• Book Name: The Fiverr Business Plan
• Author: George Ilian
• Pages: 71
• Size: 2 MB

Fiverr Business Plan by George Ilian

The machine learning refers to the automated detection of meaningful patterns in data. In the past couple of decades it has become a common tool in almost any task that requires information extraction from large data sets. We are surrounded by a machine learning based technology: Search engines learn how to bring us the best results (while placing profitable ads), antispam software learns to filter our email messages, and credit card transactions are secured by a software that learns how to detect frauds. Digital cameras learn to detect faces and intelligent personal assistance applications on smart-phones learn to recognize voice commands. Cars are equipped with accident prevention systems that are built using machine learning algorithms. Machine learning is also widely used in scientific applications such as bioinformatics, medicine, and astronomy. One common feature of all of these applications is that, in contrast to more traditional uses of computers, in these cases, due to the complexity of the patterns that need to be detected, a human programmer cannot provide an explicit, fine-detailed specification of how such tasks should be executed.

Fiverr Business Plan by George Ilian

Taking example from intelligent beings, many of our skills are acquired or refined through learning from our experience (rather than following explicit instructions given to us).Machine learning tools are concerned with endowing programs with the ability to “learn” and adapt. The first goal of this book is to provide a rigorous, yet easy to follow, introduction to the main concepts underlying machine learning: What is learning? How can a machine learn? How do we quantify the resources needed to learn a given concept? Is learning always possible? Can we know whether the learning process succeeded or failed? The second goal of this book is to present several key machine learning algorithms. We chose to present algorithms that on one hand are successfully used in practice and on the other hand give a wide spectrum of different learning techniques. Additionally, we pay specific attention to algorithms appropriate for large scale learning (a.k.a. “Big Data”), since in recent years, our world has become increasingly “digitized” and the amount of data available for learning is dramatically increasing. As a result, in many applications data is plentiful and computation time is the main bottleneck. We therefore explicitly quantify both the amount of data and the amount of computation time needed to learn a given concept. The book is divided into four parts. The first part aims at giving an initial rigorous answer to the fundamental questions of learning. We describe a generalization of Valiant’s Probably Approximately Correct (PAC) learning model, which is a first solid answer to the question “What is learning?” We describe the Empirical Risk Minimization (ERM), Structural Risk Minimization (SRM), and Minimum Description Length (MDL) learning rules, which show “how a machine can learn.” We quantify the amount of data needed for learning using the ERM, SRM, and MDL rules and show how learning might fail by deriving a “no-free-lunch” theorem. We also discuss how much computation time is required for learning. In the second part of the book we describe various learning algorithms. For some of the algorithms, we first present a more general learning principle, and then show how the algorithm follows the principle. While the first two parts of the book focus on the PAC model, the third part extends the scope by presenting a wider variety of learning models. Finally, the last part of the book is devoted to advanced theory. We made an attempt to keep the book as self-contained as possible. However, the reader is assumed to be comfortable with basic notions of probability, linear algebra, analysis, and algorithms. The first three parts of the book are intended for first year graduate students in computer science, engineering, mathematics, or statistics. It can also be accessible to undergraduate students with the adequate background. The more advanced chapters can be used by researchers intending to gather a deeper theoretical understanding.

First, let me tell you a little bit about myself. I am a TOP RATED Seller and, over the course of the last year, I have completed over 2500 orders, earning more than $25,000. With this book, I will show you how to do the same and develop your Fiverr success too! So let’s roll and learn a few things about this insanely cool website, shall we?

What You Will Learn From This Book

When you are finished reading this book, you will know the following:
• What Fiverr.com is
• All the ways you can benefit from using their website
• The different types of buyers you will meet on Fiverr.com and how to deal with them • How the Fiverr market works and how to make it work for you
• What you can offer and how much cash it can realistically bring you
• How to increase your income using ANY skill you have
• How to build a best-selling, high demand gig
• How to market your talents so you get constant work, raising your profits!
• How to stay consistent so that you boost your Fiverr reputation
• How to make more $$$$ than you currently make
• Tips and tricks as to how to improve your Fiverr experience
• How to build a great business plan that can bring $10,000+ a month!

In addition to learning all of this, a special BONUS is waiting for you at the end of the book that will help you make even more money from home! I want to see you succeed, so I hope you use it! As I said in the beginning, this is going to be a step-by-step journey that transforms you from a complete newbie to a Fiverr money-making monster! If financial freedom and doing what you love is what you dream about, then this book is for you! Now, before we move on, let me tell you why I wrote this book for you in the first place.

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