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.
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.
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.