Machine Learning Implementation

Java SE 8    |    Intermediate
  • 12 Videos | 1h 26m 39s
  • Includes Assessment
  • Earns a Badge
Likes 43 Likes 43
Explore the various machine learning techniques and implementations using Java libraries, and learn to identify certain scenarios where you can implement algorithms.

WHAT YOU WILL LEARN

  • identify the critical relation between machine learning and artificial intelligence
    specify the various classifications of machine learning algorithms
    describe the differences between supervised and unsupervised learning
    state how to implement K-Means clusters
    describe how to implement KNN algorithms
    implement decision tree and random forest
  • recall how to use and work with linear regression analysis
    implement gradient boosting algorithms using Java
    illustrate the implementation of logistic regression using Java
    recognize the usage and objective of probabilistic classifiers for statistical classification
    implement Naïve Bayes classifier using Java
    demonstrate how to use the K-Mean algorithm in ML applications

IN THIS COURSE

  • Playable
    1. 
    Machine Learning and Artificial Intelligence
    3m 11s
    UP NEXT
  • Playable
    2. 
    Machine Learning Algorithm Types
    4m 59s
  • Locked
    3. 
    Supervised and Unsupervised Learning
    6m 34s
  • Locked
    4. 
    K-Means Cluster
    7m 18s
  • Locked
    5. 
    KNN Algorithms
    9m 45s
  • Locked
    6. 
    Decision Tree and Random Forest
    12m 58s
  • Locked
    7. 
    Linear Regression Analysis
    8m 50s
  • Locked
    8. 
    Gradient Boosting Algorithms
    10m 41s
  • Locked
    9. 
    Logistics Regression
    8m 9s
  • Locked
    10. 
    Probabilistic Classifier
    3m 10s
  • Locked
    11. 
    Naïve Bayes Classifier
    7m 18s
  • Locked
    12. 
    Exercise: Implementing Machine Learning Algorithms
    3m 45s

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