Hi can someone help me understand optional labs and how to write the cost function, the gradient decent as well as the iterations in code. I need to understand them before i can pass my week 2 assessment.
The information you need is in the assignment notebooks, and in the lectures.
The assignment is quite a bit simpler than the optional labs.
The optional labs are intended to amaze and allow for exploration. They’re not intended as a how-to example.
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Yes but I am having troubles understanding how to apply the code. I am unable to do the final assessment for week 2 because of it.
What trouble are you having specifically?
Without posting your code, can you describe what part of the assignment you are asking about?
I need help with Model Representation.
I am looking in Week 2 of the MLS “Advanced Learning Algorithms” course, and I do not se where “Model Representation” is mentioned.
Can you post a link or some more details so I can find it?
Sorry i meant to say Optional lab: Model representation in week 1. https://www.coursera.org/learn/machine-learning/ungradedLab/PhN1X/optional-lab-model-representation I don’t understand the entirety of optional lab- it doesn’t make sense to me.
Not only do I not understand the Model representation in week 1 but I also don’t understand the Optional lab: Cost function as well.
I’ll change the tag in the thread title to Week 1 instead of Week 2.
I really appreciate your help. Thank you.
That lab isn’t in the “Advanced Learning Algorithms” course.
It’s in the “Supervised Learning: Regression and Classification” course.
No wonder I wasn’t able to find it.
I’ve moved the thread to the correct forum area.
What specifically do you need help with?
The lab assumes that you are familiar with simple Python programming.
The workflow in the lab is that you start from the top, read the cells that have descriptions and instructions, and then Run the code in each executable cell.
You can Run the code in a cell by clicking in it and pressing <shift-Enter>, or by clicking on the Run button in the top menu.
Note that you have to run every cell starting from the top of the notebook, every time you open it.
The optional labs expand on the discussions that were in the preceding video lectures.
I do not know Python. Is there a way I can catch up quickly, maybe a short course on Coursera or a YouTube video.
Yes, there are lots of free Python tutorials available online.
Here is one:
It is very complete, so for starters I recommend you just cover the first five sections, plus section 6.1.
If you want a video tutorial, there are lots on YouTube. You can gauge the qualify of the tutorial by the number of views.
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Man, I cannot thank you enough 
I will jump right in. Thank you very much. 
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Keep in mind that to use the courses use Jupyter notebooks, so you don’t need to install Python or an IDE on your computer.
It would be handy if you have access to a Jupyter Notebook server for following along with the tutorial examples.
Google Colab is one such source. They use Jupyter notebooks, and you enter the tutorial examples into a notebook code cell.
Colab may also have their own tutorials, that would be worth checking.
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Understood.