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I probably don't need to tell you that machine learning has become one of the most
exciting technologies of our time and age. Big companies, such as Google, Facebook,
Apple, Amazon, IBM, and many more, heavily invest in machine learning research
and applications for good reasons. Although it may seem that machine learning has
become the buzzword of our time and age, it is certainly not a hype. This exciting
field opens the way to new possibilities and has become indispensable to our daily
lives. Talking to the voice assistant on our smart phones, recommending the right
product for our customers, stopping credit card fraud, filtering out spam from our
e-mail inboxes, detecting and diagnosing medical diseases, the list goes on and on.
If you want to become a machine learning practitioner, a better problem solver, or
maybe even consider a career in machine learning research, then this book is for you!
However, for a novice, the theoretical concepts behind machine learning can be quite
overwhelming. Yet, many practical books that have been published in recent years
will help you get started in machine learning by implementing powerful learning
algorithms. In my opinion, the use of practical code examples serve an important
purpose. They illustrate the concepts by putting the learned material directly into
action. However, remember that with great power comes great responsibility! The
concepts behind machine learning are too beautiful and important to be hidden in
a black box. Thus, my personal mission is to provide you with a different book; a
book that discusses the necessary details regarding machine learning concepts, offers
intuitive yet informative explanations on how machine learning algorithms work,
how to use them, and most importantly, how to avoid the most common pitfalls.
If you type "machine learning" as a search term in Google Scholar, it returns an
overwhelmingly large number-1,800,000 publications. Of course, we cannot discuss
all the nitty-gritty details about all the different algorithms and applications that have
emerged in the last 60 years. However, in this book, we will embark on an exciting
journey that covers all the essential topics and concepts to give you a head start in this
field. If you find that your thirst for knowledge is not satisfied, there are many useful
resources that can be used to follow up on the essential breakthroughs in this field.
[ vii ]
Preface
If you have already studied machine learning theory in detail, this book will show
you how to put your knowledge into practice. If you have used machine learning
techniques before and want to gain more insight into how machine learning really
works, this book is for you! Don't worry if you are completely new to the machine
learning field; you have even more reason to be excited. I promise you that machine
learning will change the way you think about the problems you want to solve and
will show you how to tackle them by unlocking the power of data.
Before we dive deeper into the machine learning field, let me answer your most
important question, "why Python?" The answer is simple: it is powerful yet very
accessible. Python has become the most popular programming language for data
science because it allows us to forget about the tedious parts of programming and
offers us an environment where we can quickly jot down our ideas and put concepts
directly into action.
Reflecting on my personal journey, I can truly say that the study of machine learning
made me a better scientist, thinker, and problem solver. In this book, I want to
share this knowledge with you. Knowledge is gained by learning, the key is our
enthusiasm, and the true mastery of skills can only be achieved by practice. The road
ahead may be bumpy on occasions, and some topics may be more challenging than
others, but I hope that you will embrace this opportunity and focus on the reward.
Remember that we are on this journey together, and throughout this book, we will
add many powerful techniques to your arsenal that will help us solve even the
toughest problems the data-driven way.
What this book covers
Chapter 1, Giving Computers the Ability to Learn from Data, introduces you to the
main subareas of machine learning to tackle various problem tasks. In addition, it
discusses the essential steps for creating a typical machine learning model building
pipeline that will guide us through the following chapters.
Chapter 2, Training Machine Learning Algorithms for Classification, goes back to
the origin of machine learning and introduces binary perceptron classifiers and
adaptive linear neurons. This chapter is a gentle introduction to the fundamentals
of pattern classification and focuses on the interplay of optimization algorithms and
machine learning.
Chapter 3, A Tour of Machine Learning Classifirs Using Scikit-learn, describes the
essential machine learning algorithms for classification and provides practical
examples using one of the most popular and comprehensive open source machine
learning libraries, scikit-learn.
[ viii ]
Preface
Chapter 4, Building Good Training Sets – Data Preprocessing, discusses how to deal with
the most common problems in unprocessed datasets, such as missing data. It also
discusses several approaches to identify the most informative features in datasets
and teaches you how to prepare variables of different types as proper inputs for
machine learning algorithms.
Chapter 5, Compressing Data via Dimensionality Reduction, describes the essential
techniques to reduce the number of features in a dataset to smaller sets while
retaining most of their useful and discriminatory information. It discusses the
standard approach to dimensionality reduction via principal component analysis
and compares it to supervised and nonlinear transformation techniques.
Chapter 6, Learning Best Practices for Model Evaluation and Hyperparameter Tuning,
discusses the do's and don'ts for estimating the performances of predictive models.
Moreover, it discusses different metrics for measuring the performance of our
models and techniques to fine-tune machine learning algorithms.
Chapter 7, Combining Different Models for Ensemble Learning, introduces you to the
different concepts of combining multiple learning algorithms effectively. It teaches
you how to build ensembles of experts to overcome the weaknesses of individual
learners, resulting in more accurate and reliable predictions.
Chapter 8, Applying Machine Learning to Sentiment Analysis, discusses the essential
steps to transform textual data into meaningful representations for machine learning
algorithms to predict the opinions of people based on their writing.
Chapter 9, Embedding a Machine Learning Model into a Web Application, continues with
the predictive model from the previous chapter and walks you through the essential
steps of developing web applications with embedded machine learning models.
Chapter 10, Predicting Continuous Target Variables with Regression Analysis, discusses
the essential techniques for modeling linear relationships between target and
response variables to make predictions on a continuous scale. After introducing
different linear models, it also talks about polynomial regression and
tree-based approaches.
Chapter 11, Working with Unlabeled Data – Clustering Analysis, shifts the focus to a
different subarea of machine learning, unsupervised learning. We apply algorithms
from three fundamental families of clustering algorithms to find groups of objects
that share a certain degree of similarity
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