This course will help you and your team to build skills required to pass the most in demand and challenging, Azure DP-100 Certification exam. It will earn you one of the most in-demand certificate of Microsoft Certified: Azure Data Scientist Associate. DP-100 is designed for Data Scientists. This exam tests your knowledge of Data Science and Machine learning to implement machine learning models on Azure. So you must know right from Machine Learning fundamentals, Python, planning and creating suitable environments in Azure, creating machine learning models as well as deploying them in production. Why should you go for DP-100 Certification?One of the very few certifications in the field of Data Science and Machine Learning. You can successfully demonstrate your knowledge and abilities in the field of Data Science and Machine Learning. You will improve your job prospects substantially in the field of Data Science and Machine Learning. Key points about this courseCovers the most current syllabus as on May, 2021.100% syllabus of DP-100 Exam is covered. Very detailed and comprehensive coverage with more than 200 lectures and 25 Hours of contentCrash courses on Python and Azure Fundamentals for those who are new to the world of Data ScienceMachine Learning is one of the hottest and top paying skills. It’s also one of the most interesting field to work on. In this course of Machine Learning using Azure Machine Learning, we will make it even more exciting and fun to learn, create and deploy machine learning models using Azure Machine Learning Service as well as the Azure Machine Learning Studio. We will go through every concept in depth. This course not only teaches basic but also the advance techniques of Data processing, Feature Selection and Parameter Tuning which an experienced and seasoned Data Science expert typically deploys. Armed with these techniques, in a very short time, you will be able to match the results that an experienced data scientist can achieve. This course will help you prepare for the entry to this hot career path of Machine Learning as well as the Azure DP-100: Azure Data Scientist Associate exam- Exam Syllabus for DP-100 Exam -1. Set up an Azure Machine Learning Workspace (30-35%)Create an Azure Machine Learning workspaceCreate an Azure Machine Learning workspaceConfigure workspace settingsManage a workspace by using Azure Machine Learning studioManage data objects in an Azure Machine Learning workspaceRegister and maintain datastoresCreate and manage datasetsManage experiment compute contextsCreate a compute instanceDetermine appropriate compute specifications for a training workloadCreate compute targets for experiments and trainingRun Experiments and Train Models (25-30%)Create models by using Azure Machine Learning DesignerCreate a training pipeline by using Azure Machine Learning designerIngest data in a designer pipelineUse designer modules to define a pipeline data flowUse custom code modules in designerRun training scripts in an Azure Machine Learning workspaceCreate and run an experiment by using the Azure Machine Learning SDKConfigure run settings for a scriptConsume data from a dataset in an experiment by using the Azure Machine Learning SDKGenerate metrics from an experiment runLog metrics from an experiment runRetrieve and view experiment outputsUse logs to troubleshoot experiment run errorsAutomate the model training processCreate a pipeline by using the SDKPass data between steps in a pipelineRun a pipelineMonitor pipeline runsOptimize and Manage Models (20-25%)Use Automated ML to create optimal models Use the Automated ML interface in Azure Machine Learning studio Use Automated ML from the Azure Machine Learning SDKSelect pre-processing optionsDetermine algorithms to be searched Define a primary metric Get data for an Automated ML run Retrieve the best modelUse Hyperdrive to tune hyperparameters Select a sampling method Define the search space Define the primary metric Define early termination options Find the model that has optimal hyperparameter values Use model explainers to interpret models Select a model interpreter Generate feature importance data Manage models Register a trained model Monitor model usage Monitor data drift Deploy and Consume Models (20-25%) Create production compute targets Consider security for deployed servicesEvaluate compute options for deployment Deploy a model as a service Configure deployment settings Consume a deployed service Troubleshoot deployment container issues Create a pipeline for batch inferencing Publish a batch inferencing pipeline Run a batch inferencing pipeline and obtain outputs Publish a designer pipeline as a web service Create a target compute resource Configure an Inference pipeline Consume a deployed endpointSome feedback from previous students,“The instructor explained every concept smoothly and clearly. I’m an acountant without tech background nor excellent statistical knowledge. I do really appreciate these helpful on-hand labs and lectures. Passed the DP-100 in Dec 2020. This course really
