際際滷shows by User: AshokSharma18 / http://www.slideshare.net/images/logo.gif 際際滷shows by User: AshokSharma18 / Wed, 04 Mar 2015 00:06:25 GMT 際際滷Share feed for 際際滷shows by User: AshokSharma18 Machine Learning /slideshow/machine-learning-45407320/45407320 machinelearning-150304000629-conversion-gate01
The presentation includes preliminary information about the big data mainly metagenomic data and discussions related to the hurdles in analyzing using conventional approaches. In the later part, brief introduction about machine learning approaches using biological example for each. In the last, work done with special focus on implementation of a machine learning approach Random Forest for the functional annotation and taxonomic classification of metagenomic data.]]>
The presentation includes preliminary information about the big data mainly metagenomic data and discussions related to the hurdles in analyzing using conventional approaches. In the later part, brief introduction about machine learning approaches using biological example for each. In the last, work done with special focus on implementation of a machine learning approach Random Forest for the functional annotation and taxonomic classification of metagenomic data.]]> Wed, 04 Mar 2015 00:06:25 GMT /slideshow/machine-learning-45407320/45407320 AshokSharma18@slideshare.net(AshokSharma18) Machine Learning AshokSharma18 The presentation includes preliminary information about the big data mainly metagenomic data and discussions related to the hurdles in analyzing using conventional approaches. In the later part, brief introduction about machine learning approaches using biological example for each. In the last, work done with special focus on implementation of a machine learning approach Random Forest for the functional annotation and taxonomic classification of metagenomic data. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/machinelearning-150304000629-conversion-gate01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> The presentation includes preliminary information about the big data mainly metagenomic data and discussions related to the hurdles in analyzing using conventional approaches. In the later part, brief introduction about machine learning approaches using biological example for each. In the last, work done with special focus on implementation of a machine learning approach Random Forest for the functional annotation and taxonomic classification of metagenomic data.
Machine Learning from Indian Institute of Science Education and Research
]]> 1708 6 https://cdn.slidesharecdn.com/ss_thumbnails/machinelearning-150304000629-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-AshokSharma18-48x48.jpg?cb=1523227318 I joined the lab in January 2013 as a Ph. D. Student. I completed my graduation in Pharmaceutical Sciences in 2010 from Dr. H. S. Gour University Sagar, M.P. Later, moved to NIPER S.A.S Nagar, Punjab for post graduation studies and completed M.S. (Pharm) in Pharmacoinformatics in 2012. During my post-graduation, I was mainly involved in computer-aided drug designs Currently, I am involved in the taxonomic classification, functional annotation and metabolic pathway reconstruction of Next Generation Sequencing data in order to understand microbial abundance and their functional role within the community. I love to play cricket and badminton during my free time.