Machine Learning Fundamentals.pdf

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Appendix

History MACHINE LEARNING FUNDAMENTALS Topics Copyright © 2018 Packt Publishing Tutorials Offers & Deals All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Highlights Settings Support Sign Out Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy

of this information. Author: Hyatt Saleh Managing Editor: Neha Nair Acquisitions Editor: Aditya Date Production Editor: Samita Warang Editorial Board: David Barnes, Ewan Buckingham, Simon Cox, Manasa Kumar, Alex Mazonowicz, Douglas Paterson, Dominic Pereira, Shiny Poojary, Saman Siddiqui, Erol Staveley, Ankita Thakur, and Mohita Vyas First Published: November 2018 Production Reference: 1291118 ISBN: 9781789803556 Table of Contents Preface

Introduction to Scikit-Learn INTRODUCTION SCIKIT-LEARN ADVANTAGES OF SCIKIT-LEARN DISADVANTAGES OF SCIKIT-LEARN DATA REPRESENTATION TABLES OF DATA FEATURES AND TARGET MATRICES EXERCISE 1: LOADING A SAMPLE DATASET AND CREATING THE FEATURES AND TARGET MATRICES ACTIVITY 1: SELECTING A TARGET FEATURE AND CREATING A TARGET MATRIX

DATA PREPROCESSING MESSY DATA EXERCISE 2: DEALING WITH MESSY DATA DEALING WITH CATEGORICAL FEATURES EXERCISE 3: APPLYING FEATURE ENGINEERING OVER TEXT DATA RESCALING DATA EXERCISE 4: NORMALIZING AND STANDARDIZING DATA ACTIVITY 2: PREPROCESSING AN ENTIRE DATASET SCIKIT-LEARN API HOW DOES IT WORK?

SUPERVISED AND UNSUPERVISED LEARNING SUPERVISED LEARNING

UNSUPERVISED LEARNING SUMMARY Unsupervised Learning: Real-Life Applications INTRODUCTION CLUSTERING CLUSTERING TYPES APPLICATIONS OF CLUSTERING EXPLORING A DATASET: WHOLESALE CUSTOMERS DATASET UNDERSTANDING THE DATASET DATA VISUALIZATION

LOADING THE DATASET USING PANDAS VISUALIZATION TOOLS EXERCISE 5: PLOTTING A HISTOGRAM OF ONE FEATURE FROM THE NOISY CIRCLES DATASET ACTIVITY 3: USING DATA VISUALIZATION TO AID THE PREPROCESSING PROCESS K-MEANS ALGORITHM UNDERSTANDING THE ALGORITHM EXERCISE 6: IMPORTING AND TRAINING THE K- MEANS ALGORITHM OVER A DATASET ACTIVITY 4: APPLYING THE K-MEANS ALGORITHM TO A DATASET MEAN-SHIFT ALGORITHM UNDERSTANDING THE ALGORITHM

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