DP-100T01: Designing and Implementing a Data Science Solution on Azure Course Overview Following courses are similar to DP-100T01: Designing and Implementing a Data Science Solution on Azure
DP-100T01: Designing and Implementing a Data Science Solution on Azure, offered by Microsoft, equips professionals with essential skills for creating data-driven solutions. This course is vital for data scientists, machine learning engineers, and AI developers, enhancing their ability to leverage Azure’s tools and frameworks for effective data analysis and model deployment, ultimately driving business insights and innovation.
Course outline & what you'll learn
- Introduction to Data Science on Azure
Overview of Data Science concepts
- Introduction to Azure Machine Learning
- Data Exploration
- Data collection and preparation
- Exploratory data analysis techniques
- Feature engineering
- Model Training
- Selecting appropriate algorithms
- Training machine learning models
- Hyperparameter tuning
- Model Evaluation
- Evaluating model performance
- Metrics for classification and regression
- Cross-validation techniques
- Model Deployment
- Deploying models as web services
- Monitoring and managing deployed models
- Retraining models with new data
- Data Preparation and Feature Engineering
- Data wrangling techniques
- Working with Azure Data Factory
- Using Azure Databricks for data processing
- Implementing Machine Learning Pipelines
- Creating end-to-end workflows
- Automating data science processes
- Versioning and reproducibility
- Advanced Machine Learning Techniques
- Ensembling methods
- Time series analysis
- Natural language processing
- Ethical Considerations in Data Science
- Addressing bias in data and models
- Data privacy and security practices
- Capstone Project
- Real-world data science problem-solving
- Presenting and communicating results
- Best practices for documentation and reporting
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