#INTRODUCTION
Analytics is a collection of techniques and tools used for creating value from data. Techniques include
concepts such as artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms.
AI, ML, and DL are defined as follows:
- Artificial Intelligence: Algorithms and systems that exhibit human-like intelligence.
- Machine Learning: Subset of AI that can learn to perform a task with extracted data and/or models.
- Deep Learning: Subset of machine learning that imitate the functioning of human brain to solve
problems.
The relationship between AI, ML, and DL can be visualized.
The relationship between AI, ML, and DL is not accepted by all. There is another
school of thought that believes that AI and ML are different (ML is not a subset of AI) with some overlap.
The important point is that all of them are algorithms, which are nothing but set of instructions used for
solving business and social problems.
Machine learning is a set of algorithms that have the capability to learn to perform tasks such as prediction and classification effectively using data. Learning is achieved using additional data and/or additional models. An algorithm can be called a learning algorithm when it improves on a performance metric while performing a task, for example, accuracy of classification such as fraud, customer churn, and so on. The focus of this book is machine learning algorithms. In the next few sections we discuss various machine learning algorithms and how to solve a problem using ML algorithms.