Disclosure: when you buy through links on our site, we may earn an affiliate commission.

Artificial Intelligence: Reinforcement Learning in Python

Complete guide to Reinforcement Learning, with Stock Trading and Online Advertising Applications
4.6
4.6/5
(8,438 reviews)
40,527 students
Created by

9.4

CourseMarks Score®

10.0

Freshness

9.4

Feedback

8.3

Content

Platform: Udemy
Price: $49.99
Video: 13h 32m
Language: English
Next start: On Demand

Top Python courses:

Detailed Analysis

CourseMarks Score®

9.4 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

10.0 / 10
This course was last updated on 4/2021.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

9.4 / 10
We analyzed factors such as the rating (4.6/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

8.3 / 10
Video Score: 9.7 / 10
The course includes 13h 32m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
The average video length is 7 hours 24 minutes of 1,300 Python courses on Udemy.
Detail Score: 9.7 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 5.5 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

0 article.
0 resource.
0 exercise.
0 test.

Table of contents

Description

When people talk about artificial intelligence, they usually don’t mean supervised and unsupervised machine learning.
These tasks are pretty trivial compared to what we think of AIs doing – playing chess and Go, driving cars, and beating video games at a superhuman level.
Reinforcement learning has recently become popular for doing all of that and more.
Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn’t been until recently that we’ve been able to observe first hand the amazing results that are possible.
In 2016 we saw Google’s AlphaGo beat the world Champion in Go.
We saw AIs playing video games like Doom and Super Mario.
Self-driving cars have started driving on real roads with other drivers and even carrying passengers (Uber), all without human assistance.
If that sounds amazing, brace yourself for the future because the law of accelerating returns dictates that this progress is only going to continue to increase exponentially.
Learning about supervised and unsupervised machine learning is no small feat. To date I have over SIXTEEN (16!) courses just on those topics alone.
And yet reinforcement learning opens up a whole new world. As you’ll learn in this course, the reinforcement learning paradigm is more different from supervised and unsupervised learning than they are from each other.
It’s led to new and amazing insights both in behavioral psychology and neuroscience. As you’ll learn in this course, there are many analogous processes when it comes to teaching an agent and teaching an animal or even a human. It’s the closest thing we have so far to a true general artificial intelligence.What’s covered in this course?
•The multi-armed bandit problem and the explore-exploit dilemma
•Ways to calculate means and moving averages and their relationship to stochastic gradient descent
•Markov Decision Processes (MDPs)
•Dynamic Programming
•Monte Carlo
•Temporal Difference (TD) Learning (Q-Learning and SARSA)
•Approximation Methods (i.e. how to plug in a deep neural network or other differentiable model into your RL algorithm)
•Project: Apply Q-Learning to build a stock trading bot
If you’re ready to take on a brand new challenge, and learn about AI techniques that you’ve never seen before in traditional supervised machine learning, unsupervised machine learning, or even deep learning, then this course is for you.
See you in class!

“If you can’t implement it, you don’t understand it”
•Or as the great physicist Richard Feynman said: “What I cannot create, I do not understand”.
•My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
•Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
•After doing the same thing with 10 datasets, you realize you didn’t learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times…

Suggested Prerequisites:
•Calculus
•Probability
•Object-oriented programming
•Python coding: if/else, loops, lists, dicts, sets
•Numpy coding: matrix and vector operations
•Linear regression
•Gradient descent

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
•Check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in the FAQ of any of my courses, including the free Numpy course)

You will learn

✓ Apply gradient-based supervised machine learning methods to reinforcement learning
✓ Understand reinforcement learning on a technical level
✓ Understand the relationship between reinforcement learning and psychology
✓ Implement 17 different reinforcement learning algorithms

Requirements

• Calculus (derivatives)Probability / Markov Models
• Numpy, Matplotlib
• Beneficial to have experience with at least a few supervised machine learning methods
• Gradient descent
• Good object-oriented programming skills

This course is for

• Anyone who wants to learn about artificial intelligence, data science, machine learning, and deep learning
• Both students and professionals

How much does the Artificial Intelligence: Reinforcement Learning in Python course cost? Is it worth it?

The course costs $49.99.
The average price is $19.0 of 1,300 Python courses. So this course is 163% more expensive than the average Python course on Udemy.

Does the Artificial Intelligence: Reinforcement Learning in Python course have a money back guarantee or refund policy?

YES, Artificial Intelligence: Reinforcement Learning in Python has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

At the moment we could not find an available scholarship for Artificial Intelligence: Reinforcement Learning in Python.

Who is the instructor? Is Lazy Programmer Team a SCAM or a TRUSTED instructor?

Lazy Programmer Team has created 9 courses that got 29,152 reviews which are generally positive. Lazy Programmer Team has taught 188,439 students and received a 4.6 average review out of 29,152 reviews. Depending on the information available, Lazy Programmer Team is a TRUSTED instructor.
Artificial Intelligence and Machine Learning Engineer
Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.I’ve created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School.Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I’ve used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I’ve used MySQL, Postgres, Redis, MongoDB, and more.

9.4

CourseMarks Score®

10.0

Freshness

9.4

Feedback

8.3

Content

Platform: Udemy
Price: $49.99
Video: 13h 32m
Language: English
Next start: On Demand

Students are also interested in

Get this widget on your website (for course creators):

Artificial Intelligence: Reinforcement Learning in Python rating
Copy this code and paste it to your website:
<a href="https://coursemarks.com/course/artificial-intelligence-reinforcement-learning-in-python/" target="_blank" title="Artificial Intelligence: Reinforcement Learning in Python on Coursemarks.com"><img border="0" src="https://coursemarks.com/widget/94.svg" width="200px" alt="Artificial Intelligence: Reinforcement Learning in Python rating"/></a>