Programming is intimidating, but it does not have to be. If you know where to start and what concepts to learn first, you can be a programmer on an enjoyable path. This guide is open to contributions. If you have any resource recommendations, send a pull request.
It's always good to start with the fundamentals.
The best introduction course to computer science offered by Harvard. Online lectures are available.
Start your CS journey with Google's Tech Dev Guide curated by Google engineers and university faculty.
Having a version control system makes your life easier. Github's offical tutorial is a good place to start.
You are not alone. Watch the story of one of our friends' presentations. (Turkish)
If you need to make mathematical computations, you can start with
The most used language in universities. But it's not free, hope your university has a license. This Coursera course is not bad.
Python is the best language to start with for programming since it has an easy syntax. It's free and scalable. There is an Introduction to Programming book. Arindam Dawn's 30 Days of Python Series helps you to learn Python in one month.
Web programming is where most programmers end up with
Learning this couple is like learning the alphabets of web programming. Codeacademy's interactive HTML & CSS courses are fun for beginners.
If you learn JS and love it, you become unstoppable. Instead of millions of free JS courses, start with a cheap but qualifed Udemy course. Once you know the basics of JS, Just Javascript helps you build the mental model of how JavaScript works. This guide perfectly summarizes how to write Clean Code
I don't have a stand-alone NodeJS course because most of them are included in JS courses. Search on Google to be a full-stack JS developer.
ReactJS is one of the most trending frameworks. The official site has very good documentation and tutorials. Best learning resource for reading type people.
The best way to learn a CSS framework is to use it in your own projects. Find the best element for your requirements via its website.
Creating 3D views in a browser makes you amazing. If you need such a visualization, it has a minimalist docs.
Whether you want to be a machine learning engineer or not, you should have some fundamental knowledge.
Andrew NG is the introduction teacher for ML. This course is really for everyone.
A brief introduction to the ML course organized by the Global AI Hub community. Free access to videos and lecture slides are available. (Turkish)
You can learn about the prerequisite mathematics for applications in data science and machine learning offered by Imperial College London. Mathematics for Machine Learning Specialization.
The most beneficial course on Coursera is Machine Learning Course. Most probably better than your university ML lectures.
Andrew Ng's "Deep Learning: Zero to Hero" course series is highly suggested under the Deep Learning Specialization Program.
CS231n is one of the fundamental courses of computer vision at Stanford University. I suggest the Winter 2016 semester lectures in which NN parts were given by Andrej Karpathy. Lecture notes and assignments are available here.