Machine Learning
2 Books in 1:
Python Machine Learning and
Data Science.
The Complete Guide for Beginners to Master Neural Networks, Artificial Intelligence, and Data Science with Python
Andrew Park
Python Machine Learning
A Complete Guide for Beginners on Machine Learning and Deep Learning With Python
Table of Contents
Introduction
For all that we know about Machine Learning, the truth is that we are nowhere close to realizing the true potential of these studies. Machine Learning is currently one of the hottest topics in computer science. If you are a data analyst, this is a field you should focus all your energy on because the prospects are incredible. You are looking at a future where interaction with machines will form the base of our being.
In this installation, our purpose was to address Python Machine Learning from the perspective of an expert. The assumption is that you have gone through the earlier books in the series that introduced you to Machine Learning, Python, libraries, and other important features that form the foundation of your knowledge in Machine Learning. With this in mind, we barely touched on the introductory concepts, unless necessary.
Even at an expert level, it is always important to remind yourself of the important issues that we must look at in Machine Learning. Algorithms are the backbone of almost everything that you will do in Machine Learning. Because of this reason, we introduced a brief section where you can remind yourself of the important algorithms and other elements that help you progress your knowledge of Machine Learning.
Machine Learning is as much about programming as it is about probability and statistics. There are many statistical approaches that we will use in Machine Learning to help us arrive at optimal solutions from time to time. It is therefore important that you remind yourself about some of the necessary probability theories and how they affect outcomes in each scenario.
In our studies of Machine Learning from the beginner books through an intermediary level to this point, one concept that stands out is that Machine Learning involves uncertainty. This is one of the differences between Machine Learning and programming. In programming, you write code that must be executed as it is written. The code derives a predetermined output based on the instructions given. However, in Machine Learning, this is not a luxury we enjoy.
Once you build the model, you train and test it and eventually deploy the model. Since these models are built to interact with humans, you can expect variances in the type of interaction that you experience at every level. Some input parameters might be correct, while others might not. When you build your model, you must consider these factors, or your model will cease to perform as expected.
The math element of Machine Learning is another area of study that we have to look at. We didnt touch on this so much in the earlier books in the series because it is an advanced level study. Many mathematical computations are involved in Machine Learning for the models to deliver the output we need. To support this cause, we must learn how to perform specific operations on data based on unique instructions.
As you work with different sets of data, there is always the possibility that you will come across massive datasets. This is normal because as our Machine Learning models interact with different users, they keep learning and build their knowledge. The challenge of using massive datasets is that you must learn how to break down the data into small units that your system can handle and process without any challenges. In this case, you are trying to avoid overworking your learning model.
Most basic computers will crash when they have to handle massive data. However, this should not be a problem when you learn how to fragment your datasets and perform computational operations on them.
At the beginning of this book, we mentioned that we will introduce hands-on approaches to using Machine Learning in daily applications. In light of this assertion, we looked at some practical methods of using Machine Learning, such as building a spam filter and analyzing a movie database.
We have taken a careful step-by-step approach to ensure that you can learn along the way, and more importantly, tried to explain each process to help you understand the operations you perform and why.
Eventually, when you build a Machine Learning model, the aim is to integrate it into some of the applications that people use daily. With this in mind, you must learn how to build a simple solution that addresses this challenge. We used simple explanations to help you understand this, and hopefully, as you keep working on different Machine Learning models, you can learn by building more complex models as your needs permit.
There are many concepts in Machine Learning that you will learn or come across over time. You must reckon the fact that this is a never-ending learning process as long as your model interacts with the data. Over time, you will encounter greater datasets than those you are used to working with. In such a scenario, learning how to handle them will help you achieve your results faster, and without struggling.
Chapter 1 What Is Machine Learning?
We live in a world where technology has become an inalienable part of our daily lives. In fact, with all the rapid changes in technology these days, machines enabled with artificial intelligence are now responsible for different tasks like prediction, recognition, diagnosis, and so on.
Data is added or fed to the machines and these machines learn from these data. These data are referred to as training data because they are used to train the machines.
Once the machines have the data, they start to analyze any patterns present within the data and then perform actions based on these patterns. Machines use various learning mechanisms for analyzing the data according to the actions that they need to perform. These mechanisms can be broadly classified into two categories- supervised learning and unsupervised learning.
You might wonder why there arent any machines designed solely to perform those tasks that they are needed to carry out. There are different reasons why Machine Learning is important. As already mentioned, all research conducted about Machine Learning comes in handy since it helps us understand a couple of aspects of human learning. Also, Machine Learning is quintessential because it helps increase the accuracy, effectiveness, and efficiency of machines.
Here is a real-life example that will help you understand this concept better.
Let us assume that there are two random users A and B who love listening to music and we have access to their history of songs. If you were a music company, then you can use Machine Learning to understand the kind of songs each of these users prefers and thereby you can come up with different ways in which you can sell your products to them.
For instance, you have access to noting down the different attributes of songs like their tempo, frequency, or the gender of the voice, and then use all these attributes and plot a graph. Once you plot a graph, over time, it will become evident that A tends to prefer to listen to songs that have a fast tempo and are sung by male artists, whereas B likes to listen to slow songs sung by female artists, or any other similar insight. Once you obtain these data, you can transfer them to your marketing and advertising teams to make better product decisions.
At present, we have free access to all the historical data that have been collected since the advent of technology. Not only we have access to these data, but we can now store and process such large quantities of them. Technology has certainly evolved, and it has come a long way when you look at the way we can now handle such operations. The technology is so refined these days that it provides access to more data to mine from.
