In recent times, machine learning (ML) and data science have gained popularity like never before. This field is expected to grow exponentially in the coming years. First of all, what is machine learning? And why does someone need to take pains to understand the principles? Well, we have the answers for you. One simple example could be book recommendations in e-commerce websites when someone went to search for a particular book or any other product recommendations which were bought together to provide an idea to users which they might like. Sounds magic, right? In fact, utilizing machine learning, can achieve much more than this.
Machine learning is a branch of study in which a model can learn automatically from the experiences based on data without exclusively being modeled like in statistical models. Over a period and with more data, model predictions will become better.
In this first chapter, we will introduce the basic concepts which are necessary to understand both the statistical and machine learning terminology necessary to create a foundation for understanding the similarity between both the streams, who are either full-time statisticians or software engineers who do the implementation of machine learning but would like to understand the statistical workings behind the ML methods. We will quickly cover the fundamentals necessary for understanding the building blocks of models.
In this chapter, we will cover the following:
- Statistical terminology for model building and validation
- Machine learning terminology for model building and validation
- Machine learning model overview
