Learning Progress: Creating Visualizations With Pandas and Matplotlib

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TIL: Pandas - Read CSV With Custom Separator Using Regex

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.
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A Walkthrough of the “Complete Machine Learning and Data Science Zero to Mastery” Course (Part 04)

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.
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Friday Picks 038

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Tool: jupyterlab-vim

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.

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TIL: JupyterLab: Run All Cells

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A Walkthrough of the “Complete Machine Learning and Data Science Zero to Mastery” Course (Part 03)

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.
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A Walkthrough of the “Complete Machine Learning and Data Science Zero to Mastery” Course (Part 02)

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.
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Friday Picks 036

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A Walkthrough of the “Complete Machine Learning and Data Science: Zero to Mastery” Course (Part 01)

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.
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