In this blog post, I’ll post my notes and thoughts about the course “Learning to Learn [Efficient Learning]: Zero to Mastery.” The course has four main sections: Principles, Pillars, Lies, and Techniques. The Principles The section establishes some useful principles for motivated self-learners. Learning vs. Winning The System shift from trying to win the game (get good grades, get a promotion) to a mindset of long-term learning (see “The Lesson to Unlearn” by Paul Graham define your measure of success you need drive and persistence embrace the obstacle: you’ll be bad first until you get good know when to quit, make a smart choice (not everyone can become an Olympic athlete) compound learning: learn in chunks instead of cramming improve by 1% every day failures don’t count against you: people know you right now, they don’t care about the test you failed five years ago use failure as a feedback loop mindset: choice vs.

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.

I like to learn with project-based courses. The practical approach works well for me. It helps me stay motivated. I also like to work on something tangible. That's why I'm glad that I found the nand2tetris course. The course has a website that contains all the lectures, slides, and exercises. You can buy a book for the course, or you can attend a Coursera course (audit for free). nand2tetris helps you to build a general-purpose computer system from the ground up.

I've been writing a few bash scripts and some Nim command line utilities. You can run a script from the folder which contains the script. Here's an example file structure: ~/bin/ ├── git-reset-author.sh └── readme_template When I'm inside the ~/bin directory, I can type into the terminal: readme_template. But what if I want to navigate to a different folder on my machine and run the script from that location? fish: unknown command readme_template The shell doesn't find the program.

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.