What is machine learning / ai ? How to lean machine learning in practice?
machine learning / ai (artificial intelligence) and neural networks (often referred to as deep learning) are one of the hottest topics in this century - for good reasons.
There are a lot of interested people out there but many do not know where to start. The difficult question basically is how to start actually learning it?
Especially beginners might get discouraged because of statistics and math which is an integral part of machine learning. Also matrix operations in tensorflow are not considered easy peasy. None the less you do not need to be a math expert to apply machine learning. This is my third course to show you why.
Instead of telling you all the statistics and math behind the neural network and deep learning i prefer to give you a much more hands on approach. At the end of the day there's only one thing that really counts - THE RESULT. I believe in a practical approach. That's why the course is developed to encourage you to follow along and write the code yourself. At the end you can see your result.
By joining this course you can leverage the knowledge you acquired from my first two courses (Machine Learning for Beginners and machine learning for beginners - deep dive) and get the chance to dive into theworld of neural networks. Again this course is not for students who like to learn theory. Those should rather turn to a university professor or wikipedia.
But if you want to actually practise machine learning and neural networks with python and tensorflow and learn how to write and improve your own algorithms then this beginner's course is the right way to continue your learning journey!
I wish you all the best, enjoy the course, get your hands dirty and start coding! Let's master neural networks from scratch
See you in the first lecture
In case you want to dive deeper into the theoretical understanding I refer you to my two other courses.
These should give you an easy way to understand the different kinds of deep learning nets in less than 2 hours.
Define the structure of our model
The next step. Model compilation
Our model needs training
Cards on the table. What's our model's performance?
Let's make a prediction
Combine skicit learn and keras to get the best out of both
Optimize your neural network to deliver better results
What are the best Parameters? Take a look
Let's do a regression with our neural network
Show me the result!
At first we visualize our dataset. What are we dealing with?
We need to preprocess our data in order to feed our neural network
Captions says it all. Let's get into it!
Let's watch the output of our CNN model
Alpha and Omega. Let's sum up what you have accomplished
Daniel is a 28 year old entrepreneur ,data scientist and web analyst consultant. He holds a master degree as well as other major certificates from Google and others.
He is committed to support other people by offering them educational services to help them accomplishing their goals and becomming the best in their profession.
"In order to do the impossible you need to see the invisible"