Practical AI with Python and Reinforcement Learning

Practical AI with Python and Reinforcement Learning, available at $94.99, has an average rating of 4.45, with 156 lectures, 1 quizzes, based on 1163 reviews, and has 12217 subscribers.

You will learn about Reinforcement Learning with Python Creating Artificial Neural Networks with TensorFlow Using TensorFlow to create Convolution Neural Networks for Images Using OpenAI to work with built-in game environments Using OpenAI to create your own environments for any problem Create Artificially Intelligent Agents Tabular Q-Learning State–action–reward–state–action (SARSA) Deep Q-Learning (DQN) DQN using Convolutional Neural Networks Cross Entropy Method for Reinforcement Learning Double DQN Dueling DQN This course is ideal for individuals who are Python developers familiar with basics of machine learning, such as Scikit-Learn, but now want to learn how to create Artificially Intelligent Agents through Reinforcement Learning It is particularly useful for Python developers familiar with basics of machine learning, such as Scikit-Learn, but now want to learn how to create Artificially Intelligent Agents through Reinforcement Learning.

Enroll now: Practical AI with Python and Reinforcement Learning

Summary

Title: Practical AI with Python and Reinforcement Learning

Price: $94.99

Average Rating: 4.45

Number of Lectures: 156

Number of Quizzes: 1

Number of Published Lectures: 156

Number of Published Quizzes: 1

Number of Curriculum Items: 157

Number of Published Curriculum Objects: 157

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Reinforcement Learning with Python
Creating Artificial Neural Networks with TensorFlow
Using TensorFlow to create Convolution Neural Networks for Images
Using OpenAI to work with built-in game environments
Using OpenAI to create your own environments for any problem
Create Artificially Intelligent Agents
Tabular Q-Learning
State–action–reward–state–action (SARSA)
Deep Q-Learning (DQN)
DQN using Convolutional Neural Networks
Cross Entropy Method for Reinforcement Learning
Double DQN
Dueling DQN

Who Should Attend

Python developers familiar with basics of machine learning, such as Scikit-Learn, but now want to learn how to create Artificially Intelligent Agents through Reinforcement Learning

Target Audiences

Python developers familiar with basics of machine learning, such as Scikit-Learn, but now want to learn how to create Artificially Intelligent Agents through Reinforcement Learning

Please note! This course is in an “early bird” release, and we’re still updating and adding content to it, please keep in mind before enrolling that the course is not yet complete.

“The future is already here – it’s just not very evenly distributed.“

Have you ever wondered how Artificial Intelligence actually works? Do you want to be able to harness the power of neural networks and reinforcement learning to create intelligent agents that can solve tasks with human level complexity?

This is the ultimatecourse online for learning how to use Python to harness the power of Neural Networks to create Artificially Intelligent agents!

This course focuses on a practical approach that puts you in the driver’s seat to actually build and create intelligent agents, instead of just showing you small toy examples like many other online courses. Here we focus on giving you the power to apply artificial intelligence to your own problems, environments, and situations, not just those included in a niche library!

This course covers the following topics:

Artificial Neural Networks
Convolution Neural Networks
Classical Q-Learning
Deep Q-Learning
SARSA
Cross Entropy Methods
Double DQN
and much more!

We’ve designed this course to get you to be able to create your own deep reinforcement learning agents on your own environments. It focuses on a practical approach with the right balance of theory and intuition with useable code. The course uses clear examples in slides to connect mathematical equations to practical code implementation, before showing how to manually implement the equations that conduct reinforcement learning.

We’ll first show you how Deep Learning with Keras and TensorFlow works, before diving into Reinforcement Learning concepts, such as Q-Learning. Then we can combine these ideas to walk you through Deep Reinforcement Learning agents, such as Deep Q-Networks!

There is still a lot more to come, I hope you’ll join us inside the course!

Jose

Course Curriculum

Chapter 1: Course Overview

Lecture 1: Welcome Message

Lecture 2: Course Curriculum Overview

Lecture 3: Course Success and Overview

Chapter 2: Course Set-Up and Installation Procedures

Lecture 1: Anaconda and Jupyter Notebook Install and Setup

Lecture 2: Note on Environment Setup

Lecture 3: Environment Setup Walkthrough

Chapter 3: Numpy Basics Overview

Lecture 1: Introduction to Numpy Section

Lecture 2: NumPy Arrays

Lecture 3: Numpy Operations – Part One

Lecture 4: Numpy Operations – Part Two

Lecture 5: Numpy Exercise Overview

Lecture 6: Numpy Exercise Solutions

Chapter 4: Matplotlib and Visualization Overview

Lecture 1: Introduction to Matplotlib

Lecture 2: Matplotlib Basics

Lecture 3: Matplotlib – Understanding the Figure Object

Lecture 4: Matplotlib – Implementing Figures and Axes

Lecture 5: Matplotlib – Figure Parameters

Lecture 6: Matplotlib – Subplots Functionality

Lecture 7: Matplotlib Styling – Legends

Lecture 8: Matplotlib Styling – Colors and Styles

Lecture 9: Advanced Matplotlib Commands (Optional)

Lecture 10: Matplotlib Exercise Questions Overview

Lecture 11: Matplotlib Exercise Questions – Solutions

Chapter 5: Machine Learning, Deep Learning, and Reinforcement Learning

Lecture 1: What is Machine Learning, Deep Learning, and Artificial Intelligence?

Lecture 2: Supervised Machine Learning Process

Chapter 6: Pandas and Scikit-Learn Crash Course

Lecture 1: Pandas and Scikit-Learn Overview

Lecture 2: Pandas – Series Part One

Lecture 3: Pandas – Series Part Two

Lecture 4: Pandas – DataFrames – Part One

Lecture 5: Pandas – DataFrames – Part Two

Lecture 6: Pandas – DataFrames – Part Three

Lecture 7: Pandas – DataFrames – Part Four

Lecture 8: Scikit-Learn – Using Train-Test-Split

Lecture 9: Scikit-Learn – Using Metrics

Chapter 7: Artificial Neural Network and TensorFlow Basics

Lecture 1: Introduction to Artificial Neural Networks

Lecture 2: Perceptron Model

Lecture 3: Neural Networks

Lecture 4: Activation Functions

Lecture 5: Multi-Class Classification Considerations

Lecture 6: Cost Functions and Gradient Descent

Lecture 7: Backpropagation

Lecture 8: TensorFlow vs. Keras Explained

Lecture 9: Keras Syntax – Preparing the Data

Lecture 10: Keras Syntax – Creating and Training the Model

Lecture 11: Keras Syntax – Model Evaluation

Lecture 12: Keras Regression – Exploratory Data Analysis

Lecture 13: Keras Regression – EDA Continued

Lecture 14: Keras Regression – Data Preprocessing and Model Creation

Lecture 15: Keras Regression – Model Evaluation and Predictions

Lecture 16: Keras Classification – EDA and Preprocessing

Lecture 17: Keras Classification – Overfitting and Evaluation

Lecture 18: Keras Classification – Overview of Project Options

Lecture 19: Keras Project Notebook Exercise Overview

Lecture 20: Keras Project Solution – Exploratoy Data Analysis

Lecture 21: Keras Project Solutions – Missing Data – Part One

Lecture 22: Keras Project Solutions – Dealing with Missing Data – Part Two

Lecture 23: Keras Project Solutions – Categorical Data

Lecture 24: Keras Project Solutions – Data Preprocessing

Lecture 25: Keras Project Solutions- Creating and Training the Model

Lecture 26: Keras Project Solutions – Model Evaluation

Lecture 27: Tensorboard

Chapter 8: Convolutional Neural Networks with TensorFlow

Lecture 1: Convolutional Neural Networks Section Overview

Lecture 2: Image Filters and Kernels

Lecture 3: Convolutional Layers

Lecture 4: Pooling Layers

Lecture 5: MNIST Data Set Overview

Lecture 6: CNN on MNIST – The Data

Lecture 7: CNN on MNIST – Creating and Training the Model

Lecture 8: CNN on MNIST – Model Evaluation

Lecture 9: CNN on CIFAR-10 – The Data

Lecture 10: CNN on CIFAR-10 – Evaluating the Model

Lecture 11: Downloading Data Set for Real Image Lectures

Lecture 12: CNN on Real Image Files – Reading in the Data

Lecture 13: CNN on Real Image Files – Data Generation

Lecture 14: CNN on Real Image Files – Creating the Model

Lecture 15: CNN on Real Image Files – Model Evaluation

Lecture 16: CNN Exercise Project Overview

Lecture 17: CNN Exercise Project Solutions

Chapter 9: Reinforcement Learning – Core Concepts

Lecture 1: Overview of Core Concepts for Reinforcement Learning Section

Lecture 2: Agents, Environments, and Policy

Lecture 3: Rewards, Discount Factors, and Bellman Equation

Lecture 4: Deterministic vs. Stochastic Processes

Lecture 5: Tabular Reinforcement Learning

Chapter 10: Open AI Gym Overview

Lecture 1: Introduction to OpenAI Gym Section

Lecture 2: OpenAI Overview and History

Lecture 3: OpenAI Gym – Documentation Tour

Lecture 4: OpenAI Gym – Environment Key Ideas

Lecture 5: OpenAI Gym – Working with the Environment

Lecture 6: OpenAI Gym – Agent Interacting with the Environment

Instructors

Jose Portilla
Head of Data Science at Pierian Training
Pierian Training
Data Science and Machine Learning Training

Rating Distribution

1 stars: 17 votes
2 stars: 18 votes
3 stars: 60 votes
4 stars: 311 votes
5 stars: 757 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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