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 sixth part of the blog post series. part 1 part 2 part 3 part 4 part 5 part 7 part 8 10. Milestone Project 1 In this project we work through a dataset from start to finish. We use supervised machine learning to gain insight into a classification problem.
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 fifth part of the blog post series. part 1 part 2 part 3 part 4 part 6 part 7 part 8 9. Scikit-Learn Up until now, we’ve learned how to consume data and make fancy diagrams. The current section finally deals with Machine Learning and teaches you the basics of Scikit-learn.
This blog post shows my notes from the PDF “Learning to Learn Effectively“ by Jovica Ilic. The PDF is a bonus for the Mastering Vim Quickly product. The document clocks in at 19 pages and is a quick read with some learning tips. I only recorded the essential key takeaways: Mindset strive for change Motivation mini-habits measure everything track progress visually Learning Principles learning is learnable take care of yourself (hydrate, breaks, concentration spans, sleep) teach someone else change your mindset (expect to replay it back) expect to teach it (or write about it in a blog post without checking out the original learning resource) identify gaps in your knowledge implement right away (take action): “Which three things can I learn and put into action right away?
In this blog post, I’ll post my notes and thoughts about the course “Learning to Learn [Efficient Learning]: Zero to Mastery.” (Udemy Link). 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 part 5 part 6 part 7 part 8 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.
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.