Coursera Download Jupyter Notebooks
Sumiko Fagnoni
I am trying to submit an assignment in Coursera related to machine learning, but when I am unable to save the notebook in the embedded jupyter notebook and seeing forbidden, don't know what's wrong, I tried to raise the complaint to coursera support but haven't heard back from them, its been 3days and i don't want to loose the traction of it. please help me resolve the issue
As mentioned in the coursera help articles in order to download notebooks from the class we need to zip all the content of root folder into single file and download the final workspace.tar.gz using these steps: but it is not working all courses.
Anyone knows proper way to do this !!
I am auditing for free some courses in Andrew Ng's various ML specialization on Coursera which gives me access to all video lectures, but sessions in which you can run code examples in a jupyter notebook are locked, so I was wondering if there's like a github repo that collects these notebooks where I can have a peek at some code snippets mentioned in the video lectures?
BTW, if anyone's not aware, some of Deeplearning.AI team's advanced specializations have github repos like this that offers you all codes for free and are organized by courses and weeks, which is quite neat, and great for people who only need a subset of topics in the whole specialization. Unfortunately there's no official repos for the beginner level courses which are the ones I'm currently working on. So I'm wondering if anyone had collected these notebooks and are willing to share with free users, provided that there's no copyright issues of course.
Once you have finished the course, it would be interesting to still be able to read, run and experiment with the Notebooks from the course. After I passed the certification, I felt I had understood the material and knew when to apply it, but it felt I would need to go back from time to time, or just dig a bit deeper later on and play with it some more to really get it down. So running the notebooks locally, would save me a lot of trouble.
Coming from a more C#-world and having limited experience running these notebooks, I was looking for a way to do this. I initially tried downloading each file manually, which turned out not the be a good idea, so I started the browsing the Deep Learning Specialization forums and found a number of students having the same issues.
I have enrolled in some courses on Coursera that have assignments that need to be completed in a Jupyter notebook. Jupyter notebooks are awesome, but the Coursera-hub of those notebooks is very frustrating.
I am going through ML Specialization from Coursera and want to use DataLore to replace Coursera-embedded Jupyter notebooks. There is an image folder I can download from Coursera. I can upload it to the workspace (preferred) or notebook files in DataLore. So far, so good. But referencing these images in Markdown cells does nothing.
The specialization consists of three courses which you can take independently: Linear Algebra for Machine Learning and Data Science, Calculus for Machine Learning and Data Science, and Probability & Statistics for Machine Learning & Data Science. Each course consists of a number of bite-sized video lectures interspersed with some practice quizzes and programming exercises run in hosted Jupyter notebooks. The courses are expected to be completed over the course of some weeks, and each week has a final quiz and usually a programming lab. This format makes it perfect for casual learning, where you can sneak in a video in those seven minutes between meetings or so.
For creating a brand-new notebook, proceed to click New and locate Notebook - Python 3. If you have additional Jupyter Notebooks on your PC that you wish to utilize, click Upload and go to that file. Notebooks that are actively operating will have a green icon, whereas non-running notebooks will have a grey symbol.
Data analysis and exploration: Jupyter notebooks allow for iterative, interactive data exploration and analysis. Import data, clean it, and then analyze and visualize it with Python modules like Pandas, NumPy, and Matplotlib.
nbviewer: It is a web-based service for sharing Jupyter notebooks hosted on GitHub or other public repositories. It provides formatting options for the notebook, including LaTeX equations and interactive widgets.
In summary, Jupyter notebooks are an essential tool for data scientists. They provide an interactive, reproducible, and collaborative environment for data exploration, analysis, and communication. Learning Jupyter is an important step for anyone who wants to succeed in data science.
Jupyter Notebook documentation: The official documentation provides a comprehensive guide on how to use Jupyter Notebook for data science. You can access it here: jupyter-notebook.readthedocs.io/en/stable
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