Now let me say a few words about the pre-requisites to this facilitation. First let me start with the programming part. I assume that you know at least some Python and have used or at least have seen Jupyter notebooks. If you're not familiar with Jupyter notebooks, take a look at tutorial reference for you in this week's reading please. I also assume that you're familiar with other Python libraries such as NumPy and Pandas. And prior knowledge of TensorFlow is not assumed, as TensorFlow will be gradually introduced within the course. Now, let's talk about prerequisites for the math part, here's what I expect you to know. First, machine learning uses lots of linear algebra. So I expect you to be familiar with linear matrix equations in those matrices and other related concepts. I also assume that you know basic probability theory, for example, you're familiar with the Gaussian, exponential or binomial distributions. Basically, the tools such as the base line mark and you also know some basic statistics. Finally on the math side, I assume that you know basic calculus including roots of differentiation of composite functions. So that formulas like this, or like this, or even like that would not perplex you. If they do, please refresh your knowledge of calculus. As one of my heroes in science used to say, the math should not stand between you and the problem you want to solve. Just the opposite it should be able to help you once you know how to use it. By the way the question of how much of math you need to know to do machine learning, because it's a popular topic on data discussion boards. Recently I came across a very lovely post written by a currently working in the machine learning space. I strongly recommend you read this post, and here's a link for your convenience. And on my side I can confirm that everything this guy says is exactly how physicists approach problems on the mathematical side. If I had to condense this into just a few sentences, I would put it as follows. When you come across a new machine learning model, be it in our lectures, books or original papers, start with an abstract or whatever replaces the abstract. If the statement about what the model does attracts your interest, so that you want to learn more. Skim through the main equations of the paper and make sure you understand what they mean, not yet how they obtained. Use some sort of a meditation on main formulas like [INAUDIBLE] quantity stands in the left hand side of the equation and what forms appear on the right hand side. How do they enter for example exponentially or logarifically? Than assume that all equations are right, and that an implementation available for you is right as well, and proceed directly to plan hands on with the model. You may want to first feed your model with the date that you understand, for example with pure data or even constant data to see if it has this some sort of same rejects. If it does, then feed the model your actual data you want to explorer. And when you get the results, chances are that you will like them or dislike them but most likely you will notice some behaviors, some particular behavior and you will have questions about that. And it's only then, then you can return to the main section and read the math. The long story short, if you start with the math, just move on and come back to it later if needed. That would be my practical advice for you both for this course and beyond, unless of course you have an unlimited time batch. But if you don't, which is the most often the case in the real life, the approach that I just described can save you lots of time. Finally, let's talk about prerequisites on finance. This will be really short because in fact I don't assume that you have any specific knowledge in finance. And all financial concepts or problems discussed in this will be popularly explained for non-specialists. Okay, so I covered most of what I wanted to say here as a way of a general introduction to this specialization. And now let me conclude with the list of recommended literature. There is a number of excellent textbooks on machine learning, but there are no textbooks specifically on machine learning in finance. So what I did for this course, is combing multiple sources including in particular parts of books by Bishop, Murphy, Goodfellow, and also a very recent book by Geron. Few other books that I like a lot, the books by Marsland and another book by Garrisonfield. And in addition to text books, I have used original publications. My own research, industry papers, blogs, Wikipedia, postal discussion forms and so on. In short, any source of digital information that I found useful for the purpose of creation of this course. As a rule, I always refer to original source whenever I base any substantial part of a lecture on any single source, so that you can always look it up for more details. Also in such cases I usually keep the notation of the original publication or adjust it only a little bit to one that's more common conventionals. So if you need, you can always come back and pick it up from where I left at in the lecture. Okay so I think this final, all I have to say in this introductory part to the specialization and in the next video, we will start our first course. I hope you will find it helpful and interesting. But if you feel that I move too slow, or too fast, or too deep, or too shallow, do not cover some topics or the opposite state or walk to some other non-interesting topics, and so on. Please share your thoughts on the course forum. Good luck with the course and remember, machine learning starts here, see you soon.