Explore the various machine learning techniques and implementations using Java libraries, and learn to identify certain scenarios where you can implement algorithms.
Developing AI and ML Solutions with Java: Machine Learning Implementation
- 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
If you would like to provide feedback for this course, please e-mail the NICCS SO at NICCS@hq.dhs.gov.