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    • Deep Reinforcement Learning Hands on Artificial Intelligence Tutorial in Python

    Deep Reinforcement Learning: Hands-on AI Tutorial in Python

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    Udemy

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    Mode

    Online

    Fees

    ₹ 499 2299

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course and certificate fees

    Fees information
    ₹ 499  ₹2,299
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The syllabus

    Introduction

    • Introduction
    • Course Structure
    • Environment Setup

    Jump into Reinforcement Learning

    • Introduction
    • RL Applications
    • RL vs. Supervised and Unsupervised Learning
    • What is reinforcement learning?

    RL Algorithms

    • Markov Decision Process
    • Optimal Policy
    • Bellman Equation
    • Q-Learning
    • Step-by-step Example
    • Sarsa
    • Deep Q-Network
    • Exploration vs. Exploitation
    • Define RL Problem - Examples
    • Reinforcement learning algorithms
    • SARSA algorithm

    Hands-on Project 1 - Maze Problem

    • Overall Design
    • Create Project
    • Create files
    • Create Maze Environment class
    • Implement Building Maze Grid
    • Test build_maze method
    • Render and Reset methods
    • Implement getting next state and reward
    • Create Agent class
    • Implement adding states
    • Implement choosing action
    • Implement learn method
    • Create App
    • Implement main method
    • Implement plotting results
    • Run the App
    • Expand Maze Environment

    Hands-on Project 2 - Stock Trading

    • Overall Design
    • Start project
    • Prepare dataset
    • Create Market Environment class
    • Implement getting data
    • Implement getting all states
    • Implement getting next state and reward
    • Create Agent class
    • Implement creating deep learning model and reset method
    • Implement getting action
    • Implement buy and sell
    • Implement experience replay
    • Create training app
    • Test training app
    • Create evaluation app
    • Implement plotting results
    • Run training and evaluation
    • Extending Stock Trading with Multiple Features
    • Multiple Feature Stock Trader

    Summary

    • Summary

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