Machine Learning Concepts
- Supervised Learning: Training a model on labeled data to predict outcomes*
- Unsupervised Learning: Training a model on unlabeled data to identify patterns*
- Reinforcement Learning: Training a model to maximize a reward signal*
- Regression: Predicting continuous values*
- Classification: Predicting categorical values*
- Clustering: Grouping similar data points*
- Neighborhood Components Analysis (NCA): A non-parametric algorithm for dimensionality reduction*
Training Scoring Methods
- Accuracy: Proportion of correct predictions*
- Precision: Proportion of true positives among all positive predictions*
- Recall: Proportion of true positives among all actual positive instances*
- F1 Score: Harmonic mean of precision and recall*
- Mean Squared Error (MSE): Average difference between predicted and actual values*
- Mean Absolute Error (MAE): Average absolute difference between predicted and actual values*
- R-Squared (R²): Proportion of variance explained by the model*
Azure Machine Learning Services
- Azure Machine Learning (AML): A cloud-based platform for building, training, and deploying machine learning models*
- Azure Automated Machine Learning (AutoML): A service for automating machine learning model selection and hyperparameter tuning*
- Azure HyperDrive: A service for hyperparameter tuning and model selection*
- Azure Kubeflow: A cloud-based platform for building, training, and deploying machine learning models with Kubernetes*
Azure Cognitive Services
- Vision: Image processing and analysis*
- Decision: Decision support and recommendation systems*
- Speech: Speech recognition and synthesis*
- Language: Natural language processing and text analysis*
- Translator Text: Text translation and localization*
Azure Storage Services
- Azure Blob Storage: Unstructured data storage*
- Azure File Storage: Structured data storage*
- Azure Data Lake Storage: High-performance data storage for big data analytics*
Data Movement and Integration
- Azure Data Factory (ADF): A cloud-based data integration service*
- Azure Databricks: A fast, easy, and collaborative Apache Spark-based analytics platform*
- Azure Synapse Analytics: A cloud-based enterprise analytics service*
Security and Compliance
- Azure Role-Based Access Control (RBAC): Role-based access control for Azure resources*
- Azure Active Directory (AAD): Identity and access management for Azure resources*
- Azure Key Vault: Secure storage and management of cryptographic keys and secrets*
Monitoring and Optimization
- Azure Monitor: A cloud-based monitoring service for Azure resources*
- Azure Application Insights: A cloud-based application performance monitoring service*
- Azure Advisor: A cloud-based optimization service for Azure resources*
Best Practices
- Data Validation: Validate data before training a model*
- Data Preprocessing: Preprocess data before training a model*
- Model Selection: Select a model that is appropriate for the problem*
- Hyperparameter Tuning: Tune hyperparameters to optimize model performance*
- Model Evaluation: Evaluate a model’s performance on a test dataset*
Common Azure AI-102 Exam Questions
- What is the difference between supervised and unsupervised learning?
- How do you handle missing data in a dataset?
- What is the purpose of regularization in machine learning?
- How do you evaluate the performance of a regression model?
- What is the difference between Azure Machine Learning and Azure Automated Machine Learning?