The 40530: Microsoft Cloud Workshop: Cognitive Services and Deep Learning course is designed to provide learners with an in-depth understanding of integrating Microsoft Cognitive Services with Azure Machine Learning to create intelligent solutions. Learners explore a real-world customer case study, design a proof of concept, and present their solution in Module 1. This hands-on experience helps participants grasp the practical application of these technologies.
In Module 2, participants dive into the technical aspects: setting up Azure Machine Learning accounts, creating and deploying an Unsupervised model, applying TensorFlow, and rounding out their knowledge by completing the solution. This workshop is instrumental for those looking to enhance their skills in AI, machine learning, and Cognitive Computing, and provides a solid foundation for implementing deep learning solutions in the cloud.
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♱ Excluding VAT/GST
Classroom Training price is on request
You can request classroom training in any city on any date by Requesting More Information
To successfully undertake the 40530: Microsoft Cloud Workshop: Cognitive Services and Deep Learning course, the following minimum prerequisites are recommended:
These prerequisites are meant to ensure that participants can make the most of the course content and engage with the hands-on lab activities effectively. However, individuals with a strong desire to learn and a commitment to developing their skills in cognitive services and deep learning are encouraged to participate, as foundational knowledge can be built upon during the course.
Course 40530: Microsoft Cloud Workshop on Cognitive Services and Deep Learning focuses on designing and implementing AI solutions using Azure technologies.
Target Audience for Course 40530:
This course equips participants with skills to create deep learning solutions using Microsoft Cognitive Services and TensorFlow within Azure. Learners will design, deploy, and integrate AI models into applications.
Microsoft Cognitive Services are a collection of APIs and services available through Microsoft Azure, designed to enable developers to create applications that can see, hear, speak, understand, and even begin to reason. These tools build on the power of machine learning and AI, including deep learning techniques, to make it simpler for systems to recognize images, comprehend and translate spoken languages, and make decisions. Microsoft deep learning models are a significant part of this suite, offering advanced capabilities that can be easily integrated into apps, enhancing them with intelligent features powered by Azure's robust cloud infrastructure.
Deep learning is a type of artificial intelligence that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. By using algorithms that allow models to train themselves to perform tasks by exposing multilayered neural networks to vast amounts of data, deep learning achieves great accuracy. Microsoft Azure enhances this process by providing powerful cloud computing capabilities, enabling extensive deep learning processes without the need for expensive local hardware. Microsoft Azure's deep learning tools offer scalable solutions to handle complex computational tasks, making it easier for professionals to innovate and apply deep learning models efficiently.
Unsupervised learning is a type of machine learning where models are trained using data without prior labeling. This approach lets the model infer patterns and correlations from the data itself without guidance on what outcomes to predict. It's particularly useful for discovering hidden structures in data. For instance, clustering similar customers together for market segmentation is a practical application. The flexibility of unsupervised models, especially when combined with platforms like Microsoft Azure, enhances their capability, making them ideal for exploring vast datasets and developing sophisticated deep learning solutions with less human intervention.
TensorFlow is an open-source software library used for dataflow and differentiable programming. It enables developers to create complex machine learning models with ease. Essentially, it provides a foundation for building and deploying machine learning applications, particularly those that involve large-scale deep learning processes. TensorFlow operates across a range of platforms, making it ideal for experimenting with deep learning models and seamlessly scaling from research to production. Its flexibility and capability to manage high volumes of data make it a preferred tool for developers and researchers working in the field of artificial intelligence.
Cognitive computing refers to systems that simulate human thought processes in a computerized model. Incorporating self-learning algorithms using data mining, pattern recognition, and natural language processing, these systems are designed to mimic the way the human brain works. The goal is to create automated IT systems capable of solving problems without human assistance. Cognitive computing is used in numerous applications including robotics, business decision management, and health care to enhance decision-making and drive innovation through insightful data analysis.
Course 40530: Microsoft Cloud Workshop on Cognitive Services and Deep Learning focuses on designing and implementing AI solutions using Azure technologies.
Target Audience for Course 40530:
This course equips participants with skills to create deep learning solutions using Microsoft Cognitive Services and TensorFlow within Azure. Learners will design, deploy, and integrate AI models into applications.