Practical AI with Python and Reinforcement Learning
- Descripción
- Currículum
- Reseñas
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 ultimate course 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:
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Artificial Neural Networks
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Convolution Neural Networks
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Classical Q-Learning
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Deep Q-Learning
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SARSA
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Cross Entropy Methods
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Double DQN
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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

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14Introduction to MatplotlibVideo lesson
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15Matplotlib BasicsVideo lesson
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16Matplotlib - Understanding the Figure ObjectVideo lesson
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17Matplotlib - Implementing Figures and AxesVideo lesson
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18Matplotlib - Figure ParametersVideo lesson
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19Matplotlib - Subplots FunctionalityVideo lesson
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20Matplotlib Styling - LegendsVideo lesson
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21Matplotlib Styling - Colors and StylesVideo lesson
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22Advanced Matplotlib Commands (Optional)Video lesson
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23Matplotlib Exercise Questions OverviewVideo lesson
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24Matplotlib Exercise Questions - SolutionsVideo lesson
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27Pandas and Scikit-Learn OverviewText lesson
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28Pandas - Series Part OneVideo lesson
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29Pandas - Series Part TwoVideo lesson
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30Pandas - DataFrames - Part OneVideo lesson
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31Pandas - DataFrames - Part TwoVideo lesson
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32Pandas - DataFrames - Part ThreeVideo lesson
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33Pandas - DataFrames - Part FourVideo lesson
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34Scikit-Learn - Using Train-Test-SplitVideo lesson
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35Scikit-Learn - Using MetricsVideo lesson
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36Introduction to Artificial Neural NetworksVideo lesson
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37Perceptron ModelVideo lesson
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38Neural NetworksVideo lesson
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39Activation FunctionsVideo lesson
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40Multi-Class Classification ConsiderationsVideo lesson
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41Cost Functions and Gradient DescentVideo lesson
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42BackpropagationVideo lesson
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43TensorFlow vs. Keras ExplainedVideo lesson
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44Keras Syntax - Preparing the DataVideo lesson
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45Keras Syntax - Creating and Training the ModelVideo lesson
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46Keras Syntax - Model EvaluationVideo lesson
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47Keras Regression - Exploratory Data AnalysisVideo lesson
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48Keras Regression - EDA ContinuedVideo lesson
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49Keras Regression - Data Preprocessing and Model CreationVideo lesson
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50Keras Regression - Model Evaluation and PredictionsVideo lesson
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51Keras Classification - EDA and PreprocessingVideo lesson
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52Keras Classification - Overfitting and EvaluationVideo lesson
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53Keras Classification - Overview of Project OptionsVideo lesson
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54Keras Project Notebook Exercise OverviewVideo lesson
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55Keras Project Solution - Exploratoy Data AnalysisVideo lesson
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56Keras Project Solutions - Missing Data - Part OneVideo lesson
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57Keras Project Solutions - Dealing with Missing Data - Part TwoVideo lesson
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58Keras Project Solutions - Categorical DataVideo lesson
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59Keras Project Solutions - Data PreprocessingVideo lesson
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60Keras Project Solutions- Creating and Training the ModelVideo lesson
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61Keras Project Solutions - Model EvaluationVideo lesson
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62TensorboardVideo lesson
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63Convolutional Neural Networks Section OverviewVideo lesson
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64Image Filters and KernelsVideo lesson
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65Convolutional LayersVideo lesson
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66Pooling LayersVideo lesson
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67MNIST Data Set OverviewVideo lesson
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68CNN on MNIST - The DataVideo lesson
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69CNN on MNIST - Creating and Training the ModelVideo lesson
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70CNN on MNIST - Model EvaluationVideo lesson
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71CNN on CIFAR-10 - The DataVideo lesson
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72CNN on CIFAR-10 - Evaluating the ModelVideo lesson
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73Downloading Data Set for Real Image LecturesVideo lesson
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74CNN on Real Image Files - Reading in the DataVideo lesson
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75CNN on Real Image Files - Data GenerationVideo lesson
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76CNN on Real Image Files - Creating the ModelVideo lesson
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77CNN on Real Image Files - Model EvaluationVideo lesson
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78CNN Exercise Project OverviewVideo lesson
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79CNN Exercise Project SolutionsVideo lesson
