Mistakes I Made While Learning Machine Learning

Introduction

Learning Machine Learning is challenging. When starting out, it is easy to get overwhelmed by mathematics or stuck in tutorial loops without building real engineering skills. Reflecting on my learning journey, I identified several common mistakes that slowed down my progress.

Background

Machine Learning requires understanding math (linear algebra, calculus, statistics) along with software engineering. However, trying to master all the mathematics before writing any code is a common mistake that delays practical progress.

Implementation

I cataloged my primary learning mistakes and the adjustments that helped me progress:

• Prioritizing math over coding: I spent months reading textbooks instead of building datasets and training models.
• Ignoring data quality: I focused on tuning algorithms instead of cleaning raw datasets, which is often the most important step in model performance.
• Tutorial loops: Following guided tutorials without writing custom code or troubleshooting errors independently.

Challenges

Shifting from theory to implementation presented challenges:

• Overfitting Models: Training models that performed well on test data but failed completely on real-world inputs.
• Experimental Noise: Forgetting which parameters I used in previous training runs, causing inconsistent results.

Solutions

I adopted two standard workflows: 1. **Cross-Validation**: Using k-fold cross-validation to verify model generalization. 2. **Experiment Tracking**: Integrating tracking tools (like MLflow) to log parameters and dataset versions automatically on every run.

Results

Prioritizing data cleaning improved my model accuracy scores by 18%, and experiment tracking eliminated parameter loss, helping me replicate successful models easily.

Conclusion

Learn machine learning by building. Start with clean datasets, train simple models, track your experiments, and study the mathematics as you work to resolve specific problems.