Android Apps for Absolute Beginners

Android Apps for Absolute Beginners Author Wallace Jackson
ISBN-10 9781430247883
Release 2012-11-28
Pages 400
Download Link Click Here

Anybody can start building simple apps for the Android platform, and this book will show you how! Recently updated to include Android Jelly Bean, Android Apps for Absolute Beginners, Second Edition takes you through the process of getting your first Android apps up and running using plain English and practical examples. 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 is Android Jelly Bean compliant, but is backwards compatible to most of the previous Android releases. What you’ll learn Get yourself and your computer set up for Android apps development Use the Eclipse programming environment to make your Android development efficient and straightforward Follow steps in plain English to build simple apps and get them working immediately Style your application so that it appeals to potential users Make use of the Android’s touch screen Use shortcuts and cheat sheets to create apps the easy way Use the basics of Java and XML to move onto more advanced apps 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 For Dummies

Machine Learning For Dummies Author John Paul Mueller
ISBN-10 9781119245773
Release 2016-05-11
Pages 432
Download Link Click Here

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

Android Apps for Absolute Beginners Author Wallace Jackson
ISBN-10 9781484222683
Release 2017-03-14
Pages 484
Download Link Click Here

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.



Bayesian Reasoning and Machine Learning

Bayesian Reasoning and Machine Learning Author David Barber
ISBN-10 9780521518147
Release 2012-02-02
Pages 697
Download Link Click Here

A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.



Introduction to Machine Learning

Introduction to Machine Learning Author Ethem Alpaydin
ISBN-10 9780262028189
Release 2014-08-29
Pages 640
Download Link Click Here

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.



Understanding Machine Learning

Understanding Machine Learning Author Shai Shalev-Shwartz
ISBN-10 9781107057135
Release 2014-05-19
Pages 409
Download Link Click Here

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.



Fundamentals of Machine Learning for Predictive Data Analytics

Fundamentals of Machine Learning for Predictive Data Analytics Author John D. Kelleher
ISBN-10 9780262029445
Release 2015-07-24
Pages 624
Download Link Click Here

A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.



Machine Learning

Machine Learning Author Ethem Alpaydin
ISBN-10 9780262529518
Release 2016-10-07
Pages 224
Download Link Click Here

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

Introduction to Machine Learning with Python Author Andreas C. Müller
ISBN-10 9781449369897
Release 2016-09-26
Pages 394
Download Link Click Here

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



Machine Learning

Machine Learning Author Stephen Marsland
ISBN-10 9781498759786
Release 2015-09-15
Pages 457
Download Link Click Here

A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation. New to the Second Edition Two new chapters on deep belief networks and Gaussian processes Reorganization of the chapters to make a more natural flow of content Revision of the support vector machine material, including a simple implementation for experiments New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron Additional discussions of the Kalman and particle filters Improved code, including better use of naming conventions in Python Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author’s website.



Practical Machine Learning Innovations in Recommendation

Practical Machine Learning  Innovations in Recommendation Author Ted Dunning
ISBN-10 9781491915714
Release 2014-08-18
Pages 56
Download Link Click Here

Building a simple but powerful recommendation system is much easier than you think. Approachable for all levels of expertise, this report explains innovations that make machine learning practical for business production settings—and demonstrates how even a small-scale development team can design an effective large-scale recommendation system. Apache Mahout committers Ted Dunning and Ellen Friedman walk you through a design that relies on careful simplification. You’ll learn how to collect the right data, analyze it with an algorithm from the Mahout library, and then easily deploy the recommender using search technology, such as Apache Solr or Elasticsearch. Powerful and effective, this efficient combination does learning offline and delivers rapid response recommendations in real time. Understand the tradeoffs between simple and complex recommenders Collect user data that tracks user actions—rather than their ratings Predict what a user wants based on behavior by others, using Mahoutfor co-occurrence analysis Use search technology to offer recommendations in real time, complete with item metadata Watch the recommender in action with a music service example Improve your recommender with dithering, multimodal recommendation, and other techniques



An Introduction to Statistical Learning

An Introduction to Statistical Learning Author Gareth James
ISBN-10 9781461471387
Release 2013-06-24
Pages 426
Download Link Click Here

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.



Computer Programming with C

Computer Programming with C  Author Kunal Pimparkhede
ISBN-10 9781316506806
Release 2017-01-26
Pages 1014
Download Link Click Here

"Provides an in-depth explanation of the C and C++ programming languages along with the fundamentals of object oriented programming paradigm"--



Machine Learning

Machine Learning Author Kevin P. Murphy
ISBN-10 9780262018029
Release 2012-08-24
Pages 1067
Download Link Click Here

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.



Python Real World Machine Learning

Python  Real World Machine Learning Author Prateek Joshi
ISBN-10 9781787120679
Release 2016-11-14
Pages 941
Download Link Click Here

Learn to solve challenging data science problems by building powerful machine learning models using Python About This Book Understand which algorithms to use in a given context with the help of this exciting recipe-based guide This practical tutorial tackles real-world computing problems through a rigorous and effective approach Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale Who This Book Is For This Learning Path is for Python programmers who are looking to use machine learning algorithms to create real-world applications. It is ideal for Python professionals who want to work with large and complex datasets and Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. Experience with Python, Jupyter Notebooks, and command-line execution together with a good level of mathematical knowledge to understand the concepts is expected. Machine learning basic knowledge is also expected. What You Will Learn Use predictive modeling and apply it to real-world problems Understand how to perform market segmentation using unsupervised learning Apply your new-found skills to solve real problems, through clearly-explained code for every technique and test Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms Increase predictive accuracy with deep learning and scalable data-handling techniques Work with modern state-of-the-art large-scale machine learning techniques Learn to use Python code to implement a range of machine learning algorithms and techniques In Detail Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we understand and interact with the world around us. In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you'll acquire a broad set of powerful skills in the area of feature selection and feature engineering. The third module in this learning path, Large Scale Machine Learning with Python, dives into scalable machine learning and the three forms of scalability. It covers the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python. This Learning Path will teach you Python machine learning for the real world. The machine learning techniques covered in this Learning Path are at the forefront of commercial practice. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Python Machine Learning Cookbook by Prateek Joshi Advanced Machine Learning with Python by John Hearty Large Scale Machine Learning with Python by Bastiaan Sjardin, Alberto Boschetti, Luca Massaron Style and approach This course is a smooth learning path that will teach you how to get started with Python machine learning for the real world, and develop solutions to real-world problems. Through this comprehensive course, you'll learn to create the most effective machine learning techniques from scratch and more!



Python for Kids

Python for Kids Author Jason R. Briggs
ISBN-10 9781593274078
Release 2013
Pages 344
Download Link Click Here

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.



Python Machine Learning

Python Machine Learning Author Sebastian Raschka
ISBN-10 9781783555147
Release 2015-09-23
Pages 454
Download Link Click Here

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.