Thank You...Please see inline..
On Sun, Jun 12, 2016 at 3:39 PM, <mylistt...@gmail.com> wrote: > 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. > *****Deepak**** Sorry for the confusion in my question. However, I was more interested in getting hold of a book which explains how I can use MLlib and Spark for machine learning problems. *****Deepak**** > > 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 > <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 >>> <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 >>>>> " >>>>> >>>> >>>> >>> >> >
