DP-100: Azure Machine Learning & Data Science Exam Prep 2023

سرفصل های دوره

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

1 - Getting Started with Azure ML

1 - Introduction to Azure Machine Learning

2 - Introduction to Azure Machine Learning Studio

3 - Azure ML Cheat Sheet

4 - DP100 Exam Skills Measured Exam Curriculum

2 - Setting up Azure Machine Learning Workspace

5 - Azure ML Architecture and Concepts

6 - Creating AzureML Workspace

7 - Workspace Overview

8 - AzureML Studio Overview

9 - Introduction to Azure ML Datasets and Datastores

10 - Creating a Datastore

11 - Creating a Dataset

12 - Exploring AzureML Dataset

13 - Introduction to Azure ML Compute Resources

14 - Creating Compute Instance and Compute Cluster

15 - Deleting the Resources

3 - Running Experiments and Training Models

16 - Azure ML Pipeline

17 - Creating New Pipeline using AzureML Designer

18 - Submitting the Designer Pipeline Run

4 - Deploying the Models

19 - Creating RealTime Inference Pipeline

20 - Deploying RealTime Endpoint in AzureML Designer

21 - Creating Batch Inference Pipeline in AzureML Designer

22 - Running Batch Inference Pipeline in AzureML Designer

23 - Deleting the Resources

5 - AzureML Designer Data Preprocessing

24 - Setting up Workspace and Compute Resources

25 - Sample Datasets

26 - Select Columns in Dataset

27 - Importing External Dataset From Web URL

28 - Edit Metadata Column Names

29 - Edit Metadata Feature Type and Data Type

30 - Creating Storage Account Datastore and Datasets

31 - Adding Columns From One Dataset to Another One

32 - Adding Rows From One Dataset to Another One

33 - Clean Missing Data Module

34 - Splitting the Dataset

35 - Normalizing Dataset

36 - Exporting Data to Blob Storage

37 - Deleting the Resources

6 - Project 1 Regression Using AzureML Designer

38 - Creating Workspace Compute Resources Storage Account Datastore and Dataset

39 - Business Problem

40 - Analyzing the Dataset

41 - Data Preprocessing

42 - Training ML Model with Linear Regression Online Gradient Descent

43 - Evaluating the Results

44 - Training ML Model with Linear Regression Ordinary least squares

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

46 - Finalizing the ML Model

47 - Creating and Deploying RealTime Inference Pipeline

48 - Creating and Deploying Batch Inference Pipeline

49 - Deleting the Resources

7 - Project 2 Classification Using AzureML Designer

50 - Creating Workspace Compute Resources Storage Account Datastore and Dataset

51 - Business Problem

52 - Analyzing the Dataset

53 - Data Preprocessing

54 - Training ML Model with TwoClass Logistic Regression

55 - Training ML Model with TwoClass SVM

56 - Training ML Model with TwoClass Boosted Decision Tree & Decision Forest

57 - Finalizing the ML Model

58 - Creating and Deploying Batch Inference Pipeline

8 - AzureML SDK Setting up Azure ML Workspace

59 - AzureML SDK Introduction

60 - Creating Workspace using AzureMl SDK

61 - Creating a Datastore using AzureMl SDK

62 - Creating a Dataset using AzureMl SDK

63 - Accessing the Workspace Datastore and Dataset with AzureML SDK

64 - AzureML Dataset and Pandas Dataset Conversion

65 - Uploading Local Datasets to Storage Account

9 - AzureML SDK Running Experiments and Training Models

66 - Running Sample Experiment in AzureML Environment

67 - Logging Values to Experiment in AzureML Environment

68 - Introduction to Azure ML Environment

69 - Running Script in AzureML Environment Part 1

70 - Running Script in AzureML Environment Part 2

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

72 - Logistic Regression in Local Environment Part 1

73 - Logistic Regression in Local Environment Part 2

74 - Creating Python Script Logistic Regression

75 - Running Python Script for Logistic Regression in AzureML Environment

76 - logconfusionmatrix Method

77 - Provisioning Compute Cluster in AzureML SDK

78 - Automate Model Training Introduction

79 - Automate Model Training Pipeline Run Part 1

80 - Automate Model Training Pipeline Run Part 2

81 - Automate Model Training Data Processing Script

82 - Automate Model Training Model Training Script

83 - Automate Model Training Running the Pipeline

10 - Use Automated ML to Create Optimal Models

84 - Introduction to Automated ML

85 - Automated ML in Azure Machine Learning studio

86 - Automated ML in Azure Machine Learning SDK

11 - Tune hyperparameters with Azure Machine Learning

87 - What Hyperparameter Tuning Is

88 - Define the Hyperparameters Search Space

89 - Sampling the Hyperparameter Space

90 - Specify Early Termination Policy

91 - Configuring the Hyperdrive Run Part 1

92 - Configuring the Hyperdrive Run Part 2

93 - Creating the Hyperdrive Training Script

94 - Getting the Best Model and Hyperparameters

12 - Use model explainers to interpret models

95 - Interpretability Techniques in Azure

96 - Model Explainer on Local Machine

97 - Model Explainer in AzureML Part 1

98 - Model Explainer in AzureML Part 2

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تولید کننده: Udemy-Training