Calgary Public Library

Machine learning for dummies, by John Paul Mueller and Luca Massaron

eng

Includes bibliographical references

no index present

non fiction

Machine learning for dummies

dictionariesbibliography

949758901

by John Paul Mueller and Luca Massaron

For dummies

aYour no-nonsense guide to making sense of machine learningMachine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks. Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data₇or anything in between₇this guide makes it easier to understand and implement machine learning seamlessly. Grasp how day-to-day activities are powered by machine learningLearn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysisLearn to code in R using R StudioFind out how to code in Python using Anaconda Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!

Title Page -- Copyright Page -- Table of Contents -- Introduction -- About This Book -- Foolish Assumptions -- Icons Used in This Book -- Beyond the Book -- Where to Go from Here -- Part 1 Introducing How Machines Learn -- Chapter 1 Getting the Real Story about AI -- Moving beyond the Hype -- Dreaming of Electric Sheep -- Understanding the history of AI and machine learning -- Exploring what machine learning can do for AI -- Considering the goals of machine learning -- Defining machine learning limits based on hardware -- Overcoming AI FantasiesDiscovering the fad uses of AI and machine learning -- Considering the true uses of AI and machine learning -- Being useful -- being mundane -- Considering the Relationship between AI and Machine Learning -- Considering AI and Machine Learning Specifications -- Defining the Divide between Art and Engineering -- Chapter 2 Learning in the Age of Big Data -- Defining Big Data -- Considering the Sources of Big Data -- Building a new data source -- Using existing data sources -- Locating test data sources -- Specifying the Role of Statistics in Machine Learning -- Understanding the Role of AlgorithmsDefining what algorithms do -- Considering the five main techniques -- Defining What Training Means -- Chapter 3 Having a Glance at the Future -- Creating Useful Technologies for the Future -- Considering the role of machine learning in robots -- Using machine learning in health care -- Creating smart systems for various needs -- Using machine learning in industrial settings -- Understanding the role of updated processors and other hardware -- Discovering the New Work Opportunities with Machine Learning -- Working for a machine -- Working with machines -- Repairing machinesCreating new machine learning tasks -- Devising new machine learning environments -- Avoiding the Potential Pitfalls of Future Technologies -- Part 2 Preparing Your Learning Tools -- Chapter 4 Installing an R Distribution -- Choosing an R Distribution with Machine Learning in Mind -- Installing R on Windows -- Installing R on Linux -- Installing R on Mac OS X -- Downloading the Datasets and Example Code -- Understanding the datasets used in this book -- Defining the code repository -- Chapter 5 Coding in R Using RStudio -- Understanding the Basic Data Types -- Working with VectorsOrganizing Data Using Lists -- Working with Matrices -- Creating a basic matrix -- Changing the vector arrangement -- Accessing individual elements -- Naming the rows and columns -- Interacting with Multiple Dimensions Using Arrays -- Creating a basic array -- Naming the rows and columns -- Creating a Data Frame -- Understanding factors -- Creating a basic data frame -- Interacting with data frames -- Expanding a data frame -- Performing Basic Statistical Tasks -- Making decisions -- Working with loops -- Performing looped tasks without loops -- Working with functions -- Finding mean and median