What does the course cover
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The focus of this course is deep learning and deep neural networks in particular it is of utmost importance
to us to provide you with an in-depth preparation.
We don’t want to scratch the surface either.
We want to enable you to see the full picture of how things are done.
In this course we will cover very high and trending topics already shaping the future and we are super
excited about that.
I hope you are too because this will be an extraordinary adventure allowing you to learn cutting edge
technologies and methodologies.
First we all start with the very basics.
We will learn about the different types of machine learning and the building blocks of a machine learning
algorithm data model objective function and optimization algorithm.
We will build a solid foundation which will help us create our first machine learning algorithm.
And that’s just in the first hour.
Once we have the basics we will go deeper.
Bit by bit we will unfold the power of deep neural networks.
We will explore layers how to stack them and how to activate them.
We will take extra time to clarify the back propagation process which will be explained mathematically
graphically and through real life examples.
Then we will continue by learning about underfeeding and overfitting using the power of animations.
You’ll grasp the central concepts quickly.
We will talk about training validation and FULDE cross-validation testing and early stopping even if
these concepts mean nothing to you.
Now that won’t be the case for long.
Another important topic we’ll discuss is initialization.
We will show you how and why it is done and will even use the original source of academic research to
back up our explanations.
The final theoretical block of our course will be about optimizers.
These are optimization techniques such as the stochastic gradient descent batching momentum and learning
You will not only be able to create a machine learning algorithm but a fast one.
We have included pre-processing mechanics as this is another important part of machine learning.
We’ve prepared topics like standardization normalization and one high encoding.
All these concepts will ultimately lead us to our big machine learning example based on the M.A.
The list is truly the best place to see how much you’ve learned along the way.
OK what happens next.
Well then it would be up to you to create your first algorithm.
We have prepared a real life business case with real people real data and real insights.
We will give you the guidelines but you’ll quickly realize that all the hard work you put into this
course will ultimately pay off.
You will experience first hand the unmatched satisfaction of taking big amounts of data and finding
complex relationships hidden from traditional statistics methods.
Sounds great doesn’t it.
This will be an incredible adventure to get the most out of this course.
Please don’t skip any lectures as we will gradually build on your knowledge and the concepts you will
learn in the first part of the course will be useful later on.
The lessons in the Course contains several downloadable resources that will help you reinforce what
you have learned.
Of course notes exercise files PBF materials handy notebook files everything is included and can be
I would strongly suggest you complete all the exercises as they are designed not only for practice but
also as an additional source of information that deepens your understanding of machine learning post
in the course discussion board when you experience difficulties and would like to ask a question or
simply want to share something with the community this course is extraordinary on its own.
But on top of that if you complete it you will also get free access to two of our other courses.
Microsoft Excel 2016 beginner and intermediate training provided you complete 50 percent of the course
and statistics for data science and business analysis provided you complete 100 percent of the course.
Are you excited.
I assume dive straight in and let’s begin this journey together.
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