Machine Learning For Dummies

Filename: machine-learning-for-dummies.pdf
ISBN: 9781119245773
Release Date: 2016-05-11
Number of pages: 432
Author: John Paul Mueller
Publisher: John Wiley & Sons

Download and read online Machine Learning For Dummies in PDF and EPUB Your no-nonsense guide to making sense of machine learning Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine 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 data—or anything in between—this guide makes it easier to understand and implement machine learning seamlessly. Grasp how day-to-day activities are powered by machine learning Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis Learn to code in R using R Studio Find 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!


Android Apps for Absolute Beginners

Filename: android-apps-for-absolute-beginners.pdf
ISBN: 9781484200193
Release Date: 2014-08-28
Number of pages: 712
Author: Wallace Jackson
Publisher: Apress

Download and read online Android Apps for Absolute Beginners in PDF and EPUB Anybody can start building multimedia apps for the Android platform, and this book will show you how! Now updated to include both Android 4.4 and the new Android L, Android Apps for Absolute Beginners, Third Edition takes you through the process of getting your first Android apps up and running using plain English and practical examples. If you have a great idea for an Android app, but have never programmed before, then this book is for you. This book cuts through the fog of jargon and mystery that surrounds Android apps development, and gives you simple, step-by-step instructions to get you started. Teaches Android application development in language anyone can understand, giving you the best possible start in Android development Provides simple, step-by-step examples that make learning easy, allowing you to pick up the concepts without fuss Offers clear code descriptions and layout so that you can get your apps running as soon as possible This book covers both Android 4.4 (KitKat) and Android L, but is also backwards compatible to cover the previous Android releases since Android 1.5.


Android Apps for Absolute Beginners

Filename: android-apps-for-absolute-beginners.pdf
ISBN: 9781484222683
Release Date: 2017-03-14
Number of pages: 484
Author: Wallace Jackson
Publisher: Apress

Download and read online Android Apps for Absolute Beginners in PDF and EPUB Get your first Android apps up and running with the help of plain English and practical examples. If you have a great idea for an Android app, but have never programmed before, then this book is for you. Android Apps for Absolute Beginners cuts through the fog of jargon and mystery that surrounds Android app development, and gives you simple, step-by-step instructions to get you started. This book teaches Android application development in language anyone can understand, giving you the best possible start in Android development. It provides clean, straightforward examples that make learning easy, allowing you to pick up the concepts without fuss. It offers clear code descriptions and layout so that you can get your apps running as soon as possible Although this book covers what's new in Android 7, it is also backwards compatible to cover some of the previous Android releases. What You'll Learn Download, install, and configure the latest software needed for Android app development Work efficiently using an integrated development environment (IDE) Build useful, attractive applications and get them working immediately Create apps with ease using XML markup and drag-and-drop graphical layout editors Use new media and graphics to skin your app so that it has maximum appeal Create advanced apps combining XML, Java and new media content Who This Book Is For If you have a great idea for an Android app, but have never programmed before, then this book is for you. You don’t need to have any previous computer programming skills — as long as you have a desire to learn and you know which end of the mouse is which, the world of Android apps development awaits.


Machine Learning

Filename: machine-learning.pdf
ISBN: 9780262529518
Release Date: 2016-10-07
Number of pages: 224
Author: Ethem Alpaydin
Publisher: MIT Press

Download and read online Machine Learning in PDF and EPUB A concise overview of machine learning -- computer programs that learn from data -- which underlies applications that include recommendation systems, face recognition, and driverless cars.


Introduction to Machine Learning with Python

Filename: introduction-to-machine-learning-with-python.pdf
ISBN: 9781449369897
Release Date: 2016-09-26
Number of pages: 394
Author: Andreas C. Müller
Publisher: "O'Reilly Media, Inc."

Download and read online Introduction to Machine Learning with Python in PDF and EPUB Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills


Make Your Own Neural Network

Filename: make-your-own-neural-network.pdf
ISBN: 1530826608
Release Date: 2016-03-31
Number of pages: 222
Author: Tariq Rashid
Publisher: Createspace Independent Publishing Platform

Download and read online Make Your Own Neural Network in PDF and EPUB A step-by-step gentle journey through the mathematics of neural networks, and making your own using the Python computer language. Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun and unhurried journey, starting from very simple ideas, and gradually building up an understanding of how neural networks work. You won't need any mathematics beyond secondary school, and an accessible introduction to calculus is also included. The ambition of this guide is to make neural networks as accessible as possible to as many readers as possible - there are enough texts for advanced readers already! You'll learn to code in Python and make your own neural network, teaching it to recognise human handwritten numbers, and performing as well as professionally developed networks. Part 1 is about ideas. We introduce the mathematical ideas underlying the neural networks, gently with lots of illustrations and examples. Part 2 is practical. We introduce the popular and easy to learn Python programming language, and gradually builds up a neural network which can learn to recognise human handwritten numbers, easily getting it to perform as well as networks made by professionals. Part 3 extends these ideas further. We push the performance of our neural network to an industry leading 98% using only simple ideas and code, test the network on your own handwriting, take a privileged peek inside the mysterious mind of a neural network, and even get it all working on a Raspberry Pi. All the code in this has been tested to work on a Raspberry Pi Zero.


Understanding Machine Learning

Filename: understanding-machine-learning.pdf
ISBN: 9781107057135
Release Date: 2014-05-19
Number of pages: 409
Author: Shai Shalev-Shwartz
Publisher: Cambridge University Press

Download and read online Understanding Machine Learning in PDF and EPUB Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.


An Introduction to Statistical Learning

Filename: an-introduction-to-statistical-learning.pdf
ISBN: 9781461471387
Release Date: 2013-06-24
Number of pages: 426
Author: Gareth James
Publisher: Springer Science & Business Media

Download and read online An Introduction to Statistical Learning in PDF and EPUB An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.


Practical Statistics for Data Scientists

Filename: practical-statistics-for-data-scientists.pdf
ISBN: 9781491952917
Release Date: 2017-05-10
Number of pages: 320
Author: Peter Bruce
Publisher: "O'Reilly Media, Inc."

Download and read online Practical Statistics for Data Scientists in PDF and EPUB Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data


Numsense Data Science for the Layman

Filename: numsense-data-science-for-the-layman.pdf
ISBN: 9811110689
Release Date: 2017-03-24
Number of pages:
Author: Annalyn Ng
Publisher: Annalyn Ng & Kenneth Soo

Download and read online Numsense Data Science for the Layman in PDF and EPUB Used in Stanford's CS102 Big Data (Spring 2017) course. Want to get started on data science? Our promise: no math added. This book has been written in layman's terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application. To help you grasp key concepts, we stick to intuitive explanations, as well as lots of visuals, all of which are colorblind-friendly. Popular concepts covered include: A/B Testing Anomaly Detection Association Rules Clustering Decision Trees and Random Forests Regression Analysis Social Network Analysis Neural Networks Features: Intuitive explanations and visuals Real-world applications to illustrate each algorithm Point summaries at the end of each chapter Reference sheets comparing the pros and cons of algorithms Glossary list of commonly-used terms With this book, we hope to give you a practical understanding of data science, so that you, too, can leverage its strengths in making better decisions.


Python for Kids

Filename: python-for-kids.pdf
ISBN: 9781593274078
Release Date: 2013
Number of pages: 344
Author: Jason R. Briggs
Publisher: No Starch Press

Download and read online Python for Kids in PDF and EPUB Introduces the basics of the Python programming language, covering how to use data structures, organize and reuse code, draw shapes and patterns with turtle, and create games and animations with tkinter.


Introduction to Machine Learning

Filename: introduction-to-machine-learning.pdf
ISBN: 9780262028189
Release Date: 2014-08-29
Number of pages: 640
Author: Ethem Alpaydin
Publisher: MIT Press

Download and read online Introduction to Machine Learning in PDF and EPUB The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.


Hands On Machine Learning with Scikit Learn and TensorFlow

Filename: hands-on-machine-learning-with-scikit-learn-and-tensorflow.pdf
ISBN: 9781491962268
Release Date: 2017-03-13
Number of pages: 568
Author: Aurélien Géron
Publisher: "O'Reilly Media, Inc."

Download and read online Hands On Machine Learning with Scikit Learn and TensorFlow in PDF and EPUB Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.


C Programming for the Absolute Beginner

Filename: c-programming-for-the-absolute-beginner.pdf
ISBN: 9781598639414
Release Date: 2009
Number of pages: 400
Author: Don Barrett
Publisher: Cengage Learning

Download and read online C Programming for the Absolute Beginner in PDF and EPUB Digital Performer is one of the leading digital audio workstation programs for the Mac. It allows you to sequence MIDI, record multiple channels of audio, mix them down, synch them to video, and utilize plug-in software synthesizers and software studios.


Knitting in Plain English

Filename: knitting-in-plain-english.pdf
ISBN: 0312458533
Release Date: 1986-02-15
Number of pages: 241
Author: Maggie Righetti
Publisher: Macmillan

Download and read online Knitting in Plain English in PDF and EPUB An introduction to knitting covers patterns, gauges, yarns, needles, types of construction, casting on and off, increases, decreases, buttonholes, hems, color, pattern stitching, blocking, and alterations