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    Python Machine Learning: Introduction to Machine Learning with Python
    Python Machine Learning: Introduction to Machine Learning with Python
    Python Machine Learning: Introduction to Machine Learning with Python
    Ebook81 pages1 hour

    Python Machine Learning: Introduction to Machine Learning with Python

    By Frank Millstein

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    About this ebook

    Machine learning is the science of getting machines and computers to act and learn on their own without being programmed explicitly. In just the past decade, this field has given us practical speech recognition, self-driving cars, greatly improved understanding of the overall human genome, effective web search and much more. Therefore, there is no wondering why machine learning is so pervasive today.

    In this book, you will learn more about interpreting machine learning techniques using Python. You will also gain practice as you implement the most popular machine learning techniques on some real-world examples and you will learn both about the theoretical and practical machine learning implementation using Python's machine learning libraries.

    At the end of the book, you will be able to cope with more complex machine learning issues solving your own problems using Python and its libraries specifically crafted for machine learning.

    Here Is A Preview Of What You'll Learn Here…

    • Basics behind machine learning techniques
    • Different machine learning algorithms
    • Fundamental machine learning applications and their importance
    • Getting started with machine learning in Python, installing and starting SciPy
    • Loading data and importing different libraries
    • Data summarization and data visualization
    • Evaluation of machine learning models and making predictions
    • Most commonly used machine learning algorithms, linear and logistic regression, decision trees support vector machines, k-nearest neighbors, random forests
    • Solving multi-clasisfication problems
    • Data visualization with Matplotlib and data transformation with Pandas and Scikit-learn
    • Solving multi-label classification problems
    • And much, much more...

    Get this book NOW and learn more about Machine Learning with Python!

    LanguageEnglish
    PublisherFrank Millstein
    Release dateOct 19, 2019
    ISBN9781393909989
    Python Machine Learning: Introduction to Machine Learning with Python

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      Book preview

      Python Machine Learning - Frank Millstein

      By Frank Millstein

      WHAT IS IN THE BOOK?

      INTRODUCTION

      BASIC MACHINE LEARNING CONCEPTS

      MACHINE LEARNING ALGORITHMS

      MACHINE LEARNING APPLICATIONS

      EVOLUTION OF MACHINE LEARNING

      HOW WE GET MACHINES TO LEARN ON THEIR OWN

      THE GROWING IMPORTANCE OF MACHINE LEARNING

      MACHINE LEARNING LIMITATIONS AND CHALLENGES

      CHAPTER 1: GETTING STARTED WITH MACHINE LEARNING IN PYTHON

      INSTALLING AND STARTING PYTHON SCIPY

      STARTING PYTHON

      LOADING DATA

      IMPORTING LIBRARIES

      LOADING DATASET

      SUMMARIZING THE DATASET

      DATA VISUALIZATION

      EVALUATING ALGORITHMS

      BUILDING MODELS

      SELECTING BEST MODELS

      MAKING PREDICTIONS

      CHAPTER 2: MACHINE LEARNING ALGORITHMS

      LINEAR REGRESSION

      LOGISTIC REGRESSION

      DECISION TREES

      SUPPORT VECTOR MACHINES

      K-NEAREST NEIGHBORS

      RANDOM FORESTS

      K-MEAN CLUSTERING

      PRINCIPAL COMPONENTS ANALYSIS

      CHAPTER 3: SOLVING CLASSIFICATION PROBLEMS

      TRAINING TEST DATA

      LOADING DATA USING PANDAS

      MATPLOTLIB DATA VISUALIZATION

      TRANSFORMING DATA WITH SKLEARN AND PANDAS

      MODEL TRAINING

      PREDICTING USING THE CLASSIFICATION MODEL

      EVALUATION OF THE CLASSIFICATION MODEL

      CHAPTER 4: SOLVING MULTI-LABEL CLASSIFICATION PROBLEMS

      GENERATING MULTI-LABEL DATASETS

      PROBLEM TRANSFORMATION

      ADAPTED ALGORITHM

      ENSEMBLE APPROACHES

      LAST WORDS

      Copyright © 2018 by Frank Millstein- All rights reserved.

      This document is geared towards providing exact and reliable information in regards to the topic and issue covered. The publication is sold with the idea that the publisher is not required to render accounting, officially permitted, or otherwise, qualified services. If advice is necessary, legal or professional, a practiced individual in the profession should be ordered.

      From a Declaration of Principles which was accepted and approved equally by a Committee of the American Bar Association and a Committee of Publishers and Associations.

      In no way is it legal to reproduce, duplicate, or transmit any part of this document by either electronic means or in printed format. Recording of this publication is strictly prohibited, and any storage of this document is not allowed unless with written permission from the publisher. All rights reserved.

      The information provided herein is stated to be truthful and consistent, in that any liability, in terms of inattention or otherwise, by any usage or abuse of any policies, processes, or directions contained within is the solitary and utter responsibility of the recipient reader. 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.

      Respective authors own all copyrights not held by the publisher.

      The information herein is offered for informational purposes solely and is universal as so. The presentation of the information is without contract or any type of guarantee assurance.

      The trademarks that are used are without any consent, and the publication of the trademark is without permission or backing by the trademark owner. All trademarks and brands within this book are for clarifying purposes only and are owned by the owners themselves, not affiliated with this document.

      INTRODUCTION

      Machine learning is one of the artificial intelligence applications which provides systems, machines, and computers a method to automatically learn without the need for humans to program them. The method of teaching that they learn from are in the examples which are submitted to them and, in the fact that the systems, machines, and computers can learn and improve from gained experience as they process. Machine learning is mainly focused on the development of various computer programs, which can easily and accurately access any kind of data and use it to learn completely by themselves.

      The process of machine learning begins with data, examples, observations from direct experiences, or instructions to look for different patterns in data and make better decisions in the future that are based on the examples humans provide to the machines. The main goal of machine learning is to allow the machines and computers to learn from examples automatically without any human assistance or intervention needed.

      In fact, machine learning is the science of getting machines and computers to learn as well as act as humans do. Machine learning is also improving machines’ learning over time in a completely autonomous fashion just by feeding them information, data, and examples that include real-world interactions. As any other computer science concept, machine learning has different definitions, which all arrive at the same conclusion.

      Machine learning is purely based on algorithms which can learn new information from data without any need to rely on the traditional, rules-based programming. Therefore, machine learning algorithms can straightforwardly figure out how to perform valuable and important tasks just by observing examples and data.

      Therefore, this is the science of getting machines to act as humans do. Machine learning at its most fundamental practice is the science of using models and algorithms to parse data, learn valuable information from it, and

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