Dp-100: Azure Machine Learning & Data Science For Beginners

Exam DP-100: Designing and Implementing a Data Science Solution on Azure Covered, Learn Azure Machine Learning

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)

Section 2: Setting up Azure Machine Learning Workspace

Lecture 5 Azure ML: Architecture and Concepts

Lecture 6 Creating AzureML Workspace

Lecture 7 Workspace Overview

Lecture 8 AzureML Studio Overview

Lecture 9 Introduction to Azure ML Datasets and Datastores

Lecture 10 Creating a Datastore

Lecture 11 Creating a Dataset

Lecture 12 Exploring AzureML Dataset

Lecture 13 Introduction to Azure ML Compute Resources

Lecture 14 Creating Compute Instance and Compute Cluster

Lecture 15 Deleting the Resources

Section 3: Running Experiments and Training Models

Lecture 16 Azure ML Pipeline

Lecture 17 Creating New Pipeline using AzureML Designer

Lecture 18 Submitting the Designer Pipeline Run

Section 4: Deploying the Models

Lecture 19 Creating Real-Time Inference Pipeline

Lecture 20 Deploying Real-Time Endpoint in AzureML Designer

Lecture 21 Creating Batch Inference Pipeline in AzureML Designer

Lecture 22 Running Batch Inference Pipeline in AzureML Designer

Lecture 23 Deleting the Resources

Section 5: AzureML Designer: Data Preprocessing

Lecture 24 Setting up Workspace and Compute Resources

Lecture 25 Sample Datasets

Lecture 26 Select Columns in Dataset

Lecture 27 Importing External Dataset From Web URL

Lecture 28 Edit Metadata - Column Names

Lecture 29 Edit Metadata - Feature Type and Data Type

Lecture 30 Creating Storage Account, Datastore and Datasets

Lecture 31 Adding Columns From One Dataset to Another One

Lecture 32 Adding Rows From One Dataset to Another One

Lecture 33 Clean Missing Data Module

Lecture 34 Splitting the Dataset

Lecture 35 Normalizing Dataset

Lecture 36 Exporting Data to Blob Storage

Lecture 37 Deleting the Resources

Section 6: Project 1: Regression Using AzureML Designer

Lecture 38 Creating Workspace, Compute Resources, Storage Account, Datastore and Dataset

Lecture 39 Business Problem

Lecture 40 Analyzing the Dataset

Lecture 41 Data Preprocessing

Lecture 42 Training ML Model with Linear Regression (Online Gradient Descent)

Lecture 43 Evaluating the Results

Lecture 44 Training ML Model with Linear Regression (Ordinary least squares)

Lecture 45 Training ML Model with Boosted Decision Tree and Decision Forest Regression

Lecture 46 Finalizing the ML Model

Lecture 47 Creating and Deploying Real-Time Inference Pipeline

Lecture 48 Creating and Deploying Batch Inference Pipeline

Lecture 49 Deleting the Resources

Section 7: Project 2: Classification Using AzureML Designer

Lecture 50 Creating Workspace, Compute Resources, Storage Account, Datastore and Dataset

Lecture 51 Business Problem

Lecture 52 Analyzing the Dataset

Lecture 53 Data Preprocessing

Lecture 54 Training ML Model with Two-Class Logistic Regression

Lecture 55 Training ML Model with Two-Class SVM

Lecture 56 Training ML Model with Two-Class Boosted Decision Tree & Decision Forest

Lecture 57 Finalizing the ML Model

Lecture 58 Creating and Deploying Batch Inference Pipeline

Section 8: AzureML SDK: Setting up Azure ML Workspace

Lecture 59 AzureML SDK Introduction

Lecture 60 Creating Workspace using AzureMl SDK

Lecture 61 Creating a Datastore using AzureMl SDK

Lecture 62 Creating a Dataset using AzureMl SDK

Lecture 63 Accessing the Workspace, Datastore and Dataset with AzureML SDK

Lecture 64 AzureML Dataset and Pandas Dataset Conversion

Lecture 65 Uploading Local Datasets to Storage Account

Section 9: AzureML SDK: Running Experiments and Training Models

Lecture 66 Running Sample Experiment in AzureML Environment

Lecture 67 Logging Values to Experiment in AzureML Environment

Lecture 68 Introduction to Azure ML Environment

Lecture 69 Running Script in AzureML Environment Part 1

Lecture 70 Running Script in AzureML Environment Part 2

Lecture 71 Uploading the output file to Existing run in AzureML Environment

Lecture 72 Logistic Regression in Local Environment Part 1

Lecture 73 Logistic Regression in Local Environment Part 2

Lecture 74 Creating Python Script - Logistic Regression

Lecture 75 Running Python Script for Logistic Regression in AzureML Environment

Lecture 76 log_confusion_matrix Method

Lecture 77 Provisioning Compute Cluster in AzureML SDK

Lecture 78 Automate Model Training - Introduction

Lecture 79 Automate Model Training - Pipeline Run Part 1

Lecture 80 Automate Model Training - Pipeline Run Part 2

Lecture 81 Automate Model Training -Data Processing Script

Lecture 82 Automate Model Training - Model Training Script

Lecture 83 Automate Model Training - Running the Pipeline

Section 10: Use Automated ML to Create Optimal Models

Lecture 84 Introduction to Automated ML

Lecture 85 Automated ML in Azure Machine Learning studio

Lecture 86 Automated ML in Azure Machine Learning SDK

Section 11: Tune hyperparameters with Azure Machine Learning

Lecture 87 What Hyperparameter Tuning Is?

Lecture 88 Define the Hyperparameters Search Space

Lecture 89 Sampling the Hyperparameter Space

Lecture 90 Specify Early Termination Policy

Lecture 91 Configuring the Hyperdrive Run - Part 1

Lecture 92 Configuring the Hyperdrive Run - Part 2

Lecture 93 Creating the Hyperdrive Training Script

Lecture 94 Getting the Best Model and Hyperparameters

Section 12: Use model explainers to interpret models

Lecture 95 Interpretability Techniques in Azure

Lecture 96 Model Explainer on Local Machine

Lecture 97 Model Explainer in AzureML Part 1

Lecture 98 Model Explainer in AzureML Part 2

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