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.
In my spare time, I love learning new technologies and going to hackathons. Our hackathon project Pantrylogs using Artificial Intelligence was selected as one of the 10 Microsoft Imagine Cup UK finalists. I'm interested in learning more about AI, Data Science, and Machine Learning to improve the performances of our application. In this article, I would love to share my experience of using Azure Machine Learning Studio with you. Azure Machine Learning Studio is a very powerful browser-based, visual drag-and-drop authoring environment.
DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data structures transparent. It works in perfect harmony with parallelisation mechanism such as multiprocessing and SCOOP. The following documentation presents the key concepts and many features to build your own evolutions.
Data Science / AI / Machine Learning / Deep Learning, all those fields are currently being defined. So the first thing to learn is the faculty to be always active in learning. Google it (or use any other search engine as Qwant or Ecosia)! Be careful about your sources, since AI is a new field you may see a lot of different and sometimes contradictory information. No one has the absolute truth here about definitions or knowledge.