“Learning to Learn [Efficient Learning]: Zero to Mastery” - Course Review and Notes

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.
Read more →

Learning Progress: Course “Learning to Learn [Efficient Learning]: Zero to Mastery”

Read more →

Learning Progress: Creating Visualizations With Pandas and Matplotlib

Read more →

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.
Read more →

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.
Read more →

Friday Picks 038

Read more →

Learning Progress: Starting Nand2tetris

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.
Read more →

TIL: How to Run Your Scripts From Everywhere

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.
Read more →

TIL: Docker Debugging and Exit Codes

Read more →

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.

Read more →