Feature Engineering & Model Evaluation
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10 lessons3 levels1 capstone project
Engineer informative features and evaluate models rigorously with train/validation/test design, cross-validation, leakage prevention, appropriate metrics, calibration and error analysis.
BASIC
- Feature Engineering in Machine Learning (Medium)
Shows how raw data becomes model-ready signals and why features matter. - The Basics of Evaluating Machine Learning Projects (Medium)
Connects project workflow to validation and evaluation decisions. - Evaluation Metrics for Machine Learning Models (Long)
Explains classification metrics and how to choose measures aligned to the task.
INTERMEDIATE
- Machine Learning Full Course for Beginners
Apply Feature Engineering & Model Evaluation through the adjacent capability: Machine Learning. - Machine Learning with Python and Scikit-Learn — freeCodeCamp
Apply Feature Engineering & Model Evaluation through the adjacent capability: Machine Learning. - Deep Learning Lecture 1 — MIT OpenCourseWare
Apply Feature Engineering & Model Evaluation through the adjacent capability: Deep Learning.
ADVANCED
- Step by Step — Simple Neural Network with TensorFlow
Extend Feature Engineering & Model Evaluation into senior/production practice through Deep Learning. - Step by Step — Machine Learning Project Workflow
Extend Feature Engineering & Model Evaluation into senior/production practice through Machine Learning. - OpenCV Course with Python — freeCodeCamp
Extend Feature Engineering & Model Evaluation into senior/production practice through Computer Vision.