Programming is intimidating but it does not to be. If you know where to start and what concepts to learn first, you can be programmer in an enjoyful path. This guide is open to contribution. If you have any resource recommandation send a pull request.
It's always good to start with fundamentals.
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 good to start.
You are not alone. Watch the story of one of our friends presentation. (Turkish)
If you are not sure which path should you follow? Web, ML or etc. You may take this survey and see results. (Turkish)
If you need make mathematical computations. You can start with.
The most used language in universities. But it's not free, hope your university have a license. This Coursera course is not bad.
Python is best to start for programming since it has easy syntax. It's free and scalable. There is an Introduction to Programming book.
Web programming is where most programmers are end up with.
Learning this couple is like alphabets of the 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 an cheap but qualifed Udemy course. Once you know the basics of JS, Just Javascript helps you to build the mental model of how JavaScript works.
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 framework. Offical site has very good documentation and tutorials. Best learning resource for reading type people.
The best way of learning a CSS framework is use it your own project. 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 a fundalmental knowledge.
Andrew NG is like introduction teacher of ML. This course is really for everyone.
A brief introduction to ML course organized by 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 courses of Coursera is Machine Learning Course. Most probably better than your university ML lectures.
Andrew Ng's deep learning zero to hero course series are highly suggested under the Deep Learning Specialization Program.
CS231n is one of fundamental courses of computer vision at Stanford University. I suggest Winter 2016 semester lectures in which NN parts were given by Andrej Karpathy. Lecture notes and assignments are available here.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent