Machine learning has already demonstrated impressive successes despite being a relatively young field. With the ubiquity of Java resources, Java's platform independence, and the selection of ML frameworks in Java, superior skill in machine learning using Java is a highly desirable asset in the market today.
Machine learning has been around in some form—if only in the imagination of thinkers, in the beginning—for a long time. More recent developments, however, have had a radical impact in many spheres of our everyday lives. Machine learning has much in common with statistics, artificial intelligence, and several other related areas. Whereas some data management, business intelligence, and knowledge representation systems may also be related in the central role of data in each of them, they are not commonly associated with principles of learning from data as embodied in the field of machine learning.
Any discourse on machine learning would assume an understanding of what data is and what data types we are concerned with. Are they categorical, continuous, or ordinal? What are the data features? What is the target, and which ones are predictors? What kinds of sampling methods can be used—uniform random, stratified random, cluster, or systematic sampling? What is the model? We saw an example dataset for weather data that included categorical and continuous features in the ARFF format.
The types of machine learning include supervised learning, the most common when labeled data is available, unsupervised when it's not, and semi-supervised when we have a mix of both. The chapters that follow will go into detail on these, as well as graph mining, probabilistic graph modeling, deep learning, stream learning, and learning with Big Data.
Data comes in many forms: structured, unstructured, transactional, sequential, and graphs. We will use data of different structures in the exercises to follow later in this book.
The list of domains and the different kinds of machine learning applications keeps growing. This review presents the most active areas and applications.
Understanding and dealing effectively with practical issues, such as noisy data, skewed datasets, overfitting, data volumes, and the curse of dimensionality, is the key to successful projects—it's what makes each project unique in its challenges.
Analytics with machine learning is a collaborative endeavor with multiple roles and well-defined processes. For consistent and reproducible results, adopting the enhanced CRISP methodology outlined here is critical—from understanding the business problem to data quality analysis, modeling and model evaluation, and finally to model performance monitoring.
Practitioners of data science are blessed with a rich and growing catalog of datasets available to the public and an increasing set of ML frameworks and tools in Java as well as other languages. In the following chapters, you will be introduced to several datasets, APIs, and frameworks, along with advanced concepts and techniques to equip you with all you will need to attain mastery in machine learning.
Ready? Onward then!