Udemy – Artificial Intelligence: Reinforcement Learning in Python

Description

Master Reinforcement Learning: From Foundations to Real-World AI Applications

Requirements

To get the most out of this course, you should have:

• Calculus: Derivatives and basic understanding of functions

• Probability & Markov Models: Familiarity with probability theory and stochastic processes

• Python & Numpy: Strong object-oriented programming skills and experience with Numpy

• Matplotlib: For visualizing data and learning curves

• Machine Learning Knowledge (Beneficial): Experience with supervised learning methods

• Optimization Techniques: Understanding of gradient descent

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Course Description

Ever wondered how AI systems like OpenAI ChatGPT, GPT-4, or self-driving cars really work? This course is your ultimate guide to understanding reinforcement learning (RL)—the cutting-edge AI technique behind these technologies.

Unlike traditional supervised or unsupervised machine learning, RL empowers machines to learn from experience, make decisions, and improve autonomously. From AlphaGo defeating the world champion in Go to AI mastering complex video games, reinforcement learning is reshaping the future of artificial intelligence.

With the exponential growth of AI capabilities, mastering RL now puts you ahead of the curve—whether you aim to develop intelligent agents, autonomous systems, or advanced AI applications.

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What You Will Learn

This course provides a comprehensive, hands-on approach to reinforcement learning, covering:

1. The Multi-Armed Bandit Problem & the Explore-Exploit Dilemma

2. Calculating Means and Moving Averages in the context of stochastic gradient descent

3. Markov Decision Processes (MDPs) for modeling sequential decision-making

4. Dynamic Programming techniques for optimal policy determination

5. Monte Carlo Methods for predicting future rewards

6. Temporal Difference (TD) Learning: Q-Learning and SARSA

7. Function Approximation Methods: Integrating deep neural networks or other differentiable models into RL algorithms

8. OpenAI Gym Integration: Learn RL in popular simulation environments with minimal setup

9. Hands-On Project: Apply Q-Learning to build a stock trading bot

By the end of this course, you will have the skills to implement reinforcement learning algorithms from scratch, understand their theoretical foundations, and apply them to real-world problems.

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Why This Course is Unique

• Implementation-Focused: Every algorithm is coded from scratch for deeper understanding

• Math Made Practical: University-level concepts explained with real-world examples

• No Busy Work: Focus on meaningful, learning-oriented coding—not repetitive library usage

• Expert Guidance: Step-by-step explanations and direct support for questions

As Richard Feynman said: “What I cannot create, I do not understand.”
This course follows that philosophy: if you can implement it, you truly understand it.

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Suggested Prerequisites

• Calculus (derivatives, basic functions)

• Probability & Statistics

• Python programming (loops, conditionals, lists, dicts, sets)

• Object-oriented programming

• Numpy (matrix and vector operations)

• Basics of linear regression and gradient descent

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Who This Course is For

• Students and professionals interested in AI, machine learning, and data science

• Anyone aiming to master reinforcement learning and its applications in modern AI

• Developers seeking a hands-on, implementation-driven course beyond standard supervised learning

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Recommended Course Roadmap

For beginners, start with foundational courses in Python, Numpy, and supervised machine learning. Check the “Machine Learning and AI Prerequisite Roadmap” in the FAQ for a step-by-step learning path.

Please Note: Files will be included in this purchase only Full Course Video & Course Resources. You will get cloud storage download link with life time download access.