DP100 Azure Machine Learning Data Science Exam Prep 2023
What you’ll learn
Prepare for DP-100 Exam
Getting Started with Azure ML
Setting up Azure Machine Learning Workspace
Running Experiments and Training Models
Deploying the Models
AzureML Designer: Data Preprocessing
Regression Using AzureML Designer
Classification Using AzureML Designer
AzureML SDK: Setting up Azure ML Workspace
AzureML SDK: Running Experiments and Training Models
Use Automated ML to Create Optimal Models
Tune hyperparameters with Azure Machine Learning
Use model explainers to interpret models
Model Registration and Deployment Using Azureml SDK
Requirements
Basic Understanding of Machine Learning
A Free or Paid Subscription to Microsoft Azure
Description
Machine Learning and Data Science are one of the hottest tech fields nowadays! There are a lot of opportunities in these fields. Data Science and Machine Learning have applications in almost every field, like transportation, Finance, Banking, Healthcare, Defense, Entertainment, etc.Most professionals and students learn Data Science and Machine Learning but specifically, they are facing difficulties while working in a cloud environment. To solve this problem I have created this course, DP-100. It will help you to apply your data skills in Azure Cloud smoothly.This course will help you to pass the “Exam DP-100: Designing and Implementing a Data Science Solution on Azure”. In this course, you will understand what to expect on the exam and it includes all the topics that are required to pass the DP-100 Exam.Below are the skills measured in DP-100 Exam,1) Design and prepare a machine learning solution (20–25%)Design a machine learning solutionManage an Azure Machine Learning workspaceManage data in an Azure Machine Learning workspaceManage compute for experiments in Azure Machine Learning2) Explore data and train models (35–40%)Create models by using the Azure Machine Learning designerExplore data by using data assets and data storesCreate models by using the Azure Machine Learning designerUse automated machine learning to explore optimal modelsUse notebooks for custom model trainingTune hyperparameters with Azure Machine Learning3) Prepare a model for deployment (20–25%)Run model training scriptsImplement training pipelines Manage models in Azure Machine Learning4) Deploy and retrain a model (10–15%)Deploy a model Apply machine learning operations (MLOps) practicesSo what are you waiting for, Enroll Now and understand Azure Machine Learning to advance your career and increase your knowledge!
Overview
Section 1: Getting Started with Azure ML
Lecture 1 Course Overview
Lecture 2 Important – Udemy Review Update
Lecture 3 Introduction to Azure Machine Learning
Lecture 4 Azure Machine Learning Studio
Lecture 5 Azure ML Cheat Sheet
Lecture 6 Creating Microsoft Azure Account
Lecture 7 Course Materials: Slides, Colab Notebooks and Datasets
Section 2: Setting up Azure Machine Learning Workspace
Lecture 8 Azure ML: Architecture and Concepts
Lecture 9 Creating AzureML Workspace
Lecture 10 Workspace Overview
Lecture 11 AzureML Studio Overview
Lecture 12 Introduction to Azure ML Datasets and Datastores
Lecture 13 Creating a Datastore
Lecture 14 Creating a Dataset
Lecture 15 Exploring AzureML Dataset
Lecture 16 Introduction to Azure ML Compute Resources
Lecture 17 Creating Compute Instance and Compute Cluster
Lecture 18 Deleting the Resources
Section 3: Running Experiments and Training Models
Lecture 19 Azure ML Pipeline
Lecture 20 Creating New Pipeline using AzureML Designer
Lecture 21 Submitting the Designer Pipeline Run
Section 4: Deploying the Models
Lecture 22 Creating Real-Time Inference Pipeline
Lecture 23 Deploying Real-Time Endpoint in AzureML Designer
Lecture 24 Creating Batch Inference Pipeline in AzureML Designer
Lecture 25 Running Batch Inference Pipeline in AzureML Designer
Lecture 26 Deleting the Resources
Section 5: AzureML Designer: Data Preprocessing
Lecture 27 Setting up Workspace and Compute Resources
Lecture 28 Sample Datasets
Lecture 29 Select Columns in Dataset
Lecture 30 Importing External Dataset From Web URL
Lecture 31 Edit Metadata – Column Names
Lecture 32 Edit Metadata – Feature Type and Data Type
Lecture 33 Creating Storage Account, Datastore and Datasets
Lecture 34 Adding Columns From One Dataset to Another One
Lecture 35 Adding Rows From One Dataset to Another One
Lecture 36 Clean Missing Data Module
Lecture 37 Splitting the Dataset
Lecture 38 Normalizing Dataset
Lecture 39 Exporting Data to Blob Storage
Lecture 40 Deleting the Resources
Section 6: Project 1: Regression Using AzureML Designer
Lecture 41 Creating Workspace, Compute Resources, Storage Account, Datastore and Dataset
Lecture 42 Business Problem
Lecture 43 Analyzing the Dataset
Lecture 44 Data Preprocessing
Lecture 45 Training ML Model with Linear Regression (Online Gradient Descent)
Lecture 46 Evaluating the Results
Lecture 47 Training ML Model with Linear Regression (Ordinary least squares)
Lecture 48 Training ML Model with Boosted Decision Tree and Decision Forest Regression
Lecture 49 Finalizing the ML Model
Lecture 50 Creating and Deploying Real-Time Inference Pipeline
Lecture 51 Creating and Deploying Batch Inference Pipeline
Lecture 52 Deleting the Resources
Section 7: Project 2: Classification Using AzureML Designer
Lecture 53 Creating Workspace, Compute Resources, Storage Account, Datastore and Dataset
Lecture 54 Business Problem
Lecture 55 Analyzing the Dataset
Lecture 56 Data Preprocessing
Lecture 57 Training ML Model with Two-Class Logistic Regression
Lecture 58 Training ML Model with Two-Class SVM
Lecture 59 Training ML Model with Two-Class Boosted Decision Tree & Decision Forest
Lecture 60 Finalizing the ML Model
Lecture 61 Creating and Deploying Batch Inference Pipeline
Section 8: AzureML SDK: Setting up Azure ML Workspace
Lecture 62 AzureML SDK Introduction
Lecture 63 Creating Workspace using AzureMl SDK
Lecture 64 Creating a Datastore using AzureMl SDK
Lecture 65 Creating a Dataset using AzureMl SDK
Lecture 66 Accessing the Workspace, Datastore and Dataset with AzureML SDK
Lecture 67 AzureML Dataset and Pandas Dataset Conversion
Lecture 68 Uploading Local Datasets to Storage Account
Section 9: AzureML SDK: Running Experiments and Training Models
Lecture 69 Running Sample Experiment in AzureML Environment
Lecture 70 Logging Values to Experiment in AzureML Environment
Lecture 71 Introduction to Azure ML Environment
Lecture 72 Running Script in AzureML Environment Part 1
Lecture 73 Running Script in AzureML Environment Part 2
Lecture 74 Uploading the output file to Existing run in AzureML Environment
Lecture 75 Logistic Regression in Local Environment Part 1
Lecture 76 Logistic Regression in Local Environment Part 2
Lecture 77 Creating Python Script – Logistic Regression
Lecture 78 Running Python Script for Logistic Regression in AzureML Environment
Lecture 79 log_confusion_matrix Method
Lecture 80 Provisioning Compute Cluster in AzureML SDK
Lecture 81 Automate Model Training – Introduction
Lecture 82 Automate Model Training – Pipeline Run Part 1
Lecture 83 Automate Model Training – Pipeline Run Part 2
Lecture 84 Automate Model Training -Data Processing Script
Lecture 85 Automate Model Training – Model Training Script
Lecture 86 Automate Model Training – Running the Pipeline
Section 10: Use Automated ML to Create Optimal Models
Lecture 87 Introduction to Automated ML
Lecture 88 Automated ML in Azure Machine Learning studio
Lecture 89 Automated ML in Azure Machine Learning SDK
Section 11: Tune hyperparameters with Azure Machine Learning
Lecture 90 What Hyperparameter Tuning Is?
Lecture 91 Define the Hyperparameters Search Space
Lecture 92 Sampling the Hyperparameter Space
Lecture 93 Specify Early Termination Policy
Lecture 94 Configuring the Hyperdrive Run – Part 1
Lecture 95 Configuring the Hyperdrive Run – Part 2
Lecture 96 Creating the Hyperdrive Training Script
Lecture 97 Getting the Best Model and Hyperparameters
Section 12: Use Model Explainers to Interpret Models
Lecture 98 Interpretability Techniques in Azure
Lecture 99 Model Explainer on Local Machine
Lecture 100 Model Explainer in AzureML Part 1
Lecture 101 Model Explainer in AzureML Part 2
Section 13: Model Registration and Deployment Using Azureml SDK
Lecture 102 Introduction to Serialization and Deserialization
Lecture 103 Serialization Using Joblib
Lecture 104 Deserialization Using Joblib
Lecture 105 Handling Dummy Variables in Production
Lecture 106 Train ML Model for Webservice Deployment
Lecture 107 Register the Model Using Run ID pkl File
Lecture 108 Register the Model Using Local pkl File
Lecture 109 Provision AKS Production Cluster
Lecture 110 Revising the Steps Learned
Lecture 111 Project 3: Step 1 (Creating and Accessing the Workspace)
Lecture 112 Project 3: Step 2 (Train and Serialize ML Model)
Lecture 113 Project 3: Step 3 (Register the Model to Workspace)
Lecture 114 Project 3: Step 4 (Register an Environment)
Lecture 115 Project 3: Step 5 (Create AKS Cluster)
Lecture 116 Project 3: Step 6 (Inference and Deployment Configuration)
Lecture 117 Project 3: Step 7 (Creating the Entry Script)
Lecture 118 Project 3: Step 8 (Creating an Endpoint)
Lecture 119 Project 3: Step 9 (Testing the Web Service)
Lecture 120 Project 4: Deploy Multiple Models as Webservice (Step 1)
Lecture 121 Project 4: Deploy Multiple Models as Webservice (Step 2)
Lecture 122 Project 4: Deploy Multiple Models as Webservice (Step 3)
Lecture 123 Project 4: Deploy Multiple Models as Webservice (Step 4)
Section 14: Azure Fundamentals: Virtual Machines
Lecture 124 Introduction to Azure Virtual Machines
Lecture 125 Creating Virtual Machine in Azure
Lecture 126 Connecting to Virtual Machine and Running Commands
Lecture 127 Key Concepts – Image, Size and Disks
Lecture 128 Commands executed in Tutorial
Lecture 129 Installing nginx on Azure Virtual Machine
Lecture 130 Commands executed in Tutorial
Lecture 131 Simplification of Software Installation on Azure Virtual Machine
Lecture 132 Availability Sets and Zones
Lecture 133 Virtual Machine Scale Sets
Lecture 134 Scaling and Load Balancing with VM Scale Sets
Lecture 135 Static IP, Monitoring, Dedicated Host and Reducing the Cost
Lecture 136 Designing Good Solutions with Azure VMs
Section 15: Azure Fundamentals: Managed Compute Services
Lecture 137 Introduction to Azure Managed Compute Services
Lecture 138 Introduction to IaaS, PaaS and SaaS
Lecture 139 Introduction to Azure App Service
Lecture 140 Creating First Web App using Azure App Service
Lecture 141 More about the Azure App Service
Lecture 142 Introduction to Containers
Lecture 143 Introduction to Azure Container Instances
Lecture 144 Container Orchestration – AKS and Service Fabric
Lecture 145 Introduction to Azure Serverless
Lecture 146 Azure Serverless Service – Azure Functions
Lecture 147 Logic Apps
Lecture 148 Azure Shared Responsibility Model
Lecture 149 Review – Azure Compute Services
Lecture 150 Deleting Recourse Groups
Section 16: Azure Fundamentals: Storage
Lecture 151 Introduction to Azure Storage
Lecture 152 Managed and Unmanaged Block Storage in Azure
Lecture 153 Azure Files
Lecture 154 Azure Blob Storage and Tiers
Section 17: Azure Fundamentals: Databases
Lecture 155 Introduction to Database
Lecture 156 Snapshots, Transaction Logs, Standby Database
Lecture 157 RTO and RPO
Lecture 158 Data Consistency
Lecture 159 How to Select a Database ?
Lecture 160 Introduction to Relational Database
Lecture 161 Relational Database-OLTP
Lecture 162 Creating MySQL Server in Azure
Lecture 163 Code executed in next tutorial
Lecture 164 Exploring MySQL Server in Azure
Lecture 165 Relational Database – OLAP (Online Analytics Processing)
Lecture 166 Azure NoSQL Database: Azure Cosmos DB
Lecture 167 Exploring Azure NoSQL Database: Azure Cosmos DB
Lecture 168 Azure In-Memory Database: Azure Cache for Redis
Lecture 169 Review: Databases
Lecture 170 Databases: Scenarios
Lecture 171 Deleting Database Recourse Groups
Anyone who wants to learn Azure Machine Learning,Students and Professionals Who Wants to Pass DP-100 Exam
Course Information:
Udemy | English | 20h 56m | 8.36 GB
Created by: Vijay Gadhave
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