Machine learning is at the core of artificial intelligence, and many modern applications and services depend on predictive machine learning models. Training a machine learning model is an iterative process that requires time and compute resources. Automated machine learning can help make it easier. In this course, you will learn how to use Azure Machine Learning to create and publish models without writing code. This course will help you prepare for Exam AI-900: Microsoft Azure AI Fundamentals.

Machine learning is at the core of artificial intelligence, and many modern applications and services depend on predictive machine learning models. Training a machine learning model is an iterative process that requires time and compute resources. Automated machine learning can help make it easier. In this course, you will learn how to use Azure Machine Learning to create and publish models without writing code. This is the second course in a five-course program that prepares you to take the DP-100: Designing and Implementing a Data Science Solution on Azurecertification exam.

In this study we worked on the classification of the Chess Endgame problem using different algorithms like logistic regression, decision trees and neural networks. Our experiments indicates that the Neural Networks provides the best accuracy (85%) then the decision trees (79%). We did these experiments using Microsoft Azure Machine Learning as a case-study on using Visual Programming in classification. Our experiments demonstrates that this tool is powerful and save a lot of time, also it could be improved with more features that increase the usability and reduce the learning curve. We also developed an application for dataset visualization using a new programming language called Ring, our experiments demonstrates that this language have simple design like Python while integrates RAD tools like Visual Basic which is good for GUI development in the open-source world

Since it launched in 2017, Facebook's machine-learning framework PyTorch has been put to good use, with applications ranging from powering Elon Musk's autonomous cars to driving robot-farming projects. Now pharmaceutical firm AstraZeneca has revealed how its in-house team of engineers are tapping PyTorch too, and for equally as important endeavors: to simplify and speed up drug discovery. Combining PyTorch with Microsoft Azure Machine Learning, AstraZeneca's technology can comb through massive amounts of data to gain new insights about the complex links between drugs, diseases, genes, proteins or molecules. Those insights are used to feed an algorithm that can, in turn, recommend a number of drug targets for a given disease for scientists to test in the lab. The method could allow for huge strides in a sector like drug discovery, which so far has been based on costly and time-consuming trial-and-error methods.

One of the many remarkable things about artificial intelligence is that while we tend to think of it as something that will have a big effect in the not-too-distant future, it is already transforming people's lives in profound and powerful ways today. In factories and warehouses, AI is improving workplace safety by scanning thousands of videos to detect potential risks. In the U.S., researchers are exploring how AI can help public health organizations around the world prevent the spread of deadly diseases like Ebola, Chikungunya, and Zika by detecting the presence of pathogens in the environment and stopping transmission to humans before outbreaks can begin. I believe this is the true promise and challenge of AI – using these new technologies to create a healthier and safer world for everyone. Now that AI has given computers the ability to recognize words and images, discover patterns in complex systems and reason and learn much like people do, it is enabling our devices to behave more naturally and more responsively.

Now, you at least have an idea of what machine learning is and how useful for businesses and the IT industry in general it is. So, it is high time to learn how to implement these magic algorithms. It is worth noting that there are already several ready-made machine learning tools intended to somehow simplify the work for your developers. Google launched its machine learning service called Awareness API last year. This service allows developers to understand the context in which customers use their smartphones.

Healthcare organizations need predictive analytics for providing quality healthcare and population health management. Building predictive models by applying machine learning algorithms is complex in the infrastructure-as-a-service or platform-as-as-a-service environment as it involves distributed computing. The emergence of predictive analytics in the healthcare industry has offered enormous opportunity to be able to predict the events in healthcare organization and other industries as well such as aerospace industry. Predictive analytics is a subfield of data science that deploys several multi-disciplinary fields such as statistical inference, machine learning, clustering, data visualization, and machine learning iteratively through the lifecycle of the data analytics. The stages can be defined as defining the problem statement for the organization, scope of the data analytics project, collection of big data, exploratory data analysis, data preparation, deployment of predictive models leveraging machine learning algorithms.

As you explore machine learning scenarios in your cloud applications, the speed of your scoring operations is critical. Native scoring, a feature available in SQL Server 2017, supports any operation you might run in R, ranging from simple functions to training complex machine learning models, enabling faster prediction performance in your enterprise production scenarios. In this live webinar with interactive Q&A, you will learn about native scoring and Machine Learning Services on SQL Server 2017, how these features can benefit your organization, and how you can use them to implement you own machine learning scenarios. Native scoring is a feature that is available today on SQL Server on Linux, and Machine Learning Services is a feature that will soon become available on Linux.

An early dive into the Internet of Things landscape yielded sweet rewards for The Hershey Company once the chocolate manufacturer began tracking the weight of Twizzlers during production. That was the story Luis Morinigo, IoT and advanced analytics practice lead for Washington, D.C.-based solution provider New Signature, shared with a audience of midmarket IT leaders Monday evening at the Midsize Enterprise Summit. He and George Lenhart, the former senior manager of IS disruptive solutions at Hershey, explained that while the journey involved several challenges, their ability to leverage machine-learning-fueled analytics ultimately paid meaningful financial dividends. Morinigo said the advent of scalable cloud-based analytics – Microsoft Azure Machine Learning, in this case – became a catalyst for the Twizzlers project because it reduced technical barriers involved in implementing data science capabilities. As other companies began applying IoT to maintenance and operations, Hershey and New Signature felt a clear need to move forward.

Microsoft Azure Machine Learning (MAML) is a service on Windows Azure which a developer can use to build a predictive analytics model using machine learning over data and then deploy that model as a cloud service. ML Studio provides functionality to support the end-to-end workflow for constructing a predictive model, from ready access to common data sources, data exploration, feature selection and creation, building training and testing sets, machine learning over data, and final model evaluation and experimentation. In this presentation, we present an overview of the basic data science workflow, with details on select machine learning algorithms, then take you on a guided tour of ML Studio. During the presentation we will build a predictive analytics model using real-world data, evaluate several different machine learning algorithms and modeling strategies, then deploy the finished model as a machine learning web service on Azure within minutes. This end-to-end description and demonstration is intended to provide sufficient information for you to begin exploring ML Studio on your own after the session.