Artificial Intelligence Course: Reinforcement Learning in Python

September 14, 2022
Updated 2022/09/14 at 5:00 PM
5 Min Read
Artificial inteligence Course
Artificial inteligence Course

Here we offer computerized reasoning artificial intelligence course that walk you through joining profound learning, AI, and other artificial intelligence advancements to further develop your business.

This artificial intelligence course is a complete guide to Reinforcement Learning, with Stock Trading and Online Advertising Applications

What you’ll 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


  • 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


For the larger part of the time, when individuals examine man-made brainpower, they don’t necessarily suggest regulated and solo AI.

These undertakings fail to measure up to what we envision AIs to be able to do, for example, dominating computer games, driving vehicles, and playing chess and go.

As of late, support learning has acquired prevalence for achieving those objectives from there, the sky is the limit.

Like profound learning, the majority of the hypotheses were created during the 1970s and 1980s. However, it was only later that we could really see the unbelievable results that were feasible. As seen, Google’s AlphaGo crushed the Go title holder in 2016.

Computer games like Destruction and Super Mario were played by AIs. Self-driving vehicles have started utilizing genuine streets with different drivers.

Assuming that sounds astounding, prepare yourself for the future in light of the fact that the law of speeding up returns directs that this progress is simply going to dramatically keep on expanding.

Finding out about administered and unaided AI is very difficult. Until this point, I had taken more than 25 (25!) courses simply on those subjects alone.

But supported learning opens up an entirely different world. As you’ll learn in this artificial intelligence course, the support gaining worldview is very much from both regulated and solo learning.

It’s directed at new and astounding experiences both in social brain science and neuroscience. As you’ll learn in this artificial intelligence course there are numerous undifferentiated processes with regard to showing a specialist and showing a creature or even a human. It’s the closest thing we have come such a long way to genuine fake general knowledge. 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 another differentiable model into your RL algorithm)
  • How to use OpenAI Gym, with zero code changes
  • Project: Apply Q-Learning to build a stock trading bot

Assuming you’re prepared to take on a spic and span challenge and find out about artificial intelligence methods that you’ve never seen before in conventional directed AI, solo AI, or even profound learning, then this artificial intelligence course is for you.

See you in class!

“In the event that you can’t execute it, you don’t figure out 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 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


  • Check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in the FAQ 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

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