DP100 Azure Machine Learning Data Science Exam Prep 2023

Azure Machine Learning, AzureML, Exam DP-100: Designing and Implementing a Data Science Solution, 4 End-to-End Projects
DP100 Azure Machine Learning Data Science Exam Prep 2023
File Size :
8.36 GB
Total length :
20h 56m

Category

Instructor

Vijay Gadhave

Language

Last update

Last updated 12/2022

Ratings

4.4/5

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

DP100 Azure Machine Learning Data Science Exam Prep 2023

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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