Buku Pintar Pemrograman Java Untuk Pelajar Dan Mahasiswa
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Buku Pintar Pemrograman Java Untuk Pelajar Dan Mahasiswa
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Author : Vivian Siahaan
language : id
Publisher: BALIGE PUBLISHING
Release Date : 2020-03-15
Buku Pintar Pemrograman Java Untuk Pelajar Dan Mahasiswa written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-15 with Computers categories.
Puji syukur kepada Tuhan Yang Maha Kuasa atas tuntasnya penulisan buku ini. Buku ini ditulis karena spirit untuk mendokumentasikan gagasan-gagasan pemrograman berorientasi objek di dalam keluarga besar JAVA. Di Indonesia, sangat jarang ditemui buku yang mendiskusikan pemrograman JAVA yang mengupas secara detil kelebihan dan kekurangan suatu kode sumber. Buku ini menelaah suatu kode sumber dengan memberikan perhatian khusus terhadap potongan-potongan kode yang dianggap penting. Buku ini dikhususkan bagi mahasiswa sarjana dan pembelajar mandiri yang menjadi pemrogram aktif. Penulis mengucapkan penghargaan yang tinggi kepada semua pihak yang telah memberikan masukan-masukan inovatif selama penulisan buku ini. Akhirnya kami berharap buku ini menjadi referensi berguna bagi mereka yang membaca
Buku Pintar Pemrograman C Untuk Pelajar Dan Mahasiswa
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Author : Vivian Siahaan
language : id
Publisher: BALIGE PUBLISHING
Release Date : 2020-03-15
Buku Pintar Pemrograman C Untuk Pelajar Dan Mahasiswa written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-15 with Computers categories.
Puji syukur kepada Tuhan Yang Maha Kuasa atas tuntasnya penulisan buku ini. Buku ini dikonsentrasikan pada penjelasan sederhana atas tiap teknik yang menjadi bahasan. Buku ini ini untuk setiap orang yang ingin belajar bagaimana memprogram C# menggunakan .NET Framework. Beberapa bab awal pada buku ini ditujukan bagi mereka yang belum memiliki pengalaman dalam memprogram. Jika Anda telah memiliki keterampilan pemrograman bahasa pemrograman lain, maka banyak materi pada buku ini akan familiar bagi Anda. Banyak aspek pada sintaks C# memiliki kesamaan dengan bahasa permrograman lain (khususnya dengan Java). Jadi, jika Anda masih belum familiar dengan .NET Framework tetapi telah berpengalaman dalam memprogram, Anda sebaiknya memulai dari Bab 1 dan kemudian membaca cepat beberapa bab sesudahnya sebelum memasuki bab-bab yang berkaitan dengan pemrograman berorientasi objek pada C#. Buku ini ditujukan bagi pemula karena difokuskan pada aspek-aspek mendasar dari pemrograman C#. Setelah membaca buku ini, Anda akan memahami bagaimana mendefinisikan metode, properti, indekser, kelas dan antarmuka pada C#. Penjelasan tiap program diberikan baris demi baris, sehingga detil informasi di balik setiap kode dapat dipahami dengan lengkap.
Bahasa Pemrograman Populer
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Author : Tutuk Indriyani
language : id
Publisher: PT. Sonpedia Publishing Indonesia
Release Date : 2024-01-25
Bahasa Pemrograman Populer written by Tutuk Indriyani and has been published by PT. Sonpedia Publishing Indonesia this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-25 with Computers categories.
Buku "Bahasa Pemrograman Populer" mengungkap esensi dari bahasa pemrograman utama dengan merinci poin-poin kunci. Buku ini memulai perjalanannya dengan pengantar yang menyajikan landasan dasar dan pentingnya pemahaman berbagai bahasa pemrograman dalam dunia teknologi saat ini. Dari sana, pembaca dihadapkan pada keunggulan Python sebagai bahasa serba guna yang mendominasi ilmu data dan pengembangan perangkat lunak. Dilanjutkan dengan eksplorasi peran penting JavaScript dalam pengembangan web, Java sebagai bahasa cross-platform, dan C# dengan ekosistem .NET untuk aplikasi Windows dan web. Poin akhir mengenai PHP menyoroti perannya dalam pengembangan web dinamis. Buku ini menggabungkan penjelasan yang jelas dan aplikasi praktis, memberikan pembaca pemahaman yang mendalam tentang bahasa-bahasa kunci yang membentuk lanskap pemrograman modern. Dengan fokus pada Python, JavaScript, Java, C#, dan PHP, buku ini menjadi panduan yang tak ternilai bagi mereka yang ingin menjelajahi dan menguasai dunia bahasa pemrograman.
Learn From Scratch Machine Learning With Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2021-03-03
Learn From Scratch Machine Learning With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-03 with Computers categories.
In this book, you will learn how to use NumPy, Pandas, OpenCV, Scikit-Learn and other libraries to how to plot graph and to process digital image. Then, you will learn how to classify features using Perceptron, Adaline, Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN) models. You will also learn how to extract features using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kernel Principal Component Analysis (KPCA) algorithms and use them in machine learning. In Chapter 1, you will learn: Tutorial Steps To Create A Simple GUI Application, Tutorial Steps to Use Radio Button, Tutorial Steps to Group Radio Buttons, Tutorial Steps to Use CheckBox Widget, Tutorial Steps to Use Two CheckBox Groups, Tutorial Steps to Understand Signals and Slots, Tutorial Steps to Convert Data Types, Tutorial Steps to Use Spin Box Widget, Tutorial Steps to Use ScrollBar and Slider, Tutorial Steps to Use List Widget, Tutorial Steps to Select Multiple List Items in One List Widget and Display It in Another List Widget, Tutorial Steps to Insert Item into List Widget, Tutorial Steps to Use Operations on Widget List, Tutorial Steps to Use Combo Box, Tutorial Steps to Use Calendar Widget and Date Edit, and Tutorial Steps to Use Table Widget. In Chapter 2, you will learn: Tutorial Steps To Create A Simple Line Graph, Tutorial Steps To Create A Simple Line Graph in Python GUI, Tutorial Steps To Create A Simple Line Graph in Python GUI: Part 2, Tutorial Steps To Create Two or More Graphs in the Same Axis, Tutorial Steps To Create Two Axes in One Canvas, Tutorial Steps To Use Two Widgets, Tutorial Steps To Use Two Widgets, Each of Which Has Two Axes, Tutorial Steps To Use Axes With Certain Opacity Levels, Tutorial Steps To Choose Line Color From Combo Box, Tutorial Steps To Calculate Fast Fourier Transform, Tutorial Steps To Create GUI For FFT, Tutorial Steps To Create GUI For FFT With Some Other Input Signals, Tutorial Steps To Create GUI For Noisy Signal, Tutorial Steps To Create GUI For Noisy Signal Filtering, and Tutorial Steps To Create GUI For Wav Signal Filtering. In Chapter 3, you will learn: Tutorial Steps To Convert RGB Image Into Grayscale, Tutorial Steps To Convert RGB Image Into YUV Image, Tutorial Steps To Convert RGB Image Into HSV Image, Tutorial Steps To Filter Image, Tutorial Steps To Display Image Histogram, Tutorial Steps To Display Filtered Image Histogram, Tutorial Steps To Filter Image With CheckBoxes, Tutorial Steps To Implement Image Thresholding, and Tutorial Steps To Implement Adaptive Image Thresholding. You will also learn: Tutorial Steps To Generate And Display Noisy Image, Tutorial Steps To Implement Edge Detection On Image, Tutorial Steps To Implement Image Segmentation Using Multiple Thresholding and K-Means Algorithm, Tutorial Steps To Implement Image Denoising, Tutorial Steps To Detect Face, Eye, and Mouth Using Haar Cascades, Tutorial Steps To Detect Face Using Haar Cascades with PyQt, Tutorial Steps To Detect Eye, and Mouth Using Haar Cascades with PyQt, Tutorial Steps To Extract Detected Objects, Tutorial Steps To Detect Image Features Using Harris Corner Detection, Tutorial Steps To Detect Image Features Using Shi-Tomasi Corner Detection, Tutorial Steps To Detect Features Using Scale-Invariant Feature Transform (SIFT), and Tutorial Steps To Detect Features Using Features from Accelerated Segment Test (FAST). In Chapter 4, In this tutorial, you will learn how to use Pandas, NumPy and other libraries to perform simple classification using perceptron and Adaline (adaptive linear neuron). The dataset used is Iris dataset directly from the UCI Machine Learning Repository. You will learn: Tutorial Steps To Implement Perceptron, Tutorial Steps To Implement Perceptron with PyQt, Tutorial Steps To Implement Adaline (ADAptive LInear NEuron), and Tutorial Steps To Implement Adaline with PyQt. In Chapter 5, you will learn how to use the scikit-learn machine learning library, which provides a wide variety of machine learning algorithms via a user-friendly Python API and to perform classification using perceptron, Adaline (adaptive linear neuron), and other models. The dataset used is Iris dataset directly from the UCI Machine Learning Repository. You will learn: Tutorial Steps To Implement Perceptron Using Scikit-Learn, Tutorial Steps To Implement Perceptron Using Scikit-Learn with PyQt, Tutorial Steps To Implement Logistic Regression Model, Tutorial Steps To Implement Logistic Regression Model with PyQt, Tutorial Steps To Implement Logistic Regression Model Using Scikit-Learn with PyQt, Tutorial Steps To Implement Support Vector Machine (SVM) Using Scikit-Learn, Tutorial Steps To Implement Decision Tree (DT) Using Scikit-Learn, Tutorial Steps To Implement Random Forest (RF) Using Scikit-Learn, and Tutorial Steps To Implement K-Nearest Neighbor (KNN) Using Scikit-Learn. In Chapter 6, you will learn how to use Pandas, NumPy, Scikit-Learn, and other libraries to implement different approaches for reducing the dimensionality of a dataset using different feature selection techniques. You will learn about three fundamental techniques that will help us to summarize the information content of a dataset by transforming it onto a new feature subspace of lower dimensionality than the original one. Data compression is an important topic in machine learning, and it helps us to store and analyze the increasing amounts of data that are produced and collected in the modern age of technology. You will learn the following topics: Principal Component Analysis (PCA) for unsupervised data compression, Linear Discriminant Analysis (LDA) as a supervised dimensionality reduction technique for maximizing class separability, Nonlinear dimensionality reduction via Kernel Principal Component Analysis (KPCA). You will learn: 6.1 Tutorial Steps To Implement Principal Component Analysis (PCA), Tutorial Steps To Implement Principal Component Analysis (PCA) Using Scikit-Learn, Tutorial Steps To Implement Principal Component Analysis (PCA) Using Scikit-Learn with PyQt, Tutorial Steps To Implement Linear Discriminant Analysis (LDA), Tutorial Steps To Implement Linear Discriminant Analysis (LDA) with Scikit-Learn, Tutorial Steps To Implement Linear Discriminant Analysis (LDA) Using Scikit-Learn with PyQt, Tutorial Steps To Implement Kernel Principal Component Analysis (KPCA) Using Scikit-Learn, and Tutorial Steps To Implement Kernel Principal Component Analysis (KPCA) Using Scikit-Learn with PyQt. In Chapter 7, you will learn how to use Keras, Scikit-Learn, Pandas, NumPy and other libraries to perform prediction on handwritten digits using MNIST dataset. You will learn: Tutorial Steps To Load MNIST Dataset, Tutorial Steps To Load MNIST Dataset with PyQt, Tutorial Steps To Implement Perceptron With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Perceptron With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Perceptron With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression (LR) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression (LR) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression (LR) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement , Tutorial Steps To Implement Support Vector Machine (SVM) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Support Vector Machine (SVM) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree (DT) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree (DT) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree (DT) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest (RF) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest (RF) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest (RF) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement K-Nearest Neighbor (KNN) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement K-Nearest Neighbor (KNN) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, and Tutorial Steps To Implement K-Nearest Neighbor (KNN) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt.
Learn From Scratch Signal And Image Processing With Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-06-14
Learn From Scratch Signal And Image Processing With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-14 with Technology & Engineering categories.
In this book, you will learn how to use OpenCV, NumPy library and other libraries to perform signal processing, image processing, object detection, and feature extraction with Python GUI (PyQt). You will learn how to filter signals, detect edges and segments, and denoise images with PyQt. You will also learn how to detect objects (face, eye, and mouth) using Haar Cascades and how to detect features on images using Harris Corner Detection, Shi-Tomasi Corner Detector, Scale-Invariant Feature Transform (SIFT), and Features from Accelerated Segment Test (FAST). In Chapter 1, you will learn: Tutorial Steps To Create A Simple GUI Application, Tutorial Steps to Use Radio Button, Tutorial Steps to Group Radio Buttons, Tutorial Steps to Use CheckBox Widget, Tutorial Steps to Use Two CheckBox Groups, Tutorial Steps to Understand Signals and Slots, Tutorial Steps to Convert Data Types, Tutorial Steps to Use Spin Box Widget, Tutorial Steps to Use ScrollBar and Slider, Tutorial Steps to Use List Widget, Tutorial Steps to Select Multiple List Items in One List Widget and Display It in Another List Widget, Tutorial Steps to Insert Item into List Widget, Tutorial Steps to Use Operations on Widget List, Tutorial Steps to Use Combo Box, Tutorial Steps to Use Calendar Widget and Date Edit, and Tutorial Steps to Use Table Widget. In Chapter 2, you will learn: Tutorial Steps To Create A Simple Line Graph, Tutorial Steps To Create A Simple Line Graph in Python GUI, Tutorial Steps To Create A Simple Line Graph in Python GUI: Part 2, Tutorial Steps To Create Two or More Graphs in the Same Axis, Tutorial Steps To Create Two Axes in One Canvas, Tutorial Steps To Use Two Widgets, Tutorial Steps To Use Two Widgets, Each of Which Has Two Axes, Tutorial Steps To Use Axes With Certain Opacity Levels, Tutorial Steps To Choose Line Color From Combo Box, Tutorial Steps To Calculate Fast Fourier Transform, Tutorial Steps To Create GUI For FFT, Tutorial Steps To Create GUI For FFT With Some Other Input Signals, Tutorial Steps To Create GUI For Noisy Signal, Tutorial Steps To Create GUI For Noisy Signal Filtering, and Tutorial Steps To Create GUI For Wav Signal Filtering. In Chapter 3, you will learn: Tutorial Steps To Convert RGB Image Into Grayscale, Tutorial Steps To Convert RGB Image Into YUV Image, Tutorial Steps To Convert RGB Image Into HSV Image, Tutorial Steps To Filter Image, Tutorial Steps To Display Image Histogram, Tutorial Steps To Display Filtered Image Histogram, Tutorial Steps To Filter Image With CheckBoxes, Tutorial Steps To Implement Image Thresholding, and Tutorial Steps To Implement Adaptive Image Thresholding. In Chapter 4, you will learn: Tutorial Steps To Generate And Display Noisy Image, Tutorial Steps To Implement Edge Detection On Image, Tutorial Steps To Implement Image Segmentation Using Multiple Thresholding and K-Means Algorithm, and Tutorial Steps To Implement Image Denoising. In Chapter 5, you will learn: Tutorial Steps To Detect Face, Eye, and Mouth Using Haar Cascades, Tutorial Steps To Detect Face Using Haar Cascades with PyQt, Tutorial Steps To Detect Eye, and Mouth Using Haar Cascades with PyQt, and Tutorial Steps To Extract Detected Objects. In Chapter 6, you will learn: Tutorial Steps To Detect Image Features Using Harris Corner Detection, Tutorial Steps To Detect Image Features Using Shi-Tomasi Corner Detection, Tutorial Steps To Detect Features Using Scale-Invariant Feature Transform (SIFT), and Tutorial Steps To Detect Features Using Features from Accelerated Segment Test (FAST). You can download the XML files from https://viviansiahaan.blogspot.com/2023/06/learn-from-scratch-signal-and-image.html.
Metodologi Penelitian Kesehatan
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Author : I Ketut Swarjana, S.K.M., M.P.H., Dr.P.H.
language : id
Publisher: Penerbit Andi
Release Date : 2023-07-05
Metodologi Penelitian Kesehatan written by I Ketut Swarjana, S.K.M., M.P.H., Dr.P.H. and has been published by Penerbit Andi this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-05 with Science categories.
Penelitian adalah serangkaian proses yang dilakukan oleh peneliti dan umumnya dimulai dari penetapan topik maupun masalah penelitian, serta menemukan gap of knowledge; menentukan judul penelitian; rumusan masalah; tujuan penelitian; kerangka teori atau konsep; menetapkan variabel dan definisi operasional variabel; menetapkan metode penelitian termasuk desain, populasi, sampel, sampling, pengumpulan data, analisis, interpretasi, laporan penelitian, dan diseminasi. Penelitian dilakukan karena ada masalah atau ada kebutuhan untuk memecahkan masalah, menemukan penyebab masalah, dan Iain-Iain yang dilakukan dengan pendekatan ilmiah (scientific approach). Ada berbagai jenis penelitian yang kita kenal, mulai dari penelitian yang sangat sederhana atau dasar (basic research) sampai penelitian yang levelnya sangat tinggi dan kompleks (advanced research). Apabila dilihat dari desain penelitian, ada yang dikenal sebagai penelitian deskriptif dan juga analitik (cross-sectional, case-control, cohort, experimental study, dan Iain-Iain). Ada juga yang membagi penelitian menjadi penelitian observasional dan penelitian intervensional. Pembagian lainnya adalah penelitian kualitatif dan kuantitatif, serta ada juga yang menggabungkan penelitian kualitatif dan kuantitatif yang disebut sebagai a mixed method study. Selain desain penelitian, buku ini banyak membahas tentang alat dan metode pengumpulan data penelitian. Alat penelitian terdiri atas kuesioner, lembar observasi, check-list, dan Iain-Iain. Sementara itu, metode pengumpulan data terdiri atas metode kuesioner, wawancara, observasi, pengukuran, dan Iain-Iain. Selanjutnya, hal yang sangat penting dalam sebuah penelitian adalah analisis data penelitian. Analisis ini menggunakan statistik deskriptif khusus untuk penelitian deskriptif dan statistik inferensial khusus untuk penelitian analitik. Analisis inferensial menggunakan uji statistik parametrik maupun nonparametrik. Pembagian lainnya untuk analisis data penelitian, yaitu analisis univariate, bivariate, dan analisis multivariate. Selain hal di atas, etika penelitian juga sangat penting untuk diperhatikan. Semua penelitian terutama penelitian pada manusia harus mendapatkan approval dari komisi etik penelitian atau lembaga lain yang secara legalitas memiliki kewenangan untuk memberikan kelayakan etik sebelum penelitian dilaksanakan. Langkah berikutnya yang perlu dilakukan peneliti adalah membuat laporan penelitian, pembahasan, kesimpulan dan saran, serta melakukan diseminasi maupun publikasi hasil penelitian.
Connected Code
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Author : Yasmin B. Kafai
language : en
Publisher: MIT Press
Release Date : 2016-09-02
Connected Code written by Yasmin B. Kafai and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-02 with Education categories.
Why every child needs to learn to code: the shift from “computational thinking” to computational participation. Coding, once considered an arcane craft practiced by solitary techies, is now recognized by educators and theorists as a crucial skill, even a new literacy, for all children. Programming is often promoted in K-12 schools as a way to encourage “computational thinking”—which has now become the umbrella term for understanding what computer science has to contribute to reasoning and communicating in an ever-increasingly digital world. In Connected Code, Yasmin Kafai and Quinn Burke argue that although computational thinking represents an excellent starting point, the broader conception of “computational participation” better captures the twenty-first-century reality. Computational participation moves beyond the individual to focus on wider social networks and a DIY culture of digital “making.” Kafai and Burke describe contemporary examples of computational participation: students who code not for the sake of coding but to create games, stories, and animations to share; the emergence of youth programming communities; the practices and ethical challenges of remixing (rather than starting from scratch); and the move beyond stationary screens to programmable toys, tools, and textiles.
Optical Wdm Networks
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Author : Biswanath Mukherjee
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-06-15
Optical Wdm Networks written by Biswanath Mukherjee and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-06-15 with Computers categories.
Research and development on optical wavelength-division multiplexing (WDM) networks have matured considerably. While optics and electronics should be used appropriately for transmission and switching hardware, note that "intelligence'' in any network comes from "software,'' for network control, management, signaling, traffic engineering, network planning, etc.The role of software in creating powerful network architectures for optical WDM networks is emphasized. Optical WDM Networks is a textbook for graduate level courses. Its focus is on the networking aspects of optical networking, but it also includes coverage of physical layers in optical networks. The author introduces WDM and its enabling technologies and discusses WDM local, access, metro, and long-haul network architectures. Each chapter is self-contained, has problems at the end of each chapter, and the material is organized for self study as well as classroom use. The material is the most recent and timely in capturing the state-of-the-art in the fast-moving field of optical WDM networking.
Knowledge Management 2 0
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Author : Imed Boughzala
language : en
Publisher:
Release Date : 2012
Knowledge Management 2 0 written by Imed Boughzala and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Knowledge management categories.
"This book provides an overview of theoretical and empirical research on knowledge management generation in the Web 2.0 age, highlighting knowledge management evolution with a global focus and investigating the impact knowledge management 2.0 has on business models, enterprise governance and strategies, human resources, and IT design, implementation, and appropriation in organizations"--Provided by publisher.
The Mobile Learning Edge Tools And Technologies For Developing Your Teams
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Author : Gary Woodill
language : en
Publisher: McGraw Hill Professional
Release Date : 2010-09-10
The Mobile Learning Edge Tools And Technologies For Developing Your Teams written by Gary Woodill and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-09-10 with Business & Economics categories.
Engage and teach your team wherever and whenever—from one of the world's leading e-learning authorities. The digital electronics revolution keeps us connected with almost anyone around the world and makes information available anywhere, at anytime. In the workplace, the impact has been great, propelling mobile learning to the forefront of training and education. Dr. Gary Woodill, a senior analyst at a leading e-learning research firm shows you how mobile learning is evolving, and how organizations can use it more efficiently and effectively--with companies reaping the rewards of increased communication, teamwork, productivity and profitability. Learn how to break free from the old notions of training and development with the concrete strategies in The Mobile Learning Edge and Become skilled in the seven principles of successfully training employees on the move Implement new learning programs that employees can access anywhere Develop a future mobile learning strategy in an ever-changing environment Discover what might be the right kind of mobile technologies for your company With The Mobile Learning Edge you'll go beyond applications and content and be able to create engaging and productive mobile learning for your team. According to a recent study, there's one mobile device for every two people in the world, and the technology making these devices smarter and more connected is improving almost daily. The real revolution is that mobile learning releases learners from the classroom where they are immobilized, and allows them to learn at "anytime, anyplace." In The Mobile Learning Edge, Dr. Gary Woodill outlines the most effective methodologies for training and engaging employees on the move and takes the person out of the classroom, while keeping learners connected to the information they need at all times. The Mobile Learning Edge features: Information on the social media and enabled devices that can serve your mobile learning Concrete strategies for how your business can use mobile learning to train, educate, and instruct employees anywhere Pointers on information gathering and analysis on the fly Innovative ideas for creating effective mobile learning experiences Comprehensive strategies for anticipating future mobile learning needs and developments You'll find a wealth of information about the history of this emerging field, retrieving information, methods for learning, applications, uses, and experiences--and how to put it all together to build a mobile learning system that’s right for your team. Using case studies, Woodill shows how you can emulate the successes of corporations like Nike, Accenture, and Merrill Lynch in using micro-blogging, cloud computing, mobile gaming, intermodal mashups, virtual worlds, collective intelligence, and other mobile learning platforms to take your business's recruitment, training, communication, and collaboration functions to the next level.