Hello and welcome to Coursera's specialization in Machine Learning in Finance.
My name is Igor Halperin,
and I'm a research professor at NYU Tandon School of Engineering.
I will be your instructor for this specialization.
I will say a bit more about myself in a bit,
but first I would like to talk about what you will get out of this specialization.
First about this specialization itself.
Put simply this is a specialization in machine learning that
focuses exclusively on implications of machine learning confinements.
This is different from other courses that typically use
a wide range of industrial applications to teach machine learning.
The second very important point about this specialization is about its intended audience.
I assume that you are either a financial professional,
or plan to become a financial professional in the near future.
Therefore, this specialization is designed for practitioners,
or future practitioners who either work or plan to work in finance,
and want to learn machine learning from scratch,
but using problems from finance to develop your skills.
And yet finance is all about numbers and predictions.
So, why don't we use financial data from the start to learn
machine learning rather than use examples that have little to do with financial data.
The third remark I would like to make is about
examples and assignments in this specialization.
Because this is specialization for practitioners,
I did my best to avoid
meaningless exercises that have nothing to do with their real life.
For example, we will not be doing such things as predictions of stock prices,
or predictions of stock indices for this sake.
The reason is that even though you may have come across many blogs
or research papers that discuss applications of machine learning to such problems,
they have no practical answers.
Therefore in order to maximize its usefulness for you,
we will only be looking in this specialization at practically useful examples.
So, what will be your takeaways from this specialization?
First, you will get a good understanding of both core concepts,
and most important algorithms of machine learning from scratch.
Second, you will learn all this not in the abstract but rather in a very hands-on way.
We will put our hands on a lot of different kinds of financial data and
use multiple machine learning algorithms to play with the same data.
You will learn not only about classical machine learning algorithms but also
get solid understanding and practical experience of working with neural networks;
including, in particular,
the so-called deep neural networks that have recently took the industry by storm.
As a result, you will develop a good intuition about both financial data,
and about how different algorithms perform on these data.
You will know how to apply Python machine learning packages such as scikit-learn or
TensorFlow to build machine learning models for applications in quantitative trading,
asset management, banking, and other fields of finance.
You will be able to impress your potential employers with a portfolio of
interesting and relevant projects on
applications of machine learning to problems of practical interest.
And more importantly, you will be able to use your knowledge to build
new machine learning models using the libraries that you learned,
if your business need them.
And this last thing,
I mentioned brings me to the last but
not least part of your takeaway from this specialization.
Machine learning is an extremely active field.
Methods that are taught at schools as a cutting edge today will become outdated tomorrow.
Therefore, it's important that industrial practitioners in
machine learning should be able to understand as practitioners,
papers in machine learning,
and we'll be able to implement them.
And the best way to learn it is to read machine learning papers.
Therefore, our programming assignments will often have references to
original publications that can be used to dig deeper into the topics we will be covering.
In addition, I will be providing references to original publications,
and weekly regionalists which should both help
you to get a broader view of the topics to be covered,
and develop some practice in editing machine learning papers.
So, this will be
your three main takeaways after
completing this specialization in machine learning in finance.
In the next video, we'll talk more about the structure of this specialization.
Igor Halperin
