Cool, this is a helpful intro. Anyone have any recommended reading for ML in a JVM context?
The Skymind.io co-founders wrote a book that references their open-source "deep learning for Java and Scala framework"[1]. They are in the YC16 batch.
Introduction to Machine Learning for Developers
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Re: Introduction to Machine Learning for Developers
#12I cannot recommend scikit-learn enough to anyone interested in machine learning who likes python. I have been working with it for part of my Thesis, and it can do so much, with so little code. It is amazing.
I second this.
I've been somewhat addicted to HackerRank challenges over the last couple of weeks. Why is not important, don't judge :)
The python packages and tooling around learning and science are truly amazing. Try and do the Craigslist category classification without using python and see what I mean.
Re: Introduction to Machine Learning for Developers
#13I've made a similar list for economists. It included a list of practical applications of ML. Developers can get a sense of what the discipline can do before jumping in. APPLIED MACHINE LEARNING CASES ## Business 1. Kaggle, Data Science Use cases. An outline of business applications. Few companies have the data to implement these things. https://www.kaggle.com/wiki/DataScienceUseCases 2. Kaggle, Competitions. (Make su…
I recently started playing around with the data sets on past Kaggle competitions and have been learning a lot.
The Data Science use cases there are quite interesting. Are there any publicly available data-sets (other than the ones available in competitions) to work with (especially for the marketing use cases)?
Re: Introduction to Machine Learning for Developers
#14Not even a mention of TensorFlow or Torch?
Re: Introduction to Machine Learning for Developers
#15Although I did my studies with NLTK, it looks like spaCy has stepped up to the plate, particular with NLP related tasks.
Worth checking out, in parallel to or in place of NLTK: https://spacy.io
Re: Introduction to Machine Learning for Developers
#16Re: Introduction to Machine Learning for Developers
#17Cool, this is a helpful intro. Anyone have any recommended reading for ML in a JVM context?
Hi, skymind cofounder here. To offer some context on our book there: We have appendixes covering some of the fundamental concepts such as linear algebra and statistics.
For other machine learning libraries in java:
Re: Introduction to Machine Learning for Developers
#18Although I did my studies with NLTK, it looks like spaCy has stepped up to the plate, particular with NLP related tasks. Worth checking out, in parallel to or in place of NLTK: https://spacy.io
Never heard of it. Thanks for the pointer!
Re: Introduction to Machine Learning for Developers
#19I cannot recommend scikit-learn enough to anyone interested in machine learning who likes python. I have been working with it for part of my Thesis, and it can do so much, with so little code. It is amazing.
If anyone is interested in learning more about scikit-learn, I'd recommend "Hands-On Machine Learning with Scikit-Learn and Tensorflow" from O'Reilly:
http://shop.oreilly.com/product/0636920052289.do
When I first started using scikit-learn, I was overwhelmed with the number of classes and options available. I just chose some basic classifiers I was familiar with and stuck with most of the default settings. The book explains many of the other models and when they would be useful, but also spends a lot of time exploring the datasets (using pandas), preprocessing data and building data pipelines, finding the best hyperparameters, best ways to evaluate a models performance, etc. The library feels less like a big bag of algorithms now and more like a cohesive data pipeline.
Re: Introduction to Machine Learning for Developers
#20I cannot recommend scikit-learn enough to anyone interested in machine learning who likes python. I have been working with it for part of my Thesis, and it can do so much, with so little code. It is amazing.
If anyone is interested in learning more about scikit-learn, I'd recommend "Hands-On Machine Learning with Scikit-Learn and Tensorflow" from O'Reilly: http://shop.oreilly.com/product/0636920052289.do When I first started using scikit-learn, I was overwhelmed with the number of classes and options available. I just chose some basic classifiers I was familiar with and stuck with most of the default settings. The book e…
There's also o'reilly's 'Introduction to Machine Learning with Python', which is very much about scikit-learn
http://shop.oreilly.com/product/0636920030515.do
Just came out a few weeks ago so it's relatively unknown, I've read the first few chapters and so far it's good! The first author Andreas Mueller is one of the scikit core devs.
The Jupyter notebooks including the book's code are on GitHub: https://github.com/amueller/introduction_to_ml_with_python