Hello, and a warm welcome to the course. When it comes to machine learning, you might ask yourself, who is learning actually, the machine or you? So in this course, you definitely learn how to distinguish from machine learning and human learning. Of course, you will know the difference between data mining, analytics, predictive analytics, machine learning, supervised machine learning, unsupervised machine learning, deep learning, and thinking processing. We are analyzing our data on hyper-dimensional vector spaces. So, that's a very important notion on how to view data. Don't be scared here for example this three-dimensional cube. It looks like a star, but just try it. Just look a bit at this star, and eventually you will see that it is a cube. So, I think understanding to view data in hyper-dimensional vector spaces will change your life. Therefore, I would emphasize on the topic and you will become a multi-dimensional thinker. There is notion of pipelines. In machine learning, you have to do a lot of data preprocessing, and sometimes you even don't know how to preprocess your data. So, pipelines are really helping you there. So basically, you get some sort of a list of all necessary transformation steps and you just apply them in sequence. So there is a very famous machine learning framework out there which is called scikit-learn, and that makes heavily use of pipelines. So, all you do is you concatenate sequential steps until you get to the machine learning algorithm, and you can switch and swap the algorithm without problems. But scikit-learn has a big catch. If you exceed the amount of main memory you have on the system, you will crash. So, you will get the memory error. Therefore, they exist Apache Spark. Apache Spark is a parallel data framework which pushes the compute two cluster nodes. It's linearly scalable, that means the more cluster nodes you add to the cluster, the more it scales. For example, if you doubled the cluster nodes, you nearly get double the performance. Luckily in Apache Spark, there are also exists a framework for machine learning pipelines which is called Spark ML. In this course, we will concentrate on Spark ML, but we will make sure that you also get a good grasp of scikit-learn. Finally, we will also introduce a very cool machine learning library which is called SystemML, which is an optimizer for linear algebra, some sort of SQL engine for linear algebra. So, make sure you will also watch those videos. So in summary, in this course, you will learn everything about applied machine learning and signal processing, which algorithms exists how they work and also how you can scale those on peak clusters. We will use real life data and real life examples that you can directly apply what you have learned in code. So, you will watch me coding and you will code yourself. In order to get a certificate, you have to code yourself. But don't worry, a little bit of Python and a little bit of course materials you will attend here in the course is sufficient to pass. So, this would be a very nice experience and I hope I will see you in the course.