Hey there. I'm Romeo. I'm your instructor of the course Apache Spark for Scalable Machine Learning on Big Data. That's the current title. So I'm changing the title, three times a day and that's the one I like best for now. So this course will be launched in three days, on Monday. So it's now Friday. Yes, it's in quite in a good shape. So, somewhere for me to do at the weekend, but I'm quite happy with it now. So this is a little introduction video about myself. So I'm sitting here in my office and you might wonder this looks like a car, actually is a car. So I'm employed at the Center for Open-Source Data & AI Technologies in San Francisco, but I'm living in Switzerland actually near Basel. So that's where Germany, France, and Switzerland meet. Therefore, I don't usually go through the Swiss IBM office which is around two hours drive from here. So I'm either traveling or I'm working from home or in the car. Yeah. We have four chief Latin Switzerland and I have power sources so that's actually not a problem and sometimes I go just into little coffee house and work from there. That's pretty cool. I also verbal up at night and in the evening because there is a nine-hour difference between my team in San Francisco and myself. So yeah, the Center for Open Source data & AI Technologies is the center which is founded and financed by IBM and it's purely dedicated for contributing and creating open-source software related to data and AI technologies. Of course, the most famous is Apache Spark that's the course is all about here. So IBM committed more than 65,000 lines of code to Apache Spark, a lot in the Data Frame and SQL and Machine Learning part. Apart from that, IBM also contributed to other projects and we have 17 active Apache Spark committers. This is pretty cool and you can see here we are contributing to Jupyter, Pandas, Scikit-learn, and we have also our own projects which we open-sourced, the famous one is AIF, AI Fairness 360 or it's AI model bias detection and mitigating tool or ART stands for AI Robustness Toolbox. So that's toolbox for protecting AI models against intra-cellular attacks. Recently, we launched AI Explainability Toolkit which allows you to open the black box models and look inside and actually understand how the predictions are generated. I don't have a slide on that, but let me try to find it. It's a pretty cool thing which happened yesterday. So IBM announced that our center basically, here it is. So our center is now part of the Linux Foundation and we contributed our toolkits and they are now in governance of the Linux Foundation that means it's not IBM who decides what goes into the toolkits, it's an open governance. But let's pretty cool. Yeah. I mean that that's all about myself. So I have a degree, a Master's of Science in Applied Statistics, Bioinformatics and Information Systems from the Swiss Federal Institute of Technology in Zurich. Previously, I did Bachelor's in Computer Networking so it's about distributed software development from University in Germany. What else do I have to say? Yeah, I really look forward to meet you in the course. So when you have questions, don't hesitate to ask those in the discussion forum. So this is a very applied course. So I hope that you will join the programming assignments. We're providing a free version of it in the IBM Cloud so you don't need a credit card and it doesn't expire. Sometimes the setup is a bit difficult or changes the UI changes and if you have any difficulties just don't hesitate to ask me in the forum. I will look into there on a daily basis but when I'm on vacation or when I'm traveling a lot, then maybe you have to wait for five or seven days. So I hope for your understanding here. Yeah. That's it. So again, I'm very happy to meet you in the course, in the discussion forums and I hope you are enjoying the course and let's get started. Shame on me, shame on me, I've shown you the wrong link. So this is the block I wanted to show you. So IBM joins the Linux Foundation AI to advance trustworthy AI. So you can read this article put a link down into the description of the video. So you see here, there's the AI Fairness 360 Toolkit, and Adversarial Robustness 360 Toolbox, AI Explainability 360 Toolkit. So I don't know what the difference between a toolkit and the toolboxes but anyway, those are the three main contributions we are making here to the Linux Foundation. So as I said it's open governance models so please check it out. Yeah. The other thing what I wanted to mention is so I have put a couple of videos and material in this course to other courses so I'm teaching the Advanced Data Science Specialization on Coursera as well. This is as the name implies, a bit more advanced, but here I have also some basic Spark and Machine Learning courses here, and also some thing about AI and deep learning but anyway. So I have pulled some videos so don't get afraid because I just pulled a more easy ones, and I have created new videos, and a lot of learning material, and also the grading is far more easy in this course so don't worry. But the good thing is, if you're really willing to dive more into the topic, you can definitely, then check out the advanced courses and this will all look very familiar to you then. Yeah. That's basically all. So let's get started and see you soon in the discussion forums.