Azure Machine Learning

Azure Machine Learning studio is a web portal in Azure Machine Learning that contains low-code and no-code options for project authoring and asset management, it is a cloud service for accelerating and managing the machine learning project lifecycle, with drag and drop tool (Azure Machine Learning Designer) where you can drag the data sets and further process the analysis on that data. Machine learning professionals, data scientists, and engineers can use it in their day-to-day workflows: Train and deploy models and manage MLOps. Azure Machine Learning helps to build and deploy models faster. In this 2 days course designed for beginners or those with limited knowledge, participants will learn to use Azure for Machine Learning. Azure is flexible and can be used for any type of Machine Learning, from classical to deep learning, supervised, and unsupervised learning. Our expert trainer will guide learners on the fundamentals of Azure Machine Learning, learn how to use the various tools such as dragging the data sets and further process the analysis. If you would like to get started and upskill, look no further! You may additionally progress on to our other Machine Learning courses, and even venture into other programming languages to achieve a more competitive you!

Automated ML– It helps the developers in building ML models with high efficiency, scale, and productivity all the while sustaining model quality and also saves time and resources. Designer– It is an interactive interface to connect datasets and modules to create machine learning models with a drag and drop tool where you can drop the datasets and modules and connect the modules to create a pipeline Datasets– Used for easy access to data for the model from datastore or flat files on your computer. They create a reference to the data source location and create a copy of its metadata. Experiments– It is used for grouping runs for a specified script. It represents the main entry point for creating and working with the experiments in azure. Models– Creating a model in machine learning involves selecting an algorithm, providing it with data, and tuning the hyperparameters. While creating a model any popular machine learning framework can be used such as PyTorch, TensorFlow, Scikit-learn, etc Endpoints– It provides remote access to the services that are running, where instantiation of the created model into either an IoT module for integrated device deployments or web services that can be hosted in the cloud.

This course is meant for beginners, hence no coding/programming or computer science background required. Learners without any knowledge of statistics or data analytics are also welcome!

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Dwight graduated with a degree in Biotechnology (from NUS in 2001), Advanced diploma in Pharmaceutical management (from MDIS in 2007) and Masters in Education (from NTU in 2013). In 2014, Dwight ventured into Data Analytics and Database Management and picked up Statistics/Machine Learning, Big Data analytics, text mining & social media analytics and web applications. His other expertise includes data science programming languages such as R and Python. He is also deeply passionate in deep learning using sequential models and sequence to sequence models using Keras and h2oAI machine & deep learning platforms and libraries. He is also a Senior Data Analyst, where he performs data analytics and create predictive models using healthcare data.