Reinforcement Learning Web App

Deploy a stock trading robot from scratch using reinforcement learning.
Rating: 4.5 out of 5 (18 ratings)
3,739 students
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How to create real hands on Reinforcement Learning Solution and embedd it in an Webb App
How to create your own Python library and abstract all of the computation away (Automated Machine Learning)

Requirements

  • Python
  • Machine Learning
  • Reinforcement Learning

Description

Please note that this is a FREE course, so the course does not cover certain topics extensivly because there is a 2 hour limit. In order for me to keep it FREE I needed to make some compromises on the depth of the content. I tried not to talk about stuff that you can find on internet for free.




Reinforcement Learning is currently highly experimental and not a lot business cases are beeing solved outside of big tech companies. Here I will show you how you can solve real stochastic problems using Reinforcement Learning. And not only code the solution from scratch but also deploy it via Web App. In the process of, we will also learn about packing our code and publishing Python libraries. One of the examples were we showcase this will be automated machine learning, where dull processes like preprocessing, hyperparameter optimisation, algorithm selection etc. will be all abstracted away and executible with a single command.

Who this course is for:

  • Everyone with enough curiosity

Course content

3 sections7 lectures1h 34m total length
  • Introduction
    06:56

Instructor

Machine Learning Engineer
Noah Weber
  • 4.0 Instructor Rating
  • 59 Reviews
  • 8,462 Students
  • 3 Courses

My name is Noah. I'm a machine learning engineer from Austria. Trained as a mathematican I've been writing code for 6 years, and for the past three years, I've focused on writing machine learning applications. I've done this at banks and fintech companies, where I've worked on and grown production machine learning applications used by hundreds of thousands of people. I've built and maintained machine learning systems which make credit-risk and fraud detection judgements on over a billion dollars per year.

I'm also passionate about helping others tackle their own problems in machine learning area. You can find me answering questions on stackexhange, or competing on kaggle.