Tutorial Azure Machine Learning
Tutorial: Mastering Azure Machine Learning
Tutorial Azure Machine Learning
Azure Machine Learning is a cloud-based platform that provides tools and services for building, training, and deploying machine learning models. It allows users to create machine learning experiments using a visual interface or Python code, and provides capabilities for data preparation, model training, hyperparameter tuning, and model evaluation. Azure ML also supports deployment of models as web services for real-time or batch scoring. With Azure ML, users can access a rich set of algorithms, autoML capabilities, and integration with popular data science tools to accelerate the machine learning lifecycle.
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1 - Tutorial Azure Machine Learning: An introduction to Azure Machine Learning, a cloud based service that allows users to build, train, and deploy machine learning models. This tutorial is aimed at providing students with hands on experience to enhance their skills in the field of machine learning.
2) Overview of Azure Machine Learning: Understanding the key features and capabilities of Azure Machine Learning, such as data preparation, model training, and model deployment.
3) Setting up Azure Machine Learning Workspace: Step by step instructions on how to set up an Azure Machine Learning workspace, including creating resources and accessing the Azure portal.
4) Data Preparation: Techniques for preparing and cleaning data before training machine learning models, including data preprocessing and feature engineering.
5) Model Training and Evaluation: Hands on practice in training machine learning models using Azure Machine Learning algorithms and evaluating model performance metrics.
6) Hyperparameter Tuning and Optimization: Exploring techniques for optimizing machine learning models, such as hyperparameter tuning and automated machine learning.
7) Model Deployment: Learning how to deploy trained machine learning models to production environments using Azure Machine Learning services.
8) Monitoring and Maintenance: Understanding the importance of monitoring deployed models for performance and making necessary updates and maintenance.
9) Advanced Topics: Exploring advanced topics in Azure Machine Learning, such as model explainability, interpretability, and ethical AI considerations.
10) Practical Applications: Real world examples and case studies demonstrating how Azure Machine Learning can be used in various industries and applications.
11) Hands on Projects: Engaging students in hands on projects that involve applying Azure Machine Learning to real datasets and business problems.
12) Certification and Career Opportunities: Guidance on obtaining certification in Azure Machine Learning and exploring career opportunities in the field of machine learning and data science.
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