Machine Learning

Artificial Intelligence    |    Beginner
  • 14 videos | 40m 42s
  • Includes Assessment
Likes 180 Likes 180
Sometimes agents must learn how to associate certain conditions with actions and outcomes. Explore the principles of machine learning and how to use it to make smarter agents.

WHAT YOU WILL LEARN

  • describe how AI learns and the different types of machine learning
    describe how examples can be used for learning
    describe decision trees and how the model expresses knowledge
    describe entropy and information gain for learning decision tree models
    describe how to choose attributes to learn a decision tree
    describe overfitting and how decision tree models can be made to mitigate this issue
    describe neural networks and how they apply to artificial intelligence
  • describe the structure of a neural network and its individual neurons
    list some of the common types of neural networks and what problems they might be good at solving
    describe how machine learning works with a perceptron
    describe how perceptron learning can be generalized to a multilayered neural network
    describe convolutional neural networks
    describe recurrent neural networks
    describe how a perceptron can learn how to achieve a particular result given a set of examples

IN THIS COURSE

  • 3m 31s
    After completing this video, you will be able to describe how AI learns and the different types of machine learning. FREE ACCESS
  • 3m 2s
    After completing this video, you will be able to describe how examples can be used for learning. FREE ACCESS
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    3.  Using Decision Trees
    1m 19s
    After completing this video, you will be able to describe decision trees and how the model expresses knowledge. FREE ACCESS
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    4.  Decision Tree Learning: Information Gain
    2m 5s
    After completing this video, you will be able to describe entropy and information gain for learning decision tree models. FREE ACCESS
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    5.  Decision Tree Learning: Choosing Attributes
    4m 43s
    Upon completion of this video, you will be able to describe how to choose attributes to learn a decision tree. FREE ACCESS
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    6.  Overfitting
    2m 23s
    Upon completion of this video, you will be able to describe overfitting and how decision tree models can be made to prevent this issue. FREE ACCESS
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    7.  Artificial Neural Networks
    2m 43s
    After completing this video, you will be able to describe neural networks and how they are used in artificial intelligence. FREE ACCESS
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    8.  Neural Network Structure
    2m 19s
    After completing this video, you will be able to describe the structure of a neural network and its individual neurons. FREE ACCESS
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    9.  Types of Neural Networks
    4m 19s
    After completing this video, you will be able to list some of the common types of neural networks and what problems they are good at solving. FREE ACCESS
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    10.  Perceptron Learning
    1m 24s
    Upon completion of this video, you will be able to describe how a perceptron works with machine learning. FREE ACCESS
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    11.  Deep Neural Networks
    3m 43s
    Upon completion of this video, you will be able to describe how perceptron learning can be generalized to a multilayer neural network. FREE ACCESS
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    12.  Convolutional Neural Networks
    3m 15s
    After completing this video, you will be able to describe convolutional neural networks. FREE ACCESS
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    13.  Recurrent Neural Networks
    3m 53s
    After watching this video, you will be able to describe recurrent neural networks. FREE ACCESS
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    14.  Exercise: Make a Perceptron Learn From Examples
    2m 2s
    Upon completion of this video, you will be able to describe how a perceptron can learn how to achieve a particular result given a set of examples. FREE ACCESS

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