Expert Systems & Reinforcement Learning

Java SE 8    |    Intermediate
  • 12 videos | 47m 10s
  • Includes Assessment
  • Earns a Badge
Rating 5.0 of 4 users Rating 5.0 of 4 users (4)
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

  • 2m 55s
    After completing this video, you will be able to list the tools, shells, and programming languages that are used for Expert Systems. FREE ACCESS
  • 4m 17s
    Find out how to work with Jess to create rule-based expert systems. FREE ACCESS
  • Locked
    3.  Defining Rules
    4m 7s
    Upon completion of this video, you will be able to describe how to define rules and work with the expert system shell using Java. FREE ACCESS
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    4.  Supervised Learning and Notations
    4m 43s
    After completing this video, you will be able to recognize data notations from the perspective of quality, descriptive, and visualization notations. FREE ACCESS
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    5.  Datasets and Training Models
    3m 46s
    Upon completion of this video, you will be able to list the different types of datasets and their usefulness during the various phases of supervised learning. FREE ACCESS
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    6.  Outlier Types
    3m 6s
    In this video, you will identify the various types of Outliers and their impact on the accuracy of the models. FREE ACCESS
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    7.  Feature Search and Feature Evaluation Techniques
    5m 1s
    After completing this video, you will be able to describe the various approaches to feature relevance search and the evaluation techniques. FREE ACCESS
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    8.  Principal Component Analysis Data Transformation
    4m 39s
    In this video, you will learn how to implement a principal component analysis data transformation using Java. FREE ACCESS
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    9.  Clustering Concept
    3m 34s
    Upon completion of this video, you will be able to recognize the clustering implementation algorithms and illustrate the validation and evaluation techniques. FREE ACCESS
  • Locked
    10.  Hierarchical Clustering
    4m 11s
    In this video, you will implement hierarchical clustering using the top-down approach with Java. FREE ACCESS
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    11.  Graph Modeling
    3m 45s
    After completing this video, you will be able to describe the concept of graph modeling and the various approaches of implementing graphs in machine learning. FREE ACCESS
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    12.  Exercise: Working with Datasets and Clustering
    3m 7s
    In this video, you will learn how to use datasets with clustering algorithms. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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