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

Predictive Analytics
  • 9 Videos | 38m 45s
  • Includes Assessment
  • Earns a Badge
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 58s
    UP NEXT
  • Playable
    2. 
    Distance and Weight Measures for Numeric Attributes
    4m 47s
  • Locked
    3. 
    Proximity Measures for Non-numeric Attributes
    4m 30s
  • Locked
    4. 
    Implementing the k-NN Algorithm
    2m 46s
  • Locked
    5. 
    Overview of Artificial Neural Networks
    5m 37s
  • Locked
    6. 
    Basic Artificial Neural Networks
    5m 2s
  • Locked
    7. 
    Advanced Artificial Neural Network Concepts
    5m 18s
  • Locked
    8. 
    Important Parameters for Artificial Neural Networks
    4m 56s
  • Locked
    9. 
    Implementing an Artificial Neural Network
    2m 52s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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