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When we talk of Machine Learning or Artificial Intelligence, we spontaneously think of Python or R as a programming language for the subsequent implementation. However, what most of the people don’t know is that Java can also be used for the same purpose. In this article, we would uncover Machine learning in Java and the various libraries to implement it.
Below topics are covered in this tutorial:
Machine learning is flourishing at an exponential rate. From its numerous applications such as google maps, self-driving cars, google translate to fraud detection, it is everywhere. But do you know what exactly is machine learning or how is it implemented?
Let me simplify this concept. Machine learning is a powerful technique which learns from examples and experience. It is a type of Artificial Intelligence that allows software applications to learn from the data and become more accurate in predicting outcomes, without human intervention or without being explicitly programmed. So instead of you writing the entire code, you just have to feed the data and the algorithm will build the logic based on your data. Because of its high demand, an ML Engineer can expect a salary of ₹719,646 (IND) or $111,490 (US).
Coming to the second question, how is it implemented?
Machine Learning algorithm is an evolution of the regular algorithm. It makes your programs “smarter”, by allowing them to automatically learn from the provided data. The algorithm is mainly divided into two phases: Training and Testing.
Now when it comes to algorithms, it is categorized into three types:
Supervised Learning: This is a training process, where you can consider learning guided by a teacher. This is a process of an algorithm learning from the training dataset. It generates a mapping function between an input variable and output variable. Once the model is trained, it can start making predictions/ decisions when new data is given to it. Few algorithms that fall into supervised learning are – Linear regression, logistic regression, decision tree, etc.
Unsupervised Learning: This is a process where a model is trained using a piece of information which is not labeled. This process can be used to cluster the input data in classes on the basis of their statistical properties. It is commonly called a clustering analysis which means the grouping of objects based on the information found in the data, describing the objects or their relationship. Here, the goal is that objects in one group should be similar to each other but different from the objects in another group. Few algorithms that fall into unsupervised learning include K-means clustering, Hierarchical clustering, etc.
Reinforcement Learning: Reinforcement learning follows the concept of hit and trial. It is learning by interacting with space or an environment. An RL agent learns from the consequences of its actions, rather than from being taught explicitly. It is the ability of an agent to interact with the environment and find out what is the best outcome.
Next, let’s move ahead and understand how Machine Learning is used in Java.
In the world of programming, Java is one of the oldest and reliable programming languages. Due to its high popularity, demand, and ease of use, there are more than nine million developers across the globe using Java. When it comes to Machine learning, you might be thinking other programming languages such as Python, R, etc, but let me tell you that java is not far behind. Java is not a leading programming language in this domain but with the help of third-party open source libraries, any java developer can implement Machine Learning and get into Data Science.
Let me list down some more advantages of using Java programming language-
Moving ahead, let us see the most popular libraries used for Machine Learning in Java.
To implement Machine learning, there are various open-source third-party libraries available in Java. The most common ones are listed below:
1. ADAMS: It stands for Advanced Data Mining and Machine Learning Systems. It is a flexible workflow engine which aims to build quick and maintain data-driven, perform retrieval, processing, mining and visualization of data. ADAMS uses a tree -like structure and follows a philosphy of less is “more”. It provides some features such as:
2. JavaML: It is a collection of machine learning algorithms where it has a common interface for each type of algorithm. It has well good documentation with clear interfaces. You can also gather plenty of codes and tutorials aimed for software engineers or programmers. Some of its features are:
3. Mahaut: Apache Mahaut is a distributed framework which provides implementations of machine algorithms for the Apache Hadoop platform. It consists of various components for easy use and aimed at mathematicians, statisticians, data analysts, data scientist or anyone from the analytic professional. It is majorly focussed on:
4. Deeplearning4j: Deeplearning4j, as the name suggests us written in Java and is compatible with Java Virtual Machine language, such as Kotlin, Scala etc. It is an open-source distributed deep learning library which has an advantage of the latest distributed computing frameworks such as Apache Spark and Hadoop. Some of its features are:
5. WEKA: Weka is a free, easy and open-source machine learning library for Java. Its name is inspired by a flightless bird found on the islands of New Zealand. Weka is a collection of ML algorithms and it also supports deep learning. It is majorly focused on:
This brings us to the end of this article where we have discussed Machine learning in Java and how to implement it. Hope you are clear with all that has been shared with you in this tutorial.
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