Nedal Daniel - Introduction to Machine Learning with Python: A Guide for Beginners in Data Science
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- Book:Introduction to Machine Learning with Python: A Guide for Beginners in Data Science
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From AI Sciences Publisher Our books may be the best one for beginners; its a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses. To get the most out of the concepts that would be covered, readers are advised to adopt a hands on approach which would lead to better mental representations.
Step By Step Guide and Visual Illustrations and Examples This book and the accompanying examples, you would be well suited to tackle problems which pique your interests using machine learning. Instead of tough math formulas, this book contains several graphs and images which detail all important Machine Learning concepts and their applications.
Target Users The book designed for a variety of target audiences. The most suitable users would include:
Anyone who is intrigued by how algorithms arrive at predictions but has no previous knowledge of the field.
Software developers and engineers with a strong programming background but seeking to break into the field of machine learning.
Seasoned professionals in the field of artificial intelligence and machine learning who desire a birds eye view of current techniques and approaches.
Whats Inside This Book?
Supervised Learning Algorithms
Unsupervised Learning Algorithms
Semi-supervised Learning Algorithms
Reinforcement Learning Algorithms
Overfitting and underfitting
correctness
The Bias-Variance Trade-off
Feature Extraction and Selection
A Regression Example: Predicting Boston Housing Prices
Import Libraries:
How to forecast and Predict
Popular Classification Algorithms
Introduction to K Nearest Neighbors
Introduction to Support Vector Machine
Example of Clustering
Running K-means with Scikit-Learn
Introduction to Deep Learning using TensorFlow
Deep Learning Compared to Other Machine Learning Approaches
Applications of Deep Learning
How to run the Neural Network using TensorFlow
Cases of Study with Real Data
Sources & References
Frequently Asked Questions
Q: Is this book for me and do I need programming experience? A: If you want to smash Machine Learning from scratch, this book is for you. If you already wrote a few lines of code and recognize basic programming statements, youll be OK.
Q: Does this book include everything I need to become a Machine Learning expert? A: Unfortunately, no. This book is designed for readers taking their first steps in Machine Learning and further learning will be required beyond this book to master all aspects of Machine Learning.
Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you arent satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at contact@aisciences.net.If you need to see the quality of our job, AI Sciences Company offering you a free eBook in Machine Learning with Python written by the data scientist Alain Kaufmann at http: //aisciences.net/free-books/
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INTRODUCTION TO MACHINE LEARNING WITH PYTHON
A Guide for Beginners in Data Science
Daniel Nedal & Peters Morgan
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Table of Contents
Copyright 2018 by AI Sciences
All rights reserved.
First Printing, 2018
Edited by Davies Company
Ebook Converted and Cover by Pixels Studio
Publised by AI Sciences LLC
ISBN-13: 978-1724417503
ISBN-10: 1724417509
The contents of this book may not be reproduced, duplicated or transmitted without the direct written permission of the author.
Under no circumstances will any legal responsibility or blame be held against the publisher for any reparation, damages, or monetary loss due to the information herein, either directly or indirectly.
Legal Notice:
You cannot amend, distribute, sell, use, quote or paraphrase any part or the content within this book without the consent of the author.
Disclaimer Notice:
Please note the information contained within this document is for educational and entertainment purposes only. No warranties of any kind are expressed or implied. Readers acknowledge that the author is not engaging in the rendering of legal, financial, medical or professional advice. Please consult a licensed professional before attempting any techniques outlined in this book.
By reading this document, the reader agrees that under no circumstances is the author responsible for any losses, direct or indirect, which are incurred as a result of the use of information contained within this document, including, but not limited to, errors, omissions, or inaccuracies.
To all the Data Scientist and Computer Scientist in the World
Authors Biography
Daniel Nedal is a data scientist and long-time user of the Python. He currently works as a computer scientist and as a research director at one of the biggest University in Paris.
Peters Morgan is a lecturer at the Data Science Institute at Melbourne University and a long-time user and developer of the Python. He is one of the core developers of some data science libraries in Python. Peter worked also as Machine Learning Scientist at Google for many years.
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Preface
Some people call this artificial intelligence, but the reality is this technology will enhance us. So instead of artificial intelligence, I think we'll augment our intelligence.
Ginni Rometty
The main purpose of this book is to provide the reader with the most fundamental knowledge of machine learning with Python so that they can understand what these are all about.
Book Objectives
This book will help you:
- Have an appreciation for machine learning and deep learning and an understanding of their fundamental principles.
- Have an elementary grasp of machine learning concepts and algorithms.
- Have achieved a technical background in machine learning and also deep learning
Target Users
The book designed for a variety of target audiences. The most suitable users would include:
- Newbies in computer science techniques and machine learning
- Professionals in machine learning and social sciences
- Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way
- Students and academicians, especially those focusing on machine learning practical guide using R
Is this book for me?
If you want to smash machine learning from scratch, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, youll be OK.
Why this book?
This book is written to help you learn machine learning using Python programming. If you are an absolute beginner in this field, youll find that this book explains complex concepts in an easy to understand manner without math or complicated theorical elements. If you are an experienced data scientist, this book gives you a good base from which to explore machine learning application.
Topics are carefully selected to give you a broad exposure to machine learning application. While not overwhelming you with information overload.
The example and cases studies are carefully chosen to demonstrate each algorithm and model so that you can gain a deeper understand of machine learning. Inside the book and in the appendices at the end of the book we provide you a convenient references.
You can download the source code for the project and other free books at:
http://aisciences.net/code
Your Free Gift
As a way of saying thanks you for your purchase, AI Sciences Publishing Company offering you a free eBook in Machine Learning with Python written by the data scientist Alain Kaufmann.
It is a full book that contains useful machine learning techniques using python. It is 100 pages book with one bonus chapter focusing in Anaconda Setup & Python Crash Course. AI Sciences encourage you to print, save and share. You can download it by going to the link below or by clicking in the book cover above.
http://aisciences.net/free-books/
If you want to help us produce more material like this, then please leave an honest review on amazon. It really does make a difference.
The importance of machine learning and deep learning is such that everyone regardless of their profession should have a fair understanding of how it works. Having said that, this book is geared towards the following set of people:
Anyone who is intrigued by how algorithms arrive at predictions but has no previous knowledge of the field.
Software developers and engineers with a strong programming background but seeking to break into the field of machine learning.
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