This workshop is designed for those who want to learn how to use R to analyze data. The material is based on Hadley Wickham and Garrett Grolemund’s R for Data Science. We’ll talk about how to conduct a complete data analysis from data import to final reporting in R using a suite of packages known as the tidyverse. The two goals of this workshop are: 1) learn how to use R to answer questions about our data; and 2) write code that is human readable and reproducible. We will also talk about how to share our code and analyses with others.
You should take this workshop if you are new to R, or to the tidyverse, and want to learn how to take advantage of this ecosystem to do data analysis. You’ll get the most from the workshop if you are primarily interested in applying pre-existing R packages and functions to your own data. We will give minimal tutorials on how to write your own functions; however, the main focus will be on using existing tools, rather than building our own.
Now that I’m slowly familiarizing myself in the world of Python, I am much more often confronted with classes and object-oriented programming (OOP). While R has its own OOP paradigms (yes, multiple, obviously, it’s R after all), I have never experienced the need to create my own classes. However, in other languages, like Python, Ruby, or Java, OOP is much more an essential of developers’ and programmers’ skillsets.
During this course you will learn all the basics of computer programming, algorithms and object-oriented programming using the Java programming language. The course includes comprehensive course materials and plenty of programming exercises, each tested using our automatic testing service Test My Code.
Part 1 of the course will teach you all the basics of the Java language:
Part 2 continues with some more advanced topics:
While I have not taken the course myself yet, I have read a lot of good reviews about it. Moreover, what better way to learn a new language than by deep diving into it with a specialized topic like OOP. And it’s free! And taught by trained academics! What are you still doing here, start learning!
This overview is curated in the sense that all resources are rated by CourseDuck’s users. These ratings seem quite reliable, at least, I personally enjoyed their top-3 resources sometime the past years:
This specific link has been on my to-do list for so-long now that I’ve decided to just share it without any further ado.
The Deep Learning textbook helps students and practitioners enter the field of machine learning in general and deep learning in particular. Its online version is available online for free whereas a hardcover copy can be ordered here on Amazon. You can click on the topics below to be redirected to the book chapter:
Massachusetts Institute of Technology (MIT) hosts their entire 2010 course on artificial intelligence / machine learning by Professor Patrick Winston on YouTube. Although some parts seem already kind of dated seven years later, the videos on several evolving topics (e.g., Neural Networks) have been updated in the fall of 2015. The tutorial assignments you can find at the course website. Requirements for the course include experience with Python programming and an understanding of search algorithms (depth-first, breadth-first, uniform-cost, A*), basic probability, state estimation, the chain rule, partial derivatives, and dot products.
Below is the first, introductory lecture, which provides a short introduction to the history and concept of artificial intelligence: AI is about algorithms enabled by constraints exposed by representations that support models targeted at loops that tie together thinking, perception and action.