Overview
I successfully completed "Structuring Machine Learning Projects," the third course in the Deep Learning Specialization on Coursera. This course provided essential guidance on how to effectively structure machine learning projects, covering:
- ML Strategy: How to set up your machine learning project for success.
- ML Development Process: Understanding how to split your data sets (train/dev/test), bias vs. variance.
- Error Analysis: How to perform error analysis.
- Training Neural Networks: How to set up your data for neural network training.
This course equipped me with the knowledge to approach machine learning projects in a systematic and organized manner.
Key Concepts Covered
- Orthogonalization
- Setting up development and test sets
- Bias vs. Variance
- Basic Error Analysis
- Building machine learning system
- Error Analysis
- Data Mismatch
- Learning from Multiple Tasks
- End-to-end Deep Learning