In this blog, I aim to make machine learning, statistics, and R both easy and fun to learn from a beginner perspective.
Why did I start to write this blogs on machine learning, statistics, and R? It was because I have struggled with it.
Beginner struggle with learning
I understand the confusion and pain when you just started learning a new subject. There are piles and piles of books, online courses and youtube videos to choose from. The upside is that they are all good, and the downside is that they are all good. You, as a beginner, would have so much trouble trying to figure out a way to learn a new topic by yourself without any knowledge of it.
Three approaches to learn
There are basically three approaches to solve this problem:
First, start with any book, videos or online courses, learn as much as you could understand. After immersing yourself in this topic for long enough time, you would have your own structure of knowledge. This is how academics learn, and this is how most graduate students learn, I assume. This is a great approach and once you master it, you could learn anything with this approach. But! But it could take you a long time to learn. Let us call it random-walk approach.
Second, search online for the proper approach to learn a topic. I believe people begin to understand the importance to have a proper structure to learn a subject. Coursera began to have a specialization, datacamp began to have a career path, and codecademy began to have intensive pro lessons. But! You might not learn it to do what the courses taught it for. For example, you might spend a lot of time on learning the basic syntax of python, numpy, pandas, and sklearn for doing machine learning. But you actually just want to learn python to do some data manipulation and visualization. Yes, numpy and pandas covered the data manipulation part but they might not cover d3, seaborn and etc visualization tools. Let us call it expert approach.
Third, learn the bare minimum of a subject, and expand it to a direction wherever you see fit. My blog here is trying to take this approach to ‘teach’ you the bare minimum of a new subject, and draw a roadmap for you to pick up any direction you want. Let us call it minimum approach.
Why do I want to take this approach?
The minimum approach is actually a combination of random-walk approach and expert approach. You learn the bare minimum with the random-walk approach and expand your learning direction with expert-approach. This approach gives you the ability to learn what you want without the huge amount of time spent.
More about myself:
I mostly use R for my research and some side projects, because I am really familiar with it. I am also a big fan of python, but I use it much less often. I mainly use python when I need to try out some deep learning algorithms, some web scraping or some small fun projects.
Let me know if you are interested in anything relating to statistics, random process, machine learning, deep learning or anything else you have in mind.
Side note: Please excuse my bad fonts in some code or equations, I am learning to use blogdown , a new R package, to write my own blog. Stay tuned.