Reinforcement Learning Tutorial | Reinforcement Learning Example Using Python | Edureka
** Python Data Science Training: https://www.edureka.co/python **
In this video on “Reinforcement Learning Tutorial” you will get an in-depth understanding about how reinforcement learning is used in the real world. I’ll be covering the following topics in this session:
(01:48) Introduction to Machine Learning
(05:51) What is Reinforcement Learning?
(06:55) Reinforcement Learning with an analogy
(07:54) Reinforcement Learning process
(08:57) Reinforcement Learning Counter-Strike example
(10:40) Reinforcement Learning Definitions
(13:01) Reinforcement Learning Concepts
(16:21) Markov’s Decision Process
(20:00) Understanding Q-Learning
Check out our Python Training Playlist: https://goo.gl/Na1p9G
How it Works?
1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work
2. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate!
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About the Course
Edureka’s Data Science Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Data Science Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR.
During our Python Certification Training, our instructors will help you to:
1. Master the basic and advanced concepts of Python
2. Gain insight into the ‘Roles’ played by a Machine Learning Engineer
3. Automate data analysis using python
4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application
5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn
6. Explain Time Series and it’s related concepts
7. Perform Text Mining and Sentimental analysis
8. Gain expertise to handle business in future, living the present
9. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience
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Why learn Python?
Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next “Big Thing” and a must for Professionals in the Data Analytics domain.
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