Have you ever delved into the mysterious world of training machine learning models? 
Let’s explore the captivating realm of learning curves and model evaluation, where it’s not just about making predictions but truly understanding how our models learn! 
Attention all coders! Dive deeper into the process by checking out the full Colab notebook [Google Colab Notebook].
The Set-Up: Unraveling Google Stock Prices with Moving Averages 
Picture this: predicting Google’s stock prices using the 50-day and 200-day Simple Moving Averages (SMAs) as our technical indicators, armed with data spanning 2010 to 2024 (now that’s a data goldmine! 
). But hold on, it gets even more riveting…
It’s not a one-model show here. Oh no, we’re putting three different powerhouses to the test:
1. Linear Regression (the timeless classic! 
)
2. Support Vector Regression (SVR — the agile contender 
)
3. Kernel Ridge Regression (the epitome of synthesis? 
)
The Twist: Unveiling Learning Curves 
Here’s where the plot thickens as we reveal the learning curves…