Machine Learning
Advanced
Evaluation-First Machine Learning
Improve machine-learning systems by measuring the right failures first.
Course Overview
Design representative test sets, slice errors, and compare changes with discipline. The material helps teams avoid shipping impressive averages that hide unacceptable behavior.
What You Will Learn
Master core concepts and practical workflows in Evaluation-First Machine Learning
Apply industry best practices to build production-ready Machine Learning solutions
Utilize modern tools including machine learning, evaluation, benchmarks effectively
Diagnose common execution failures, performance bottlenecks, and edge cases
Establish repeatable engineering habits for continuous improvement
Tools & Technologies Used
Machine Learning
Evaluation
Benchmarks
Error Analysis
Python 3.11
VS Code
Structured Curriculum
2 Modules · 6 Lessons · 230 Minutes Total
Module 1
Module 1: Fundamentals of Evaluation-First Machine Learning
3 lessons
Module 1: Fundamentals of Evaluation-First Machine Learning
Explore core principles, setup, and foundational concepts of Evaluation-First Machine Learning.
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Introduction to Evaluation-First Machine Learning Principles
Deconstruct core building blocks and architecture of Evaluation-First Machine Learning.
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Core Workflows with Machine Learning
Hands-on demonstration of primary tools and API patterns.
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Practical Implementation & Configuration
Build your first working module with error validation.
Module 2
Module 2: Advanced Patterns & Real-World Application
3 lessons
Module 2: Advanced Patterns & Real-World Application
Master practical engineering patterns and deploy resilient Machine Learning projects.
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Handling Edge Cases & Error Boundaries
Implement defensive programming and robust exception management.
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Performance Optimization & Benchmarking
Benchmark performance, identify bottlenecks, and apply efficiency gains.
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Production Integration & Review
Assemble the final project, run test suites, and review deployment steps.
Practical Project & Capstone Outcome
🚀 Capstone Project
Production Evaluation-First Machine Learning Capstone Project
Build and test a complete, real-world application demonstrating all core skills learned in Evaluation-First Machine Learning, featuring comprehensive tests and documentation.
Prerequisites
- Basic familiarity with Machine Learning concepts
- A working computer with command-line terminal access
Intended Audience
- Practitioners wanting to build expertise in Evaluation-First Machine Learning
- Engineers and Analysts working with Machine Learning tools
Instructor Information
Y
Yuki Tan
Course Author & Industry Expert
Yuki Tan is a seasoned industry professional with over 8 years of hands-on experience in Machine Learning and technical education.
Frequently Asked Questions
What background knowledge is required for Evaluation-First Machine Learning?
A foundational understanding of Machine Learning is helpful, but all key concepts are explained step-by-step.
Is source code provided for all exercises?
Yes! All lessons include repository code samples and step-by-step solution guides.