Introduction to Machine Learning for Developers

#1

Post by erinjerri1678 » Thu, Nov 10, 2016, 8:29 PM UTC

Introduction to Machine Learning for Developers
blog.algorithmia.com

Re: Introduction to Machine Learning for Developers

#2

Post by adamnemecek » Thu, Nov 10, 2016, 9:23 PM UTC

For anyone trying to get into the field, I put together a list of resources I found useful:

https://news.ycombinator.com/item?id=12900448

Re: Introduction to Machine Learning for Developers

#3

Post by mi100hael » Thu, Nov 10, 2016, 9:49 PM UTC

Cool, this is a helpful intro. Anyone have any recommended reading for ML in a JVM context?

Re: Introduction to Machine Learning for Developers

#4

Post by adamnemecek » Thu, Nov 10, 2016, 9:51 PM UTC

mi100hael wrote: post #3

Cool, this is a helpful intro. Anyone have any recommended reading for ML in a JVM context?

I haven't read it but this book looks reasonable https://www.amazon.com/Scala-Machine-Learning-Patrick-Nicola...

Re: Introduction to Machine Learning for Developers

#5

Post by anton_tarasenko » Thu, Nov 10, 2016, 10:00 PM UTC

I'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 sure you chose “All Competitions” and then “Completed”.) Each competition has a leaderboard. When users publish their solutions on GitHub, you can find links to these solutions on the leaderboard. https://www.kaggle.com/competitions

Industrial solutions are more powerful and complex than these examples, but they are not publicly available. Data-driven companies post some details about this work in their blogs.

## Emerging applications

1. Stanford’s CS229 Course, Student projects. See “Recent years’ projects.” Hundreds of short papers. http://cs229.stanford.edu/

2. CMU ML Department, Student projects. More advanced problems, compared to CS229. http://www.ml.cmu.edu/research/data-analysis-projects.html

3. arXiv, Machine Learning. Drafts of important papers appear here first. Then they got published in journals. http://arxiv.org/list/stat.ML/recent

4. CS journals. Applied ML research also appear in engineering journals. https://scholar.google.com/citations?view_op=top_venues&hl=e...

5. CS departments. For example: CMU ML Department, PhD dissertations. http://www.ml.cmu.edu/research/phd-dissertations.html

## Government

1. Bloomberg and Flowers, “NYC Analytics.” NYC Mayor’s Office of Data Analysis describes their data management system and improvements in operations. http://www.nyc.gov/html/analytics/downloads/pdf/annual_repor...

2. UK Government, Tax Agent Segmentation. https://www.gov.uk/government/uploads/system/uploads/attachm...

3. Data.gov, Applications. Some are ML-based. http://www.data.gov/applications

4. StackExchange, Applications. http://opendata.stackexchange.com/questions/3346/examples-of...

Re: Introduction to Machine Learning for Developers

#6

Post by highCs » Thu, Nov 10, 2016, 10:05 PM UTC

If one understand decently all of that, does he get a job?

Re: Introduction to Machine Learning for Developers

#7

Post by Bedon292 » Thu, Nov 10, 2016, 10:06 PM UTC

I 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.

Re: Introduction to Machine Learning for Developers

#8

Post by DrNuke » Thu, Nov 10, 2016, 10:14 PM UTC

highCs wrote: post #6

If one understand decently all of that, does he get a job?

Supply-demand dynamics at play. The general answer is no, though. Chances are higher if you use these in your own domain and become an applied expert or if you win some competition on Kaggle or similar tough envinronment.

Re: Introduction to Machine Learning for Developers

#9

Post by lisivka » Thu, Nov 10, 2016, 10:19 PM UTC

highCs wrote: post #6

If one understand decently all of that, does he get a job?

If you will be able to debug all that, you will get a job.

Re: Introduction to Machine Learning for Developers

#10

Post by esfandia » Thu, Nov 10, 2016, 10:22 PM UTC

mi100hael wrote: post #3

Cool, this is a helpful intro. Anyone have any recommended reading for ML in a JVM context?

Weka is a popular machine learning toolkit in Java: http://www.cs.waikato.ac.nz/ml/weka/

and they have a textbook to go with it: http://www.cs.waikato.ac.nz/ml/weka/book.html

as well as an online course: https://weka.waikato.ac.nz/dataminingwithweka/preview