Machine Learning with Python_ Comprehensive Beginner’s Guide to Machine Learning in Python with Exercises and Case Studies.pdf
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Machine Learning with Python
Comprehensive Beginner’s Guide to
Machine Learning in Python with
Exercises and Case Studies
© Copyright 2018 - All rights reserved.
The content contained within this book may not be reproduced, duplicated or
transmitted without direct written permission from the author or the
publisher.
Under no circumstances will any blame or legal responsibility be held against
the publisher, or author, for any damages, reparation, or monetary loss due to
the information contained within this book. Either directly or indirectly.
Legal Notice:
This book is copyright protected. This book is only for personal use. You
cannot amend, distribute, sell, use, quote or paraphrase any part, or the
content within this book, without the consent of the author or publisher.
Disclaimer Notice:
Please note the information contained within this document is for educational
and entertainment purposes only. All effort has been executed to present
accurate, up to date, and reliable, complete information. No warranties of any
kind are declared or implied. Readers acknowledge that the author is not
engaging in the rendering of legal, financial, medical or professional advice.
The content within this book has been derived from various sources. Please
consult a licensed professional before attempting any techniques outlined in
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By reading this document, the reader agrees that under no circumstances are
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including, but not limited to, —errors, omissions, or inaccuracies.
Table Of Contents
Introduction
Chapter One: What is Machine Learning?
Applications of Machine Learning
Virtual Personal Assistants
Density Estimation
Latent Variables
Reduction of Dimensionality
Advantages and Disadvantages of Machine Learning
Chapter Two: Machine Learning - Concepts & Terms
Machine Learning, Artificial Intelligence and Deep Learning
Objectives of Machine Learning System
Predict a quantity
Anomaly Detector Systems
Clustering Systems
Category of Machine Learning systems
Supervised Learning
Unsupervised Learning
Reinforced learning
Steps in building a Machine learning System
Define Objective
Collect Data
Prepare Data
Select Algorithm
Train Model
Test Model
Predict
Deploy
Chapter Three: Why Use Python for Machine Learning
Simple and Easy to Learn
High-level Language
Open Source
Portable
Interpreted
Object Oriented
Fast and Efficient to Use
Batteries Included
Numpy, Panda and SciKit Learn
Chapter Four: Basics of Python Programming
Running Python
Installing on Windows
Choosing the Right Version
Getting Started
Creating your own files
Choosing the right identifier
Python Keywords
Understanding the naming convention
Creating and Assigning Values to Variables
Recognizing different types of Variables
Working with Dynamic Typing
The None Variable
Using Quotes
Nesting Quotes
How to use Whitespace Characters
How to Create a Text Application
Working with Numbers
Computers only count to one
Using Boolean
Using Whole Numbers
Performing Basic Mathematical Operations
Working with Floats and Fractions
Converting Data Types
Loops
While Statement
Nesting Loops
Items in sequences
Tuples
Creating a Tuple
Changing Values in a Tuple
List
Creating a List
Modifying a List
Stacks and Queues
Dictionaries
Install Packages required for Machine Learning Applications
Open a terminal and Run the following commands
Chapter Five: Data Scrubbing and Preparation
Noisy Data
Missing Data
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