Machine Learning: 2 Books in 1: Machine Learning for Beginners, Machine Learning Mathematics. An Introduction Guide to Understand Data Science Through the Business Application

Study Deep Learning through Data
Science: How to Build Artificial
Intelligence through Concepts of Statistics,
Algorithms, Analysis, and Data Mining

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TABLE OF CONTENTS
Introduction
Chapter 1: Introduction to Machine Learning
Chapter 2: Machine Learning Algorithms
Chapter 3: Neural Network Learning Models
Chapter 4: Learning Through Uniform Convergence
Chapter 5: Data Science Lifecycle And Technologies
Conclusion

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Introduction
Congratulations on purchasing Machine Learning Mathematics: Study
Deep Learning through Data Science. How to Build Artificial Intelligence
Through Concepts Of Statistics, Algorithms, Analysis, and Data Mining and
thank you for doing so.
The following chapters will discuss the fundamental concepts of machine
learning algorithms and the need for machine learning in resolving modern-
day business problems. You will find a detailed explanation of the four
different types of machine learning algorithms available in the market today
along with the importance of machine learning in the first chapter of this
book. Representation, evaluation, and optimization make up the three core
concepts of machine learning that are explained in detail. You will be
introduced to the concept of “Statistical Learning”, which is a descriptive
statistics-based machine learning framework that can be categorized as
supervised or unsupervised.
In chapter 2 of this book titled "Machine Learning Algorithms", you will
learn development and application of some of the most popular supervised
machine learning algorithms, with explicit details on linear regression,
logistic regression, and Naïve Bayes classification algorithms. In chapter 3
titled "Neural Network Learning Models", it will provide you an
overarching guide for everything you need to know for successful
development of neural network models by learning how to build data

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pipelines for your machine learning models and then following specific
neural network training approaches. The end-to-end process described in
this chapter will provide you an overarching view on how to generate your
desired machine learning model from scratch with a focus on neural
network models. You will also learn the various components and functions
at play in the Artificial Neural Network and Perceptron (single neuron-
based network) models as well as various applications of these advance and
futuristic machine learning models to resolve everyday business problems.
In the 4th chapter of this book titled, "Learning Through Uniform
Convergence", we will take a deep dive into the overlap of machine
learning with the field of statistics. One of many borrowed statistical
concepts used in the development of machine learning models is "Uniform
Convergence", which allows the developer to identify the learnability of the
problem at hand based on the data sample size using empirical risk
minimizers. You will gain a thorough understanding of the concept of
"General Setting of Learning" introduced by Vapnik in 1995 and continues
to be central to the concept of machine learning development. A statistical
explanation of the impact of “Uniform Convergence” on learnability as a
prerequisite using finite classes is provided, along with a discussion on
potential learnability without “Uniform Convergence”.
The final chapter of this book will provide you a holistic overview of
various cutting edge data science technologies like data mining and
Artificial Intelligence. The most highly recommended lifecycle for
structured data science projects is the "Team Data Science Process" (TDSP)
is explained in exquisite detail along with various deliverables that need to
be generated at every stage. You will also learn how data science is being
leveraged by businesses in their decision-making process. The power of

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artificial intelligence has already started to manifest in our environment and
our everyday objects. So you need to learn the difference between Business
Intelligence and Data Science technology. This book is filled with real-life
examples to help you understand the nitty-gritty of the concepts and names
and description of multiple tools that you can further explore and
selectively implement in your business to reap the benefits of these cutting-
edge technologies.
There are plenty of books on this subject on the market, thanks again for
choosing this one! Every effort was made to ensure it is full of as much
useful information as possible, please enjoy!

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