I finished the course!

I really enjoyed the learning experiences in Andrew's course so far.

Let's see what I've learn for the two days!

Overfitting - The Last Topic of this Course!

Overfitting occurs when a machine learning model learns the details and noise in the training data to an extent that it negatively impacts the performance of the model on new data. This means the model is great at predicting or fitting the training data but performs poorly on unseen data, due to its inability to generalize from the training set to the broader population of data.

The course explains that overfitting can be addressed by:

• Reducing model complexity: Simplifying the model by selecting one with fewer parameters.
• Regularization: Adding a regularization term to the loss function, which penalizes large coefficients.
• Using more training data: More data can help the model learn more generalizable patterns.

We can't bypass underfitting.

Overfitting and underfitting both are undesirable effects that suggest a model is not well-tuned to the task at hand, but they stem from opposite causes and have different solutions.

Below two screenshots captured from course for my notes:

Questions help me to master the content

1. What is overfitting (aka. high variance)? How to address it?
   Solution to address:
   a. getting more training data.
   b. select features to exclude/include (because more features + insufficient data) will cause overfitting). However, useful features could be lost
   c. Regularization - reduce size of parameters - Encourage the learning algorithm to shrink the values of the parameters without necessarily demanding that the parameter is set to exactly 0.
2. What is underfitting?
3. What is ƛ? How would it impact the learning algorithm if choose very large or small value?
4. In practice, dose regularizing b make much difference or not?
5. Give an example explaining what is preconception (aka. bias or underfit)
6. What is generalization?
   You want your learning algorithm to generalize well, which means to make good predictions even on brand new examples that never seen before.
7. (The hardest one) Write down all the formulas taught in videos and explain how they could be implemented in python!

Words From Andrew At The End!

I want to say congratulations on how far you've come and I want to say great job for getting through all the way to the end of this video. 

I hope you also work through the practice labs and quizzes. 

Having said that, there are still many more exciting things to learn.

Awesome!

I am already ready for next machine learning journeys!