Machine learning - I would suggest that you pick up a fine book that explains machine learning. That's the way I went about - pick up each type of machine learning concept - say Linear regression then understand the why/when/how etc and infer results etc.
Then apply the learning to a small data set using python or R or scala without Spark. This is to familiarize the learning. Then run the same with MLlib and see it with a big data set on Spark. I would call this consolidation. Few things to remember - all Machine learning algorithms are not available On spark. There is a list of machine learning supported in spark. Kindly look at that. Also look at how to integrate mahout / h20 with spark and see how you can run the machine learning stuff supported by mahout with spark. And then your journey begins :-). Regards, Harmeet On Jun 12, 2016, at 0:31, Mich Talebzadeh <mich.talebza...@gmail.com> wrote: > yes absolutely Ted. > > Thanks for highlighting it > > > > Dr Mich Talebzadeh > > LinkedIn > https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw > > http://talebzadehmich.wordpress.com > > > On 11 June 2016 at 19:00, Ted Yu <yuzhih...@gmail.com> wrote: > Another source is the presentation on various ocnferences. > e.g. > http://www.slideshare.net/databricks/apache-spark-mllib-20-preview-data-science-and-production > > FYI > > On Sat, Jun 11, 2016 at 8:47 AM, Mich Talebzadeh <mich.talebza...@gmail.com> > wrote: > Interesting. > > The pace of development in this field is such that practically every single > book in Big Data landscape gets out of data before the ink dries on it :) > > I concur that they serve as good reference for starters but in my opinion the > best way to learn is to start from on-line docs (and these are pretty > respectful when it comes to Spark) and progress from there. > > If you have a certain problem then put to this group and I am sure someone > somewhere in this forum has come across it. Also most of these books' authors > actively contribute to this mailing list. > > > HTH > > > Dr Mich Talebzadeh > > LinkedIn > https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw > > http://talebzadehmich.wordpress.com > > > On 11 June 2016 at 16:10, Ted Yu <yuzhih...@gmail.com> wrote: > https://www.amazon.com/Machine-Learning-Spark-Powerful-Algorithms/dp/1783288515/ref=sr_1_1?ie=UTF8&qid=1465657706&sr=8-1&keywords=spark+mllib > > https://www.amazon.com/Spark-Practical-Machine-Learning-Chinese/dp/7302420424/ref=sr_1_3?ie=UTF8&qid=1465657706&sr=8-3&keywords=spark+mllib > > https://www.amazon.com/Advanced-Analytics-Spark-Patterns-Learning/dp/1491912766/ref=sr_1_2?ie=UTF8&qid=1465657706&sr=8-2&keywords=spark+mllib > > > On Sat, Jun 11, 2016 at 8:04 AM, Deepak Goel <deic...@gmail.com> wrote: > > Hey > > Namaskara~Nalama~Guten Tag~Bonjour > > I am a newbie to Machine Learning (MLIB and other libraries on Spark) > > Which would be the best book to learn up? > > Thanks > Deepak > -- > Keigu > > Deepak > 73500 12833 > www.simtree.net, dee...@simtree.net > deic...@gmail.com > > LinkedIn: www.linkedin.com/in/deicool > Skype: thumsupdeicool > Google talk: deicool > Blog: http://loveandfearless.wordpress.com > Facebook: http://www.facebook.com/deicool > > "Contribute to the world, environment and more : http://www.gridrepublic.org > " > > > >
