What do you think?
Rate this book
Microsoft Certified: Azure AI Engineer Associate Exam Study Guide
Microsoft Azure AI Engineer Associate Exam Study Guide
The book is a Study Guide for Microsoft Azure AI Engineer Associate Exam and covers the
Chapter 1: Introduction to Azure AI and Machine Learning
Introduction to Azure AI services and capabilities
Overview of Azure Machine Learning service
Azure AI and Machine Learning development process
Understanding data science and machine learning concepts
Ethical considerations in AI and responsible AI practices
Chapter 2: Azure Machine Learning Workspace and Data Preparation
Setting up Azure Machine Learning workspace
Managing and organizing experiments in Azure ML
Working with datastores and datasets in Azure ML
Data preprocessing and feature engineering in Azure ML
Data sampling, splitting, and balancing techniques in Azure ML
Chapter 3: Machine Learning Model Training and Evaluation in Azure ML
Choosing the right machine learning algorithm in Azure ML
Supervised and unsupervised learning techniques in Azure ML
Training and fine-tuning machine learning models in Azure ML
Hyperparameter tuning and model optimization in Azure ML
Evaluating model performance and metrics selection in Azure ML
Chapter 4: Azure Machine Learning Pipelines and Deployments
Introduction to Azure ML pipelines and workflows
Creating and managing ML pipelines in Azure ML
Data ingestion and transformation in pipelines in Azure ML
Model training and deployment in Azure ML pipelines
Monitoring and versioning ML pipelines in Azure
Chapter 5: Natural Language Processing (NLP) in Azure
Introduction to NLP and language understanding in Azure
Text classification and sentiment analysis in Azure
Entity recognition and named entity recognition (NER) in Azure
Text summarization and language translation in Azure
Building chatbots and conversational AI with Azure Bot Service
Chapter 6: Computer Vision and Image Recognition in Azure
Introduction to computer vision and image processing in Azure
Object detection and image classification in Azure
Image segmentation and semantic segmentation in Azure
Custom vision and transfer learning in computer vision in Azure
Anomaly detection and image similarity in computer vision in Azure
Chapter 7: Speech Recognition and Speech Synthesis in Azure
Introduction to speech recognition and speech-to-text conversion in Azure
Building speech recognition models with Azure Cognitive Services
Language understanding and intent recognition in speech in Azure
Text-to-speech synthesis and customization in Azure
Multilingual and multi-channel speech processing in Azure
Chapter 8: Azure Cognitive Services and Custom AI Models
Overview of Azure Cognitive Services offerings
Implementing computer vision with Azure Cognitive Services
Natural Language Processing (NLP) with Azure Cognitive Services
Speech and audio processing with Azure Cognitive Services
Building custom AI models with Azure Machine Learning
Chapter 9: Reinforcement Learning and Decision Making in Azure
Introduction to reinforcement learning concepts in Azure
Markov Decision Processes (MDPs) and Q-Learning in Azure
Value iteration and policy iteration algorithms in Azure
Applying reinforcement learning in real-world scenarios in Azure
Model-based and model-free reinforcement learning in Azure
Chapter 10: Deploying and Managing AI Solutions in Azure
Chapter 11: Responsible AI and Governance in Azure
Chapter 12: Azure AI Engineer Associate Exam Preparation and Practice
The book is a Study Guide for Microsoft Azure AI Engineer Associate Exam and covers the
Chapter 1: Introduction to Azure AI and Machine Learning
Introduction to Azure AI services and capabilities
Overview of Azure Machine Learning service
Azure AI and Machine Learning development process
Understanding data science and machine learning concepts
Ethical considerations in AI and responsible AI practices
Chapter 2: Azure Machine Learning Workspace and Data Preparation
Setting up Azure Machine Learning workspace
Managing and organizing experiments in Azure ML
Working with datastores and datasets in Azure ML
Data preprocessing and feature engineering in Azure ML
Data sampling, splitting, and balancing techniques in Azure ML
Chapter 3: Machine Learning Model Training and Evaluation in Azure ML
Choosing the right machine learning algorithm in Azure ML
Supervised and unsupervised learning techniques in Azure ML
Training and fine-tuning machine learning models in Azure ML
Hyperparameter tuning and model optimization in Azure ML
Evaluating model performance and metrics selection in Azure ML
Chapter 4: Azure Machine Learning Pipelines and Deployments
Introduction to Azure ML pipelines and workflows
Creating and managing ML pipelines in Azure ML
Data ingestion and transformation in pipelines in Azure ML
Model training and deployment in Azure ML pipelines
Monitoring and versioning ML pipelines in Azure
Chapter 5: Natural Language Processing (NLP) in Azure
Introduction to NLP and language understanding in Azure
Text classification and sentiment analysis in Azure
Entity recognition and named entity recognition (NER) in Azure
Text summarization and language translation in Azure
Building chatbots and conversational AI with Azure Bot Service
Chapter 6: Computer Vision and Image Recognition in Azure
Introduction to computer vision and image processing in Azure
Object detection and image classification in Azure
Image segmentation and semantic segmentation in Azure
Custom vision and transfer learning in computer vision in Azure
Anomaly detection and image similarity in computer vision in Azure
Chapter 7: Speech Recognition and Speech Synthesis in Azure
Introduction to speech recognition and speech-to-text conversion in Azure
Building speech recognition models with Azure Cognitive Services
Language understanding and intent recognition in speech in Azure
Text-to-speech synthesis and customization in Azure
Multilingual and multi-channel speech processing in Azure
Chapter 8: Azure Cognitive Services and Custom AI Models
Overview of Azure Cognitive Services offerings
Implementing computer vision with Azure Cognitive Services
Natural Language Processing (NLP) with Azure Cognitive Services
Speech and audio processing with Azure Cognitive Services
Building custom AI models with Azure Machine Learning
Chapter 9: Reinforcement Learning and Decision Making in Azure
Introduction to reinforcement learning concepts in Azure
Markov Decision Processes (MDPs) and Q-Learning in Azure
Value iteration and policy iteration algorithms in Azure
Applying reinforcement learning in real-world scenarios in Azure
Model-based and model-free reinforcement learning in Azure
Chapter 10: Deploying and Managing AI Solutions in Azure
Chapter 11: Responsible AI and Governance in Azure
Chapter 12: Azure AI Engineer Associate Exam Preparation and Practice
297 pages, Kindle Edition
Published June 10, 2023
4 people are currently reading
Ratings & Reviews
Friends & Following
Create a free account to discover what your friends think of this book!
Community Reviews
No one has reviewed this book yet.