An Introduction To Machine Learning In Java

Machine learning has taken the world by storm, and Java is a popular programming language for designing machine learning systems. Machine learning problems in Java are prime reasons why many seek Java programming assignment help from experts. Without a doubt, ML is tough and requires a solid grasp of the implementation language, ML algorithms & the underlying mathematics.

Start your journey towards mastering machine learning in Java with this handy article. It comes straight from the experts from leading online Java programming assignment help services and offers some awesome insights.

Machine Learning in Java

Choose the Right Machine Learning Library

There are a host of ML libraries in Java. Below are some of the most popular and potent ones in existence.

1. Weka
2. Apache Mahout 
3. Spark MLib
4. Deeplearning4j

We have used Weka, an open-source library suitable for general-purpose machine learning applications. You can choose anyone from the above or other libraries out there. Just be sure of your immediate and probable long-term requirements when choosing.

Supervised vs Unsupervised Learning

Java programming assignment in colleges and universities generally deal with ML problems that can be solved easily by supervised or unsupervised learning algorithms. However, if you need to deal with problems that require deep learning or neural networks, then make you have gained substantial mastery of ML.

Supervised ML algorithms train upon sets of feature vectors and associated labels. They tally vectors with the correct tags & then determine the internal structures and underlying relationships.

Unsupervised ML algos have to predict the right answer without any supervision. This makes unsupervised learning much more resource-heavy as it mines through massive datasets and requires considerable processing power.

A Sample ML Code in Java

The key challenge of supervised learning is determining the right function that accurately maps feature vectors to the right labels.

Following is a code that implements Java’s linear regression function/ML algorithm.

public class LinReg implements Function<Double[], Double> {

private final double[] thetaVec;

LinReg (double[] thetaVec){

this.thetaVec = Arrays.copyOf(thetaVec, thetaVec.length);

}

}

public double apply(Double [] fetVec){

assert fetVec[0]==1.0;

double pred =0;

for (int j=0; j < thetaVec.length; j++){

pred+= thetaVec[j] * fetVec[j];

}

return pred;

public double[] getThet(){

return Arrays.copyOf(thetaVec, thetaVec.length);

}

}

As you may have understood, arrays can store feature vectors for simple data and ML operations. The theta vector in the above code stores the training data vectors. The vector can change as per the training data fed to the system.

Learn more about the code above right here. It is one of the simplest examples of supervised machine learning in Java.  

And, if you are interested in learning more about ML using Java, then some great books are:

• Machine Learning in Java: Helpful Techniques to Design, Build, and Deploy Powerful Machine Learning Applications in Java, and
• Mastering Java Machine Learning

That’s all the space we have for this write-up. Hope it comes in handy for anyone looking to build ML skills in Java. Study and code as much as possible, and if you need perl programming help on ML, look for a reputed online programming assignment help service.

All the best!

go to homepage