ML Fundamentals
Get an introduction to both the theoretical and the applicable knowledge of data science methods using Python programming language.
Learn how to:
- Describe what data science is and what data science process is
- Explain the difference between supervised and unsupervised learning
- Perform exploratory data analysis to troubleshoot datasets
- Build supervised machine learning models in Python
- Conduct analyses using unsupervised machine learning algorithms
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ML Engineering
Machine learning engineering is the practice of applying machine learning science to production systems. It requires expertise in both machine learning methods and software engineering.
Learn how to:
- Validate problem definition and approach given available data and methods
- Select preliminary models
- Recreate results on benchmark data sets
- Develop training and validation data sets
- Automate experiment tracking and measurement
- Adapt models and tune performance
- Deploy and support production models
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API design
Cover key skills for creating consistently successful APIs and simple tools that you can use to turn those skills into working models, sketches, and running code.
Learn how to:
- Write API stories
- Diagram APIs with Web Sequence Diagrams
- Describe APIs with ALPS Profiles
- Generate multiple working API Sketches with API Blueprint
- Select a candidate sketch to Prototype with OpenAPISpec
- Build a fully-functional API using NodeJS, Express, and the DORR framework
- Test APIs with Postman and Newman
- Deploy your API to Heroku via Git & Github
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Security
Learn the fundamentals required to analyze your designs for security issues and create security first solutions to new problems.
Explore software security fundamentals including:
- Threat Modeling
- Securing distributed systems and services
- Tiny Types/Type Safety
- Designing for observation and audit
- Managing the software supply chain
- Security testing
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