There are literally countless resources available to learn R -- blogs, online tutorials, Massive Open Online Courses (MOOCs), platforms, and books. For somebody new to the field of Data Science, it is thus hard to identify really great resources. While everybody has a different way of acquiring new knowledge, I believe that the following books are particularly useful, especially for beginners. This book is co-authored by Hadley Wickham who is best known for the hugely popular R package ggplot2. This book provides a well-written overview of how to import, tidy, transform and model data in R. The book is also available free of charge as an online version. It includes many exercises after each section which makes the book particularly interesting for beginners.

Many top universities make some of their courses available for free to non-students, a trend which has been gradually increasing over the years. While perhaps not the first example of such an offering, we can thank Andrew Ng (among others, certainly) for making his Stanford Machine Learning course available beyond the classroom, first via third party means, and then as one of the first courses on the MOOC platform Coursera. Since then, courses offered both via such a platform as well as those with publicly-accessible course websites have rapidly increased in number. There are no shortages of quality, free university level courses these days & mdash especially in computer science, data science, machine learning, and other tech disciplines. Right off the bat, note that when we say "free" we mean that much of a course's learning material has been made available to the masses without cost.

Online education has surpassed the conventional classroom paradigm as the new standard. The need for learning AI mobility solutions has increased as a result of COVID. For the intellectual ecosystem, it opens up a world of possibilities. Teachers increasingly use sophisticated applications and linked gadgets to teach their students. They can do this because of the combination of live video, animations, live chat, and other features.

For the code explained in each lecture, you can find a GitHub link in the resources section.

Kids can make wonders; many surveys and stats prove that the age from 5-15 is the best time for anyone in their entire lifetime to store as much data into their brain as possible. But the very idea of introducing them to such complex topics like AI and ML can be extremely terrifying. But worry not, we are here to help you take your kids into the world of ML. Machine learning is definitely not as easy as teaching rhymes but we can make it fun. "Rome wasn't built in a day" and hence, make sure you don't dig deep into the topics like robots or automation.

I finished my Business degree on 2014, I decided to study the A.I.. I started machine learning and deep learning in 2014. I got it myself in my school journal. Anyway, I was doing my best to study whatever it looks cool. Because it was only two years and it is not good enough to achieve the good knowledge.

It's a good day to learn something new, don't you think? Let's take a break from our ML / DL articles today and breathe together. Wouldn't it be nice to have a scholarship with many goodies available to you as a machine learning engineer or researcher-developer? I will be sharing with you how I went through the steps for this scholarship and my own roadmap. If you are looking for ways to achieve an AWS ML scholarship, let's chat together. This scholarship program, along with the adventure of being a core member of the TensorFlow Turkey community, has been a scholarship program that I learned through my dear teacher Yusuf Sarıgoz.

Hello everyone, we have updated this article recently, as the offer we shared here earlier has been updated and extended until the end of year 2021 while the supplies last. So, please take a note. Also, we hope you are staying safe from COVID19 and mastering new skills while staying at home. Here's a great news for our learners: The tech giant, IBM is offering Data Science and Artificial Intelligence Programs on Coursera for free for first 30 days until 31st of December 2021 (Updated). Consider looking at this exclusive offer here.

Education should not be a privilege but a common good. It should be openly accessible to everyone, with as few barriers as possible; even more so for key technologies such as Machine Learning (ML) and Data Science (DS). Open Educational Resources (OER) are a crucial factor for greater educational equity. In this paper, we describe the specific requirements for OER in ML and DS and argue that it is especially important for these fields to make source files publicly available, leading to Open Source Educational Resources (OSER). We present our view on the collaborative development of OSER, the challenges this poses, and first steps towards their solutions. We outline how OSER can be used for blended learning scenarios and share our experiences in university education. Finally, we discuss additional challenges such as credit assignment or granting certificates.

As we all know, Covid-19 struck Wuhan on New Year's Eve in 2019, and the city embraced total lockdown. Soon, the rest of China and the world followed. But what many don't know is Chinese education never went into lockdown. Within less than three months, Beijing Normal University (BNU) started a new semester offering more than 3,000 online courses. BNU could do so only because of China's comprehensive education technology (EdTech) drive in the preceding years, as a blog post at the Oxford Internet Institute elaborates.