Requirements
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Your personal interest in the topic and a hands on mentality
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Basic knowledge in Python
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Tools are free – no additional costs required
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This course is hands on – instead of theory we implement neural networks in code and I explain what we do and why we do it
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You should be familiar with neural networks – I do not start with explaining what a neural network is
Description
Let’s dive into data science with python and learn how to build recommender systems and autoencoders in keras
machine learning / ai ? How to learn machine learning in python? What are autoencoders? How to build a neural network recommender system with keras in python?
Good questions here is a point to start searching for answers
In the world of today and especially tomorrow machine learning and artificial intelligence will be the driving force of the economy. Data science No matter who you are, an entrepreneur or an employee, and in which industry you are working in, machine learning (especially deep learning neural networks) will be on your agenda.
“From my personal experience I can tell you that companies will actively searching for you if you aquire some skills in the data science field. You do not need to know everything! Some basics can already open up a lot of doors! So diving into this topic can not only immensly improve your career opportunities but also your job satisfaction!”
It’s time to get your hands dirty and dive into one of the hottest topics on this planet.
To me the best way to get exposure is to do it “Hands on”. And that’s exactly what we do. Together we will go through the whole process of data import, a little bit of data preprocessing (if necessary) , creating a neural network in keras as well as training the neural network and test it (= make predictions) / make recommendations!
The course consists of 2 parts. In the first part we will create an autoencoder neural network to learn how data compression with neural networks work. In the second part we create a neural network recommender sytem, make predictions and user recommendations.
Let’s get into it. See you in the first lecture
Who this course is for:
- It’s a hands on course so Your committment to code along with me
- beginners to intermediate students in neural networks and machine learning who already know the basics
- students who ask how to build an autoencoder in keras
- students who ask how to build a recommender system in keras
- students who are eager to learn and dive into one of the hottest topics currently out there
