Welcome to the course on constitutional neural networks where you will learn both the theoretical concepts
behind a CNN model as well as the implementation of deep learning models for image recognition.
My name is Ivy shake and along with my CO instructor grudge I will be leading you through this course
as instructors of courses have over two hundred fifty thousand enrolments worldwide.
We both call engineering and MBA degrees and have experience of data analytics consulting industry while
doing our jobs.
We realized that many data analysts and beginners in the field of machine learning feel a barrier in
learning CNN models and believe that the mathematics in all is overwhelming.
We have designed this course for such students.
We will cover everything up practicing data scientist needs to know from concepts to court without getting
too mathematical about it.
If you put one hour a day regularly within a week you will be able to make CNN based image recognition
models and answer CNN related interview questions.
We start this course by setting up python in your system and doing a crash course in Python.
If you are familiar with these languages you can even skip this part.
Then we understand and be a simple deep learning model with multi level pass approach which is used
for simple classification tasks with these foundations.
We then understand convolution and neural networks and see how they outperform simple neural network
models and imagine condition.
Lastly we build a complete end to end project where we classify colored images and achieve accuracy
is as high as 97 percent.
It has been proven that our curriculum provides solid inclusion to even those professionals who do not
have a strong mathematical background.
The ideal student for this course is a data analyst who wants to expand on the current skills of a student
who wants to have a career in data sciences and machine learning.
The prerequisite for learning and implementing convolution and neural networks are two.
The first is having an understanding of simple and models and secondly knowledge of the software tool.
We have tried to cover both of these and this goes so that you do not have to look for separate courses
for these.
If you have any query or draw toward the course you can post them in the discussion forum.
I will be personally solving all your doubts so feel free to have a look at the course description and
we look forward to seeing you insight.