Supervised learning

[Christian_Simonis]

Hi there,

I suggest you take a look at the course content again since the underlying concepts are explained really well. And they are important to understand since several future concepts will build upon these basics.

A cost function provides a metric you can use to improve your optimization. When fitting a model you actually want to minimise the costs, (the model error). See also:

• https://towardsdatascience.com/cost-functions-the-underpinnings-of-machine-learning-549ac5edb211

Gradient descent is a powerful method for above mentioned optimization when fitting your model. It is really well explained by Andrew Ng in this video:

• Gradient Descent (C1W2L04) - YouTube

Also this thread might be interesting for you:

• How different initialization of centroids of K-means results in drastic different clusters ? They all share common cost function - #5 by Christian_Simonis

Best
Christian