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

Predictive Analytics    |    Intermediate
  • 9 Videos | 42m 49s
  • Includes Assessment
  • Earns a Badge
Likes 34 Likes 34
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

  • Playable
    1. 
    Overview of the k-NN Algorithm
    2m 55s
    UP NEXT
  • Playable
    2. 
    Distance and Weight Measures for Numeric Attributes
    4m 44s
  • Locked
    3. 
    Proximity Measures for Non-numeric Attributes
    4m 28s
  • Locked
    4. 
    Implementing the k-NN Algorithm
    2m 43s
  • Locked
    5. 
    Overview of Artificial Neural Networks
    5m 34s
  • Locked
    6. 
    Basic Artificial Neural Networks
    4m 59s
  • Locked
    7. 
    Advanced Artificial Neural Network Concepts
    5m 15s
  • Locked
    8. 
    Important Parameters for Artificial Neural Networks
    4m 53s
  • Locked
    9. 
    Implementing an Artificial Neural Network
    2m 49s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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