Machine Learning for Novices

By Gabrielle Curtis

In an era dominated by technological advancements, understanding the fundamentals of machine learning has become increasingly crucial. This comprehensive guide is tailored for novices, offering a clear and accessible introduction to the world of data science and artificial intelligence.

This book covers:

Basic concepts and terminology of machine learning, eliminating jargon and simplifying complex ideas

Step-by-step guidance on setting up and running simple machine learning models without prior programming knowledge

Real-world case studies that showcase the transformative impact of machine learning in various industries

Practical tips for implementing machine learning in everyday life, from optimizing personal tasks to making informed decisions

A roadmap for further exploration, enabling readers to delve deeper into advanced topics as their understanding grows

And Much More!...

Whether you're a curious beginner or a professional in a different field seeking to expand your knowledge, "Machine Learning for Novices" is the perfect starting point for unlocking the potential of this revolutionary technology.

• Language: English
• Publisher: Gabrielle Curtis
• Release date: Mar 22, 2024
• ISBN: 9798224264445

Book preview

Introduction

Since ancient times, if not longer, machines have played a significant role in human life. Although robots’ complexity has changed, they have always served to expand the human mind, automate laborious activities, and free up humans to accomplish other things.

Of course, not everyone has been happy with this. When textile workers’ employment was automated by machines in the 18th century, riots broke out. The Luddites, a group renowned for destroying numerous industrial machinery, feared losing their capacity to find jobs as a result of being outcompeted by the machines. Ironically, those industries had twenty to thirty times more employees 50 years later. The enhanced productivity that the machines unleashed led to the development of new jobs. Sadly, despite the obvious lessons learned from that event, modern-day Luddites continue to be concerned that robots and artificial intelligence would eliminate all employment in their community.

There will undoubtedly be difficulties since there always are. As more advanced machines that employ statistical algorithms to learn and perform better are put into use, workers will need more thorough training than they did in previous decades.

One of the most fascinating and active fields of science and engineering research right now is machine learning. We will introduce you to the field of machine learning in this book, starting with a review of how technology has advanced alongside humankind throughout history.

We will also go into great detail regarding the rapidly expanding discipline of data science. Data science is one of the hottest job choices right now, and it’s employed everywhere from Wall Street to the Pentagon to private companies like Amazon and Facebook. But machine learning will be this book’s primary subject of interest.

Next, we’ll look at how artificial intelligence, machine learning, and deep learning differ from one another while still being distinct subjects and ideas in their own right. All three of these topics have the potential to interact with one another.

After that, we’ll talk about the procedures involved in machine learning, such as data collection, data management, analysis, training and testing of the algorithms, and deployment. After discussing the fundamental categories of machine learning, we’ll go on to discuss algorithms.

Chapter 1: The Evolution of Machines and What Is Machine Learning?

You will learn about the idea of machine learning in this chapter, as well as how it fits into the larger field of computer science and artificial intelligence.

A Brief History of Computer Science In the beginning, in order for a computer to complete a task, precise, step-by-step instructions had to be supplied to it. These instructions were initially entered into the computer using punch cards that could be read by a computer system. The binary is the language used by computers, where 1 denotes yes and 0 denotes no. Complete streams of logic can be constructed, and anything can be stored or represented using only binary. That covers everything from the financial market to the pixels that make up an image to your basic details like name, age, and social security number.

An algorithm is a step-by-step process that computers employ to carry out a specific task. Algorithms were therefore written in binary language in the early days of computers.

Early programming techniques were incredibly laborious, but computers quickly proved to be of great use. During the Second World War, they were employed to decipher secret codes and determine the trajectories of projectile weapons like artillery. Compared to humans, who are intelligent but incredibly slow thinkers, computers are much superior at carrying out such monotonous jobs. Computers gained widespread use and made their first foray into the commercial sphere after the war.

If programmers had been required to continuously develop algorithms in binary, the usage of computers would have remained very constrained. Although binary is excellent for computers, it is challenging for people to think in binary. Even students of engineering and computer science must train their minds to think in binary, therefore higher-level languages were created to make things simpler for everyone.

After the war, there were two tiers of higher-level languages employed in the early years of computer science. Assembly language is the initial level. If you can call it that, this lower-level language is still using the kind of cognitive process that a machine would employ. Using assembly language to create complex algorithms can be tricky for many individuals and is difficult to understand. A computer may need to be instructed to shift a piece of data from one spot in memory to another or to perform the separate steps necessary to multiply two numbers together. Just above binary is assembly language.

But programmers also started to create high-level languages.

These are languages that employ logical patterns and structures together with more logically crafted instructions. However, programming in a high-level language is still a very difficult endeavor and is very helpful. This explains why those who have degrees in computer science are paid well.

Many of the high-level languages used today are presumably ones you are already acquainted with. FORTRAN reigned supreme among high-level programming languages in the 1950s, and it is currently employed in a variety of scientific contexts, including the simulation of nuclear explosions.

It is utilized in systems like that for heritage reasons in part, but also because it is a very powerful language for calculations.

As the 20th century progressed, new and more widely used computer languages were created. These included C, C++, Ada, Pascal, and others. The idea of object-oriented programming was first presented in the language C++, which was an extension of C. The idea of an algorithm as a series of instructions is elevated to a higher plane by object-oriented languages, which allow programmers to create objects inside their programs rather than just developing algorithms. These objects can be acted on or go through events as well.

Later, a large number of new languages that were created especially for usage in particular circumstances proliferated after the development of the internet and smartphones. For instance, Objective-C, a C extension used to program iPhones, was developed. Later, Swift took its position. Among many others that are too many to list, Javascript and Java have become increasingly popular on the internet and on Android smartphones.

The Evolution of Machines Because humans are natural problem-solvers, machines are simply an extension of our mental abilities. Humans put their minds together to devise a plan and find a means to complete tasks that appear to be insurmountable. Humans have always acted in this manner. People may have needed to figure out how to cross a river long ago. Then, in 1969, people collaborated to send two men to the moon.

The initial tools used by humans were only additions to our hands and limbs that served as replacements for the large canine teeth and claws we lacked.

These implements comprised stone spear tips, scrapers, and cutters. Despite having rather straightforward goals, they were quite groundbreaking. These were the initial