Dp-100: Azure Machine Learning & Data Science Exam Prep 2022

Azure Machine Learning, AzureML, Exam DP-100: Designing and Implementing a Data Science Solution, 4 End-to-End Projects

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
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 now a days ! There are a lot of opportunities in these fields. Data Science and Machine Learning has applications in almost every field, like transportation, Finance, Banking, Healthcare, Defense, Entertainment, etc.Most of the professionals and students learn Data Science and Machine Learning but specifically they are facing difficulties while working on 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 require to pass the DP-100 Exam.Below are the skills measured in DP-100 Exam,1) Manage Azure resources for machine learning (25–30%)Create an Azure Machine Learning workspaceManage data in an Azure Machine Learning workspaceManage compute for experiments in Azure Machine LearningImplement security and access control in Azure Machine LearningSet up an Azure Machine Learning development environmentSet up an Azure Databricks workspace2) Run experiments and train models (20–25%)Create models by using the Azure Machine Learning designerRun model training scriptsGenerate metrics from an experiment runUse Automated Machine Learning to create optimal modelsTune hyperparameters with Azure Machine Learning3) Deploy and operationalize machine learning solutions (35–40%)Select compute for model deploymentDeploy a model as a serviceManage models in Azure Machine LearningCreate an Azure Machine Learning pipeline for batch inferencingPublish an Azure Machine Learning designer pipeline as a web serviceImplement pipelines by using the Azure Machine Learning SDKApply ML Ops practices4) Implement responsible machine learning (5–10%)Use model explainers to interpret modelsDescribe fairness considerations for modelsDescribe privacy considerations for dataSo what are you waiting for, Enroll Now and understand Azure Machine Learining to advance your career and increase your knowledge!

Overview

Section 1: Getting Started with Azure ML

Lecture 1 Introduction to Azure Machine Learning

Lecture 2 Introduction to Azure Machine Learning Studio

Lecture 3 Azure ML Cheat Sheet

Lecture 4 DP-100 Exam Skills Measured (Exam Curriculum)

Lecture 5 Course Slides, Colab Notebooks and Datasets

Section 2: Microsoft Azure Fundamentals - Introduction

Lecture 6 [OPTIONAL] Introduction to Microsoft Azure

Lecture 7 [OPTIONAL] Introduction to Microsoft Azure Fundamentals

Lecture 8 [OPTIONAL] Introduction to Cloud Computing

Lecture 9 [OPTIONAL] Introduction to Azure Portal

Lecture 10 [OPTIONAL] Introduction to Azure Marketplace

Lecture 11 [OPTIONAL] Azure Free Account

Lecture 12 Creating Microsoft Azure Account

Section 3: Setting up Azure Machine Learning Workspace

Lecture 13 Azure ML: Architecture and Concepts

Lecture 14 Creating AzureML Workspace

Lecture 15 Workspace Overview

Lecture 16 AzureML Studio Overview

Lecture 17 Introduction to Azure ML Datasets and Datastores

Lecture 18 Creating a Datastore

Lecture 19 Creating a Dataset

Lecture 20 Exploring AzureML Dataset

Lecture 21 Introduction to Azure ML Compute Resources

Lecture 22 Creating Compute Instance and Compute Cluster

Lecture 23 Deleting the Resources

Section 4: Running Experiments and Training Models

Lecture 24 Azure ML Pipeline

Lecture 25 Creating New Pipeline using AzureML Designer

Lecture 26 Submitting the Designer Pipeline Run

Section 5: Deploying the Models

Lecture 27 Creating Real-Time Inference Pipeline

Lecture 28 Deploying Real-Time Endpoint in AzureML Designer

Lecture 29 Creating Batch Inference Pipeline in AzureML Designer

Lecture 30 Running Batch Inference Pipeline in AzureML Designer

Lecture 31 Deleting the Resources

Section 6: AzureML Designer: Data Preprocessing

Lecture 32 Setting up Workspace and Compute Resources

Lecture 33 Sample Datasets

Lecture 34 Select Columns in Dataset

Lecture 35 Importing External Dataset From Web URL

Lecture 36 Edit Metadata - Column Names

Lecture 37 Edit Metadata - Feature Type and Data Type

Lecture 38 Creating Storage Account, Datastore and Datasets

Lecture 39 Adding Columns From One Dataset to Another One

Lecture 40 Adding Rows From One Dataset to Another One

Lecture 41 Clean Missing Data Module

Lecture 42 Splitting the Dataset

Lecture 43 Normalizing Dataset

Lecture 44 Exporting Data to Blob Storage

Lecture 45 Deleting the Resources

Section 7: Project 1: Regression Using AzureML Designer

Lecture 46 Creating Workspace, Compute Resources, Storage Account, Datastore and Dataset

Lecture 47 Business Problem

Lecture 48 Analyzing the Dataset

Lecture 49 Data Preprocessing

Lecture 50 Training ML Model with Linear Regression (Online Gradient Descent)

Lecture 51 Evaluating the Results

Lecture 52 Training ML Model with Linear Regression (Ordinary least squares)

Lecture 53 Training ML Model with Boosted Decision Tree and Decision Forest Regression

Lecture 54 Finalizing the ML Model

Lecture 55 Creating and Deploying Real-Time Inference Pipeline

Lecture 56 Creating and Deploying Batch Inference Pipeline

Lecture 57 Deleting the Resources

Section 8: Project 2: Classification Using AzureML Designer

Lecture 58 Creating Workspace, Compute Resources, Storage Account, Datastore and Dataset

Lecture 59 Business Problem

Lecture 60 Analyzing the Dataset

Lecture 61 Data Preprocessing

Lecture 62 Training ML Model with Two-Class Logistic Regression

Lecture 63 Training ML Model with Two-Class SVM

Lecture 64 Training ML Model with Two-Class Boosted Decision Tree & Decision Forest

Lecture 65 Finalizing the ML Model

Lecture 66 Creating and Deploying Batch Inference Pipeline

Section 9: AzureML SDK: Setting up Azure ML Workspace

Lecture 67 AzureML SDK Introduction

Lecture 68 Creating Workspace using AzureMl SDK

Lecture 69 Creating a Datastore using AzureMl SDK

Lecture 70 Creating a Dataset using AzureMl SDK

Lecture 71 Accessing the Workspace, Datastore and Dataset with AzureML SDK

Lecture 72 AzureML Dataset and Pandas Dataset Conversion

Lecture 73 Uploading Local Datasets to Storage Account

Section 10: AzureML SDK: Running Experiments and Training Models

Lecture 74 Running Sample Experiment in AzureML Environment

Lecture 75 Logging Values to Experiment in AzureML Environment

Lecture 76 Introduction to Azure ML Environment

Lecture 77 Running Script in AzureML Environment Part 1

Lecture 78 Running Script in AzureML Environment Part 2

Lecture 79 Uploading the output file to Existing run in AzureML Environment

Lecture 80 Logistic Regression in Local Environment Part 1

Lecture 81 Logistic Regression in Local Environment Part 2

Lecture 82 Creating Python Script - Logistic Regression

Lecture 83 Running Python Script for Logistic Regression in AzureML Environment

Lecture 84 log_confusion_matrix Method

Lecture 85 Provisioning Compute Cluster in AzureML SDK

Lecture 86 Automate Model Training - Introduction

Lecture 87 Automate Model Training - Pipeline Run Part 1

Lecture 88 Automate Model Training - Pipeline Run Part 2

Lecture 89 Automate Model Training -Data Processing Script

Lecture 90 Automate Model Training - Model Training Script

Lecture 91 Automate Model Training - Running the Pipeline

Section 11: Use Automated ML to Create Optimal Models

Lecture 92 Introduction to Automated ML

Lecture 93 Automated ML in Azure Machine Learning studio

Lecture 94 Automated ML in Azure Machine Learning SDK

Section 12: Tune hyperparameters with Azure Machine Learning

Lecture 95 What Hyperparameter Tuning Is?

Lecture 96 Define the Hyperparameters Search Space

Lecture 97 Sampling the Hyperparameter Space

Lecture 98 Specify Early Termination Policy

Lecture 99 Configuring the Hyperdrive Run - Part 1

Lecture 100 Configuring the Hyperdrive Run - Part 2

Lecture 101 Creating the Hyperdrive Training Script

Lecture 102 Getting the Best Model and Hyperparameters

Section 13: Use Model Explainers to Interpret Models

Lecture 103 Interpretability Techniques in Azure

Lecture 104 Model Explainer on Local Machine

Lecture 105 Model Explainer in AzureML Part 1

Lecture 106 Model Explainer in AzureML Part 2

Section 14: Model Registration and Deployment Using Azureml SDK

Lecture 107 Introduction to Serialization and Deserialization

Lecture 108 Serialization Using Joblib

Lecture 109 Deserialization Using Joblib

Lecture 110 Handling Dummy Variables in Production

Lecture 111 Train ML Model for Webservice Deployment

Lecture 112 Register the Model Using Run ID pkl File

Lecture 113 Register the Model Using Local pkl File

Lecture 114 Provision AKS Production Cluster

Lecture 115 Revising the Steps Learned

Lecture 116 Project 3: Step 1 (Creating and Accessing the Workspace)

Lecture 117 Project 3: Step 2 (Train and Serialize ML Model)

Lecture 118 Project 3: Step 3 (Register the Model to Workspace)

Lecture 119 Project 3: Step 4 (Register an Environment)

Lecture 120 Project 3: Step 5 (Create AKS Cluster)

Lecture 121 Project 3: Step 6 (Inference and Deployment Configuration)

Lecture 122 Project 3: Step 7 (Creating the Entry Script)

Lecture 123 Project 3: Step 8 (Creating an Endpoint)

Lecture 124 Project 3: Step 9 (Testing the Web Service)

Lecture 125 Project 4: Deploy Multiple Models as Webservice (Step 1)

Lecture 126 Project 4: Deploy Multiple Models as Webservice (Step 2)

Lecture 127 Project 4: Deploy Multiple Models as Webservice (Step 3)

Lecture 128 Project 4: Deploy Multiple Models as Webservice (Step 4)

Section 15: Azure Fundamentals: Virtual Machines

Lecture 129 Introduction to Azure Virtual Machines

Lecture 130 Creating Virtual Machine in Azure

Lecture 131 Connecting to Virtual Machine and Running Commands

Lecture 132 Key Concepts - Image, Size and Disks

Lecture 133 Commands executed in Tutorial

Lecture 134 Installing nginx on Azure Virtual Machine

Lecture 135 Commands executed in Tutorial

Lecture 136 Simplification of Software Installation on Azure Virtual Machine

Lecture 137 Availability Sets and Zones

Lecture 138 Virtual Machine Scale Sets

Lecture 139 Scaling and Load Balancing with VM Scale Sets

Lecture 140 Static IP, Monitoring, Dedicated Host and Reducing the Cost

Lecture 141 Designing Good Solutions with Azure VMs

Section 16: Azure Fundamentals: Managed Compute Services

Lecture 142 Introduction to Azure Managed Compute Services

Lecture 143 Introduction to IaaS, PaaS and SaaS

Lecture 144 Introduction to Azure App Service

Lecture 145 Creating First Web App using Azure App Service

Lecture 146 More about the Azure App Service

Lecture 147 Introduction to Containers

Lecture 148 Introduction to Azure Container Instances

Lecture 149 Container Orchestration - AKS and Service Fabric

Lecture 150 Introduction to Azure Serverless

Lecture 151 Azure Serverless Service - Azure Functions

Lecture 152 Logic Apps

Lecture 153 Azure Shared Responsibility Model

Lecture 154 Review - Azure Compute Services

Lecture 155 Deleting Recourse Groups

Section 17: Azure Fundamentals: Storage

Lecture 156 Introduction to Azure Storage

Lecture 157 Managed and Unmanaged Block Storage in Azure

Lecture 158 Azure Files

Lecture 159 Azure Blob Storage and Tiers

Section 18: Azure Fundamentals: Databases

Lecture 160 Introduction to Database

Lecture 161 Snapshots, Transaction Logs, Standby Database

Lecture 162 RTO and RPO

Lecture 163 Data Consistency

Lecture 164 How to Select a Database ?

Lecture 165 Introduction to Relational Database

Lecture 166 Relational Database-OLTP

Lecture 167 Creating MySQL Server in Azure

Lecture 168 Code executed in next tutorial

Lecture 169 Exploring MySQL Server in Azure

Lecture 170 Relational Database - OLAP (Online Analytics Processing)

Lecture 171 Azure NoSQL Database: Azure Cosmos DB

Lecture 172 Exploring Azure NoSQL Database: Azure Cosmos DB

Lecture 173 Azure In-Memory Database: Azure Cache for Redis

Lecture 174 Review: Databases

Lecture 175 Databases: Scenarios

Lecture 176 Deleting Database Recourse Groups

Anyone who wants to learn Azure Machine Learning,Students and Professionals Who Wants to Pass DP-100 Exam