About This Course
Artificial intelligence and machine learning come with huge benefits — and pitfalls. Tasks that once required specialized skills, large teams, complex workflows and deep algorithmic tuning can now be accelerated, automated or simulated using AI/ML tools. But the more accessible these tools become, the more important it is to know how to apply rigorous statistical methods and engineering standards to build models with integrity.
In this course, you’ll learn to harness the power of AI to quickly build machine learning models that meet best practices in data science. First, you’ll build a traditional model from scratch, applying AI tools at every step to automate ML workflows and speed your work. Then, you’ll build a native AI application over a foundation model, giving you the real-world experience to develop AI capabilities at any organization.
Designed For
Software engineers and Python programmers who want to use AI-powered technologies for rapid ML modeling.
What You'll Learn
- Fundamental best practices in data science
- How to select appropriate AI tools for ML pre-processing in your workflow
- How to build retrieval-augmented generation (RAG) models over your own data using foundation models
- Approaches to address security, ethics, compliance and scaling
Earn a Digital Badge
After successfully completing this course, you can claim a digital achievement badge that can be shared on LinkedIn and other social media sites. Learn more about digital badges.
Our Enrollment Coaches Are Here to Help
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This program is intended for professional development and is not designed to meet educational requirements for professional licensure or certification.
