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Student and Faculty Guide – 10 easy steps to get up and running with Azure Machine Learning

#artificialintelligence

My colleague Amy Nicholson is the UK expert on Azure Machine Learning, the following blog post is after a quizzing session to get understand how to get started with Azure Machine Learning" Each student receives $100 of Azure credit per month, for 6 months. The Faculty member receives $250 per month, for 12 months. The Azure machine learning team provided a very nice walkthrough tutorial which covers a lot of the basics. This tutorial is really useful as it takes you through the entire process of creating an AzureML workspace, uploading data, creating an experiment to predict someone's credit risk, building, training, and evaluating the models, publishing your best model as a web service, and calling that web service. Now you need to learn how to import a data set into Azure Machine Learning, and where to find interesting data to build something amazing.



Student and Faculty Guide – 10 easy steps to get up and running with Azure Machine Learning

#artificialintelligence

My colleague Amy Nicholson is the UK expert on Azure Machine Learning, the following blog post is after a quizzing session to get understand how to get started with Azure Machine Learning" Each student receives $100 of Azure credit per month, for 6 months. The Faculty member receives $250 per month, for 12 months. The Azure machine learning team provided a very nice walkthrough tutorial which covers a lot of the basics. This tutorial is really useful as it takes you through the entire process of creating an AzureML workspace, uploading data, creating an experiment to predict someone's credit risk, building, training, and evaluating the models, publishing your best model as a web service, and calling that web service. Now you need to learn how to import a data set into Azure Machine Learning, and where to find interesting data to build something amazing.


The 5 Phases of Every Machine Learning Project – Blog

#artificialintelligence

Machine learning and predictive analytics are pervasive in our lives today. AI impacts nearly everything we do and interact with including retail and wholesale pricing, consumer habits and behaviors, marketing and advertising, politics, entertainment, sports, medicine, business logistics and planning, fraud and risk detection, airline and truck route planning, pricing strategy, gaming, AI speech recognition, AI image recognition, self-driving cars, and robotics.


Machine learning and its applications in plant molecular studies

#artificialintelligence

The advent of high-throughput genomic technologies has resulted in the accumulation of massive amounts of genomic information. However, biologists are challenged with how to effectively analyze these data. Machine learning can provide tools for better and more efficient data analysis. Unfortunately, because many plant biologists are unfamiliar with machine learning, its application in plant molecular studies has been restricted to a few species and a limited set of algorithms. Thus, in this study, we provide the basic steps for developing machine learning frameworks and present a comprehensive overview of machine learning algorithms and various evaluation metrics. Furthermore, we introduce sources of important curated plant genomic data and R packages to enable plant biologists to easily and quickly apply appropriate machine learning algorithms in their research. Finally, we discuss current applications of machine learning algorithms for identifying various genes related to resistance to biotic and abiotic stress. Broad application of machine learning and the accumulation of plant sequencing data will advance plant molecular studies. The advent of high-throughput sequencing technologies has produced several large-scale data sets. This enormous amount of information enables biologists to explore topics that were once difficult or impossible to investigate, such as associations between microRNA and certain diseases, the causes of vascular inflammation and atherosclerosis in humans [1–3] and stress breeding in plants [4]. However, many challenges have also emerged. For example, the European Bioinformatics Institute now stores 273 petabytes of raw molecular data on humans, plants and animals (https://www.ebi.ac.uk/).