To my JVM friends looking to explore Machine Learning techniques - you don’t necessarily have to learn Python to do that. There are libraries you can use from the comfort of your JVM environment. đŸ§”đŸ‘‡
https://github.com/oracle/tribuo  a machine learning library in Java that provides multi-class classification, regression, clustering, anomaly detection and multi-label classification.
https://spark.apache.org/mllib/ : ML algorithms, feature preprocessing and pipelines. Scalable through distributed computations.
https://opennlp.apache.org : The toolkit for common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, coreference resolution, language detection and more!
https://github.com/apache/mahout : distributed linear algebra framework and mathematically expressive Scala DSL designed to let mathematicians, statisticians, and data scientists quickly implement their own algorithms.
https://github.com/haifengl/smile  : Statistical Machine Intelligence and Learning Engine: classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithms, missing value imputation, nearest neighbor search..
https://github.com/breandan/kotlingrad Kotlin∇ is a type-safe automatic differentiation framework in Kotlin. It allows users to express differentiable programs with higher-dimensional data structures and operators.
https://github.com/awslabs/djl  open-source, high-level, engine-agnostic Java framework for deep learning. DJL is designed to be easy to get started with and simple to use for Java developers.
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