (or basically, take a highly educated guess) future patterns and/or desired program outputs, based on the statistics surrounding particular inputs.

Algorithms

Bayesian Models

Bayesian models, also known as Bayes algorithm, is a basic method of infering outputs based on known/provided inputs. It breaks up probabilities of outcomes into grids of percentage of likelihood of occurence, providing a somewhat reliable but still quite basic method to evaluate (or learn) about what outputs are best for a given situation.

Neural Networks

• Neural Net for Handwritten Digit Recognition in JavaScript: http://myselph.de/neuralNet.html
• Machine (Re)Learning -- Neural Networks From Scratch: http://dzone.com/articles/machine-relearning-neural-networks-from-scratch | SRC (examples use Scala)

Transformers

• wikipedia: Transformer (machine learning model)

Tools

• Machine Learning Open Source Software (MLOSS): https://mloss.org/software/
• TensorFlow: http://www.tensorflow.org/ (Open Source Software Library for Machine Intelligence)
• BigML: https://bigml.com/

Weka

Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.

• Weka 3 -- Data Mining & Machine Learning Software in Java: http://www.cs.waikato.ac.nz/ml/weka/

Zeitgeist

As a tool for providing desktop activity awareness, Zeitgeist runs as a service which logs users's activities and events, anywhere from files opened to websites visited and conversations.

• Zeitgeist project: http://zeitgeist-project.com/

Google

Cloud ML

• Google Cloud - Machine Learning: https://cloud.google.com/ml/

PredictionIO

• Prediction.IO: http://prediction.io/ (Machine Learning library)
• Prediction.IO tutorial - Food Delivery: http://blog.prediction.io/food-delivery-apps-meet-machine-learning-tutorial-sample-code/#.UnJm-RA2Y1p
• Prediction.IO tutorial - Courseware Class Discovery: http://blog.prediction.io/build-discovery-feature-course-platforms-predictionio/#.UnJm-xA2Y1p

Seldon

• Seldon: https://docs.seldon.io/ | DOCS | SRC

Resources

• ML Class: http://www.ml-class.com/ (offered by Stanford University - Dept. of Computer Science, Fall 2011)
• Kaggle: https://www.kaggle.com/ (community around selecting the best ML algorithm for each type of data/use-case)
• Packt -- Java Deep Learning Essentials cookbook (BOOK): https://www.packtpub.com/big-data-and-business-intelligence/java-deep-learning-essentials | SRC
• Packt -- Java Deep Learning Cookbook (BOOK): https://www.packtpub.com/big-data-and-business-intelligence/java-deep-learning-projects | SRC

• Deep Java Library (DJL): https://javadoc.djl.ai/
• Java Machine Learning Library (Java-ML): http://java-ml.sourceforge.net/
• UCI - Machine Learning datasets: http://archive.ics.uci.edu/ml/datasets.html
• PHP Math: http://www.phpmath.com (Machine Learning and Data Mining code examples in PHP)
• Vector & Matrix Math for JS: http://sylvester.jcoglan.com/ (can be used for most matrix-based Machine Learning use cases)

Tutorials

• Multiply a Matrix: http://www.mathwarehouse.com/algebra/matrix/multiply-matrix.php
• Multiplying Matrices: http://www.intmath.com/matrices-determinants/4-multiplying-matrices.php
• PHP Array functions: http://oreilly.com/catalog/progphp/chapter/ch05.html
• Implement Bayesian inference using PHP, Part 1: http://www.ibm.com/developerworks/web/library/wa-bayes1/
• Implement Bayesian inference using PHP, Part 2: http://www.ibm.com/developerworks/web/library/wa-bayes2/
• Predicting the 2017 Oscar Winners With BigML: http://dzone.com/articles/predicting-the-2017-oscar-winners
• Embracing Machine Learning: How to Get 2 Steps Ahead of Everyone Else: https://dzone.com/articles/embracing-machine-learning-how-to-get-2-steps-ahea
• An Introduction to TensorFlow: https://dzone.com/articles/tensorflow-simplified-examples
• Data Science for Java Developers With Tablesaw: https://dzone.com/articles/learn-data-science-with-java-and-tablesaw
• A Machine Learning Approach — Building a Hotel Recommendation Engine: https://towardsdatascience.com/a-machine-learning-approach-building-a-hotel-recommendation-engine-6812bfd53f50
• Intro to Machine Learning for Developers: https://dzone.com/articles/deep-intro-to-machine-learning-for-developers
• How to build your own Neural Network from scratch in Python: https://towardsdatascience.com/how-to-build-your-own-neural-network-from-scratch-in-python-68998a08e4f6
• Machine Learning in Java with "Amazon Deep" Java Library: https://www.infoq.com/articles/java-machine-learning-djl/

External Links

• wikipedia: Machine learning
• wikipedia: List of machine learning algorithms
• wikipedia: Classification (machine learning)
• wikipedia: Zipf's law
• Machine learning -- The smart person's guide: http://www.techrepublic.com/article/machine-learning-the-smart-persons-guide/
• Statistics vs. Machine Learning, fight! (also, ML nomenclature’s original sin): http://anyall.org/blog/2008/12/statistics-vs-machine-learning-fight-also-ml-nomenclatures-original-sin/
• Thinking Machine: http://hoowstuffworks.blogspot.com/2010/02/thinking-machine.html
• Machine Learning with Linear Model : http://horicky.blogspot.com/2009/11/machine-learning-with-linear-model.html
• Has Ayasdi turned machine learning into a magic bullet?: http://gigaom.com/2013/01/16/has-ayasdi-turned-machine-learning-into-a-magic-bullet/
• Big Data & Machine Learning Convergence: http://java.dzone.com/articles/big-data-machine-learning
• Machine language - how Siri found its voice: http://www.theverge.com/2013/9/17/4596374/machine-language-how-siri-found-its-voice
• On Chomsky and the Two Cultures of Statistical Learning: http://norvig.com/chomsky.html
• Machine Learning: The Bigger Picture, Part I: http://dzone.com/articles/machine-learning-the-bigger-picture-part-i
• Machine Learning - The Bigger Picture, Part II: http://dzone.com/articles/machine-learning-the-bigger-picture-part-ii
• Should Amazon be your AI & Machine Learning platform?: http://www.zdnet.com/article/should-amazon-be-your-ai-and-machine-learning-platform/
• 3 Machine Learning Algorithms You Need to Know : https://dzone.com/articles/3-machine-learning-algorithms-you-need-to-know (Regression Models, Decision Trees, Clustering/Classification)
• Deep Dive into Machine Learning history & approaches: https://dzone.com/articles/deep-dive-into-machine-learning
• Top 10 Machine Learning Use Cases: https://dzone.com/articles/top-10-machine-learning-use-cases-part-3
• 10 Machine Learning Algorithms You Should Know to Become a Data Scientist: https://dzone.com/articles/ten-machine-learning-algorithms-you-should-know-to
• Machine Learning for Humans: https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12
• What If... you could inspect a machine learning model: https://pair-code.github.io/what-if-tool/
• Machine Learning in Java with Amazon Deep Java Library: https://www.infoq.com/articles/java-machine-learning-djl/
• AutoKeras -- The Killer of Google’s AutoML: https://towardsdatascience.com/autokeras-the-killer-of-googles-automl-9e84c552a319
• Why machine learning models fail: https://sdtimes.com/ai/why-machine-learning-models-fail/

See Also

Statistics | AI | Robotics | Semantic Web | Big Data | Data Science | Analytics | Recommender Systems | Personalization | Advertising | Marketing