If you want to convert a CSV file into Pandas, you can use [pandas.read_csv][readcsv]. The function takes several options. One of them is sep (default value is ,). You can use a regular expression to customize the delimiter. Let's say your data looks like this: vhigh,high,2,2,more,small med,vhigh,3,more,big … You want to load that data into a Pandas DataFrame. You can split each line on the comma, but you want to ignore the comma inside floating point numbers like 2.

I'm going through the Udemy course Complete Machine Learning and Data Science: Zero to Mastery and writing down my observations/lecture notes. This is the fourth part of the blog post series. part 1 part 2 part 3 7. NumPy The section covers an introduction into NumPy. NumPy will covert any data into a series of numbers. NumPy is the backbone of all data-science in Python. Pandas and other machine-learning libraries are built on top of NumPy.

If you want to make JupyterLab more Vim-like, you can use Vim key bindings and install the jupyterlab-vim extension.

1. Vim Key Bindings The notebook UI has the option to use Vim, Emacs, or the default key mappings. The documentation shows how. In my Docker image I create a json file to hard code these settings: File jupyter-codemirror-settings.json: { "keyMap": "vim" } Dockerfile:

previous setup - base image, working directory, etc.

I'm going through the Udemy course Complete Machine Learning and Data Science: Zero to Mastery and writing down my observations/lecture notes. This is the third part of the blog post series. part 1 part 2 4. The 2 Paths The class aims to be beginner-friendly. Now you have the choice to learn how to program in Python or to continue with the default route. The program contains more than 8 hours of video lectures on Python, which I'll skip.

I'm going through the Udemy course Complete Machine Learning and Data Science: Zero to Mastery and writing down my observations/lecture notes. This is the second part of the blog post series. Go to part 1 here. 3. Machine Learning and Data Science Framework The course focusses on learning by doing. Instead of learning higher mathematics and over-thinking the process, the instructors show you a framework that encourages a fast feedback loop.

I'm going through the Udemy course Complete Machine Learning and Data Science: Zero to Mastery. The course runs under the flag of Andrei Neagoie. Andrei is a popular instructor on Udemy, with almost 200.000 students, and top reviews. For this course, he has paired up with Daniel Bourke, a self-taught Machine Learning Engineer from Australia. In this blog post series, I will jot down my thoughts on the course, and what I've learned.