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The mention of developers and programming usually has a lot of people directing their thoughts to the wider study of computer science. Computer science is a wide area of study. In machine learning, computers learn from experience, aided by algorithms. To aid their cause, they must use data with specific features and attributes. This is how they identify patterns that we can use to help in making important decisions. In machine learning, assignments are grouped under different categories, such as predictive modeling and clustering models. The concept behind machine learning is to provide solutions to pertinent problems without necessarily waiting for direct human interaction.

Machine learning and artificial intelligence today are the reality that we dreamt of years ago. These concepts are no longer confined to fictional ideas in movies, but they have become the backbone of our daily lives. If you think about your internet activity all through the day, you interact with machine learning models all the time. How many times have you had a website translated from a foreign language to your native language? Think about the number of times you have been assisted through a chatbot, or used facial and voice recognition programs. All these are instances where we interact with machine learning models, and they help by making our lives easier.

Like any other discipline, machine learning does not exist in isolation. Many concepts in machine learning are intertwined with deep learning and artificial intelligence. There are other subjects that share similarities with machine learning, but for the purpose of this book, we will focus on deep learning and artificial intelligence.

This being the first book in a series of enlightening books about machine learning, will introduce you to the fundamental ideologies you should understand the technology, systems, and procedures used in machine learning, and how they are connected.

Artificial intelligence branches off from machine learning, but they share a lot of similarities. Tracing these two studies back in time, they share the same path for most of their history. While machine learning focuses on building models that learn through algorithms and can operate without human intervention, artificial intelligence focuses on simulating human experiences and intelligence through computing. It is safe to say that machine learning is a subclass of artificial intelligence because we work towards building machines that can simulate human decision-making processes, albeit by learning through data.

Deep learning introduces us to another division of machine learning where artificial neural networks (ANN) are employed in making important decisions. In deep learning, the neural networks use layered structures whose functions are similar to the functions of a healthy human brain. Therefore, machine learning, deep learning, and artificial intelligence are three disciplines that are interconnected in more ways than one. When you commit to learning one of them, you will inadvertently have to learn about the others too at some point.

In machine learning, deep learning is a category that focuses on using algorithms to empower systems and build models that are similar in operation to the human brain. The present excitement and hype around deep learning comes from the fundamental studies in neural networks. Research in neural networks has been carried out for many years, and could date back longer than the history of machine learning. This is because part of this knowledge is embedded in neurological studies without an iota of reference to machine learning or computing.

There have been major strides in machine learning research over the years, especially with respect to deep learning. While we must recognize the scalability of these disciplines, the advancement in these technologies is made possible by three important factors; the development of efficient algorithms, the increasing and matching demand for significant computing resources, and the increase in the internet population, hence massive chunks of data are available to train and empower these machines.

So how do we find the link between deep learning and machine learning? The answer lies in how these models operate. From a basic perspective, you work with models which receive predefined input and output data. Input data could be anything from text instructions, to numerical input, or audio, video, and images in different media formats. Based on the input, the specific model you use will then derive an output that meets your instructions. Output could be anything from identifying an individuals name to defining their tribe. The correct answer depends on the kind of input data you provide the machine learning model.