K-Nearest Neighbor (k-NN) & Artificial Neural Networks

Predictive Analytics    |    Intermediate
  • 9 videos | 38m 19s
  • Includes Assessment
  • Earns a Badge
Rating 4.4 of 20 users Rating 4.4 of 20 users (20)
Choosing the appropriate technique to deliver confident predictions can be challenging for analysts. Examine algorithms used for predictive analytics, including the K-Nearest Neighbor (k-NN) algorithm and artificial neural network modeling.

WHAT YOU WILL LEARN

  • Recognize features of the k-nn algorithm
    Recognize distance and weighted distance measures
    Recognize proximity measures for non-numeric attributes
    Implement the k-nn algorithm
    Identify key features of artificial neural networks
  • Recognize steps and considerations to building artificial neural networks
    Recognize the purpose of nonlinear activation functions and methods to find the global minimum sse
    Recognize important parameters for artificial neural networks
    Implement an artificial neural network

IN THIS COURSE

  • 2m 55s
    After completing this video, you will be able to recognize features of the k-nearest neighbor algorithm. FREE ACCESS
  • 4m 44s
    After completing this video, you will be able to recognize distance and weighted distance measures. FREE ACCESS
  • Locked
    3.  Proximity Measures for Non-numeric Attributes
    4m 28s
    After completing this video, you will be able to recognize proximity measures for non-numeric attributes. FREE ACCESS
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    4.  Implementing the k-NN Algorithm
    2m 43s
    In this video, you will learn how to implement the k-nearest neighbors algorithm. FREE ACCESS
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    5.  Overview of Artificial Neural Networks
    5m 34s
    In this video, you will learn about the key features of artificial neural networks. FREE ACCESS
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    6.  Basic Artificial Neural Networks
    4m 59s
    Upon completion of this video, you will be able to recognize steps and considerations for building artificial neural networks. FREE ACCESS
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    7.  Advanced Artificial Neural Network Concepts
    5m 15s
    After completing this video, you will be able to recognize the purpose of nonlinear activation functions and methods to find the global minimum sum of squared errors. FREE ACCESS
  • Locked
    8.  Important Parameters for Artificial Neural Networks
    4m 53s
    Upon completion of this video, you will be able to recognize important parameters for artificial neural networks. FREE ACCESS
  • Locked
    9.  Implementing an Artificial Neural Network
    2m 49s
    In this video, you will learn how to implement a neural network. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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