Hi, I'm Chris Brooks. I'm faculty here at
the School of Information at the University of Michigan.
I teach in our data science courses
here and I use Python heavily,
and I work daily in Python and use
this development environment
called the Jupyter Notebooks.
I'm pleased that I'll be able to
share that with you through this course.
The Jupyter Notebooks is a great piece of
technology that allows you to use the web
essentially to write real-world
Python programs that leverage
really impressive APIs
including optical character recognition,
and detecting faces, and so forth.
In my work here, I run a research lab called
the educational technology collective or etc.
We do research in
educational technology and learning analytics.
I'm really interested in how students,
like yourself, interact with technology,
like the Coursera platform, including the contents,
so videos like these,
and your peers on the discussion forums.
So my research is really focused on
building things like predictive models of
student success and studying how you interact in
these different avenues to exceed learning expectations.
In this course, we're going to be more project based,
and it's a little different
focused in the previous courses.
So in the first four courses in this specialization,
you learn the fundamentals of Python.
Now we want you to practice
those fundamentals to try and solve a project.
We're going to introduce to you new libraries.
Now, the goal here is not actually to
learn those libraries in detail,
but to learn enough about those libraries
and moreover to have this meta-learning,
to learn how to approach
a new library so that you can start
using your skills in the while
to solve the project so you might be interested in.
Those libraries are going to include
image recognition libraries, so pillow,
and image manipulation, Tesseract,
which is an optical character recognition library.
So how we take pictures of books and
take the texts out of them
and turn into something we can process.
Cracking, which is a layout library for tax,
and you'll get a sense for
the challenges that come when dealing
with taking images and
trying to recognize characters in them.
Then the last one we'll introduce is called Open CV,
or CV stands for Computer Vision.
It's used for a lot of things,
but we're going to focus on using it to
detect faces and pictures,
and your project will be about that.
So one last thing to note along with some of
the other faculty here at the School of Information,
I teach a data science specialization on Coursera.
We use Python heavily in that and the Jupyter Notebooks,
and we think that after you finish the course like
this and come all the way through it,
you're more than ready to take that specialization.
So if you want to continue your learning on
the platform and continue your learning with us,
please join us in that specialization.
Let's just dig in to the class now.