Expert Systems & Reinforcement Learning

Java SE 8
  • 12 Videos | 52m 40s
  • Includes Assessment
  • Earns a Badge
Likes 3 Likes 3
Explore the concepts of expert system along with its Implementation using Java based frameworks, and examine the implementation and usages of ND4J and Arbiter to facilitate optimization.

WHAT YOU WILL LEARN

  • list the tools, shells, and programming languages that are being used for Expert Systems
    work with Jess to create rule based expert systems
    describe how to define rules and work with expert system shell using Java
    recognize data notations from the perspective of quality, descriptive, and visualization notations
    list the different types of datasets and their utility over the various phases of supervised learning
    identify the various types of Outliers and their impact on the accuracy of the models
  • describe the various approaches of feature relevance search and the evaluation techniques
    implement principal component analysis data transformation using Java pca-tranform
    recognize the clustering implementation algorithms and illustrate the validation and evaluation techniques
    implement hierarchical clustering using the top down approach with Java
    describe the concept of graph modelling and the various approaches of implementing graphs in machine learning
    demonstrate how to use datasets with clustering

IN THIS COURSE

  • Playable
    1. 
    Expert Systems Tools
    2m 55s
    UP NEXT
  • Playable
    2. 
    Working with Jess
    4m 17s
  • Locked
    3. 
    Defining Rules
    4m 7s
  • Locked
    4. 
    Supervised Learning and Notations
    4m 43s
  • Locked
    5. 
    Datasets and Training Models
    3m 46s
  • Locked
    6. 
    Outlier Types
    3m 6s
  • Locked
    7. 
    Feature Search and Feature Evaluation Techniques
    5m 1s
  • Locked
    8. 
    Principal Component Analysis Data Transformation
    4m 39s
  • Locked
    9. 
    Clustering Concept
    3m 34s
  • Locked
    10. 
    Hierarchical Clustering
    4m 11s
  • Locked
    11. 
    Graph Modeling
    3m 45s
  • Locked
    12. 
    Exercise: Working with Datasets and Clustering
    3m 7s

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