Artificial Intelligence: Reinforcement Learning in Python (Udemy.com)

Complete guide to Artificial Intelligence, prep for Deep Reinforcement Learning with Stock Trading Applications

Created by: Lazy Programmer Inc.

Produced in 2022

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What 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

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Quality Score

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Overall Score : 82 / 100

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Course Description

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artificial intelligence Awards Best Paid Course

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!

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


TIPS (for getting through the course):
  • Watch it at 2x.
  • Take handwritten notes. This will drastically increase your ability to retain the information.
  • Write down the equations. If you don't, I guarantee it will just look like gibberish.
  • Ask lots of questions on the discussion board. The more the better!
  • Realize that most exercises will take you days or weeks to complete.
  • Write code yourself, don't just sit there and look at my code.


WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
  • Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)


Who this course is for:
  • Anyone who wants to learn about artificial intelligence, data science, machine learning, and deep learning
  • Both students and professionals

*Some courses are excluded from this sale. Coupon not working? If the link above doesn't drop prices, clear the cookies in your browser and then click this link here.
Also, you may need to apply the coupon code directly on the cart page to get the discount.

Coupon Code

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Instructor Details

Lazy Programmer Inc.

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

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Reviews

4.1

100 total reviews

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This course introduces all basic concepts for Reinforcement Learning. For me the personal highlight was to write the code from scratch. Coding all the topics helped me to be very much hands on with this topic.

Great projects throughout the course. We got to write an online advertising agent and a tic tac toe agent, and the final project on building a stock trading bot. It's a nice showcase of what reinforcement learning can do but the benefit of taking this course is you get to learn with the lazy programmer who always breaks down the complicated math equations, not just ignoring them like other instructors.

I feel there are some key points that might be explained better but overall good and steady pace that eventually results in gating knowledge.Would be nice to have more examples applying learned methods to solve real problems.

Great overview of reinforcement learning methods. The theory is clearly explained, and the exercises provide for a good playing ground to understand how different methods work in practice. The course is well thought and Lazy Programmer clearly made an effort to come up with examples that are doable even for beginners. To make the best out of this course, follow the instructor's suggestion to spend time coming up with your own implementation!

This course is awesome and very dense.I strongly recommend.

Fortunately for Lazy Programmer, there are few Udemy instructors teaching reinforcement learning, but the teaching style in this course (and really all of LP's courses) is just so, so bad.If you're going to sign up for this class, you should know in advance that if you don't understand something, it is 100% your fault. How do I know this? Well, Lazy Programmer will tell you repeatedly throughout all of his courses. Of course students who have a specific background and lots of practice in the prerequisites are more likely to succeed. That's pretty obvious. But most Udemy instructors show some degree of flexibilty in explaining the concepts so that students with a wide variety of backgrounds can succeed. I've taken over 40 Udemy classes and been pleased with most of them (I think this is the first course I've rated something other than 5 stars). Lazy Programmer's videos are easily the most frustrating, but again, he's one of the few games in town in terms of reinforcement learning. The other major machine learning/deep learning instructor is SuperDataScience. Although SDS vides do not typically go into great depth, they at least get you started on a path to learning more about a topic by focusing on getting an inuition for the subject matter.The biggest issue is this: Lazy Programmer speaks in references to mathematical notation rather than underlying concepts. What that means is that 1) whenever you hear an explanation of something, you first have to mentally translate the spoken version of the mathematical symbol back into the underlying concept, and 2) if you don't fullly grasp something the first time, you'll find yourself rewinding through videos to try to figure out what some symbol means. In time, linking a concept with its mathematical notation will become easier for anyone, but presenting new material in that way is inefficient and ineffective.The only reason I'm not giving this course 1 star is that it at least seems to be thorough in terms of introductory RL subtopics, but I probably would have been better off just googling the topics (since that's what I had to do anyway).

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Lectures were detailed, but easy to understand. It would be really nice if the course provides projects and their materials.

Incredibly useful and goes over a lot that I couldn't find in other courses, especially breaking down the code line by line. The instructor did an excellent job here organizing each of the topics in order. Top notch assistance on the Q & A as a bonus.

Very knowledgeable instructor, wonderful to learn from. The presentation and structure is intuitive, you'll know why you've learned each topic because it always feeds into the next one. You'll want to be good with programming and statistics first, because this ain't no fake course.

Really enjoyed the course's content. The teaching style is just the right speed for me. All the concepts are explained so in depth I felt like I just got my PhD in RL!

This is the perfect course if you want to get to the highest level in reinforcement learning and you have some mathematics background. It's very sophisticated unlike many other courses I saw.

Despite the general difficulty in the field, the teacher explains technical terms as simply as possible, using lots of compare and contrasts and providing general intuition whenever possible. I highly recommend this class