Belajar Data Science Pengenalan Azure Machine Learning Studio

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Belajar Data Science Pengenalan Azure Machine Learning Studio

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Author : M Reza Faisal
language : id
Publisher: M Reza Faisal
Release Date :

Belajar Data Science Pengenalan Azure Machine Learning Studio written by M Reza Faisal and has been published by M Reza Faisal this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

**Cara Pembelian** Bagi yang tidak punya kartu kredit, maka pembelian dapat dilakukan dengan potong pulsa jika transaksi dilakukan pada device Android. Buku ini ditujukan bagi pembaca yang telah mengetahui konsep atau teori dari teknik, metode dan algoritma di bidang statistik dan machine learning, dan bagi pembaca yang ingin mencari tool yang dapat memudahkan menggunakan dan menerapkan konsep dan teori tersebut. Microsoft Azure ML Studio adalah tool berupa layanan komputasi awan yang berfungsi untuk membantu mengolah dan mengalisis data dengan berbagai metode konversi dan transformasi data, berbagai fungsi statistik serta bermacam-macam algoritma machine learning. Layanan seperti ini cocok digunakan bagi siapa saja yang bergelut di bidang data science namun tidak memiliki komputer dengan kinerja yang bagus. Atau kendala sumber daya listrik tidak selalu ada setiap waktu sehingga dapat mengganggu atau menghentikan pemrosesan data yang sedang berjalan. Maka dengan adanya layanan seperti Microsoft Azure ML Studio ini akan sangat membantu bagi siapa saja yang memiliki kendala serupa. Buku ini dibuat sebagai rangkuman dan catatan dari hal-hal yang penulis kerjakan dalam melakukan analisis dan pemrosesan data dengan Microsoft Azure ML Studio. Setiap pembahasan yang ditulis akan diberikan penjelesan sederhana tentang langkah-langkah yang dilakukan. Sehingga pembaca dapat mencoba langsung menyelesaikan masalah-masalah umum yang bidang statistik dan machine learning. **Isi Buku** 1 Pendahuluan - Komputasi Awan - Jenis-Jenis Layanan Komputasi Awan Infrastructure as a Service (IaaS) Platform as a Service Software as a Service - Microsoft Azure Program Gratis Mencoba Microsoft Azure Registrasi Portal Virtual Machine - Microsoft Azure Machine Learning Studio 2 Pengantar Azure ML Studio - Antarmuka Utama Projects Experiments Web services Notebooks Datasets Trained models Settings - Mengelola Dataset Menambah Dataset Melihat Dataset Menghapus Dataset - Mengelola Experiment Membuat Experiment Menjalankan Experiment Menyimpan Experiment Menghapus Experiment - Mengelola Modul Port Input & Output Bantuan & Dokumentasi Memberi Deskripsi Memberi Parameter Menghapus Modul 3 Data - Input Data Enter Data Manually Import Data - Missing Value Summarize Data Clean Missing Value - Duplicate Row Memilih & Mengabung Data Select Column in Dataset Add Columns Add Rows - Normalisasi Data Normalize Data - Sampling & Membagi Data Split Data Partition and Sample - Konversi Data 4 Fungsi Statistik - Operasi Matematika - Statistik Dasar - Korelasi Antar Variable - Distribusi Probabilitas - Hipotesis dengan t-Test 5 Machine Learning - Klasifikasi Klasifikasi Dua Class - Split Data Klasifikasi Dua Class - Cross Validation Klasifikasi Multi Class - Regresi Regresi - Split Data Regresi - Cross Validation - Clustering Sumber Data Clustering Hasil 6 Web Service Untuk Prediksi - Web Service - Setup Web Service Penentuan Experiment Membuat Web Service - Akses Web Service Akses dari Website Azure ML Akses dari Aplikasi Client 7 Topik Lanjutan - Modul dengan Bahasa Pemrograman R Versi R Input & Ouput Dataset Output R Device R Package Contoh Kasus - Klasifikasi Data Text dari Twitter Import Data Execute R Script Edit Metadata Feature Hashing Split Data Filter Based Feature Selection Train Model & Two-Class Support Vector Machine Score Model & Evaluate Model - Aplikasi Client Untuk Akses Azure ML Web Service Aplikasi Web Aplikasi Desktop Aplikasi Mobile Source Code 8 Penutup **Source Code & Free Ebook** Terima kasih bagi Anda mau membeli ebook ini. Ebook ini juga tersedia gratis jika Anda belum ingin membeli buku ini sekarang. Ebook gratis dapat diakses di link berikut: https://www.researchgate.net/publication/330184412_Seri_Belajar_Data_Science_Pengenalan_Azure_Machine_Learning_Studio Sedangkan source code contoh kasus paa ebook ini dapat diakses pada link berikut: https://github.com/rezafaisal/AzureMLStudioCodeSamples

Belajar Data Science Klasifikasi Dengan Bahasa Pemrograman R

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Author : M Reza Faisal
language : id
Publisher: M Reza Faisal
Release Date : 2017-01-04

Belajar Data Science Klasifikasi Dengan Bahasa Pemrograman R written by M Reza Faisal and has been published by M Reza Faisal this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-04 with Computers categories.

**Cara Pembelian** Bagi yang tidak punya kartu kredit, maka pembelian dapat dilakukan dengan potong pulsa jika transaksi dilakukan pada device Android. **Isi Buku** Metode atau teknik matematika, statistik atau machine learning yang dibahas pada buku ini adalah telah umum digunakan. Sehingga buku ini tidak akan membahas tentang konsep metode dan teknik tersebut. Buku hanya fokus membahas implementasi setiap metode dan teknik pada lingkungan R. Topik machine learning yang dibahas pada buku ini hanya fokus kepada supervised learning pada umumnya dan klasifikasi pada khususnya. Setiap teknik klasifikasi yang dibahas disertai contoh masalah dan penyelesaian langkah demi langkah sehingga dapat diikuti oleh pembaca dengan mudah. Selain membahas teknik-teknik supervised learning - klasifikasi, buku ini juga membahas teknik yang digunakan untuk mengukur kinerja teknik klasifikasi yang digunakan. Sehingga pembaca dapat memiliki pengetahuan yang lengkap untuk menyelesaikan masalah klasifikasi pada lingkungan R. Daftar isi dari buku ini adalah sebagai berikut: 1. Pendahuluan. Bagaimana manusia belajar mengenali? Kenapa komputer perlu mempunyai kemampuan belajar seperti manusia? Machine learning. Machine learning & data mining. 2. Pengantar Pemrograman R. Installasi. Tool Pemrograman. 3. Fungsi-Fungsi dasar R. Package. Working directory. Dataset. Menulis data ke file. Membaca file text. Membaca file Excel. Akses database. Menampilkan data. Memfilter data. Menggabung data. Eksplorasi data. Visualisasi data. Help. 4. Pengantar Klasifikasi. Definisi. Data. Langkah-langkah pengembangan. 5. Pengenalan & Pembagian Data. Pengenalan data. Pembagian data. 6. Rancangan Aplikasi Klasifikasi. Apakah langkah selanjutnya? Rancangan aplikasi. 7. K-Nearest Neighbors (KNN). Cara kerja. Persiapan. Sintaks. Implementasi. Catatan. 8. Naïve Bayes. Cara kerja. Persiapan. Sintaks. Implementasi. Catatan. 9. Support Vector Machine. Cara kerja. Persiapan. Sintaks. Implementasi. Catatan. 10. Decision Tree. Cara kerja. Persiapan. Sintaks. Implementasi. Catatan. 11. Klasifikasi Kelas Tidak Seimbang Definisi & Efek Kelas Tidak Seimbang Solusi Masalah Dataset Class Tidak Seimbang Solusi Pendekatan Data (Undersampling, Oversampling, Gabungan Undersampling & Oversampling) Solusi Pendekatan Algoritma (Bagging, Boosting & Stacking) Penutup **Source Code & Free Ebook** Terima kasih bagi Anda mau membeli ebook ini. Ebook ini juga tersedia gratis jika Anda belum ingin membeli buku ini sekarang. Ebook gratis dapat di akses di link berikut: https://www.researchgate.net/publication/312160783_Seri_Data_Science_Klasifikasi_dengan_Bahasa_Pemrograman_R.

Managing Technology In Higher Education

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Author : A. W. (Tony) Bates
language : en
Publisher: John Wiley & Sons
Release Date : 2011-04-08

Managing Technology In Higher Education written by A. W. (Tony) Bates and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-04-08 with Education categories.

Universities continue to struggle in their efforts to fully integrate information and communications technology within their activities. Based on examination of current practices in technology integration at 25 universities worldwide, this book argues for a radical approach to the management of technology in higher education. It offers recommendations for improving governance, strategic planning, integration of administrative and teaching services, management of digital resources, and training of technology managers and administrators. The book is written for anyone wanting to ensure technology is integrated as effectively and efficiently as possible.

What Every Engineer Should Know About Software Engineering

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Author : Philip A. Laplante
language : en
Publisher: CRC Press
Release Date : 2007-04-25

What Every Engineer Should Know About Software Engineering written by Philip A. Laplante and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-04-25 with Computers categories.

Do you Use a computer to perform analysis or simulations in your daily work? Write short scripts or record macros to perform repetitive tasks? Need to integrate off-the-shelf software into your systems or require multiple applications to work together? Find yourself spending too much time working the kink

Exploring Biological Diversity Environment And Local People S Perspectives In Forest Landscapes Methods For A Multidisciplinary Landscape Assessment

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Author : Douglas Sheil
language : id
Publisher: CIFOR
Release Date : 2002-01-01

Exploring Biological Diversity Environment And Local People S Perspectives In Forest Landscapes Methods For A Multidisciplinary Landscape Assessment written by Douglas Sheil and has been published by CIFOR this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-01-01 with Biodiversity categories.

Operational overview. Villages and communities. Field sample selection. Village-based activities. First community meeting. Community landscape mapping. Selecting local informants. Community-based data collections. Field-based activities. Site, vegetation and trees. Plants and site - ethnoecological data. Soil assessment. Data control and management. Plant taxonomy and verification. Database. Conclusiones.

Python Data Science Handbook

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Author : Jake VanderPlas
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-11-21

Python Data Science Handbook written by Jake VanderPlas and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-21 with Computers categories.

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

Data Lake Architecture

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Author : Bill Inmon
language : en
Publisher:
Release Date : 2016

Data Lake Architecture written by Bill Inmon and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Big data categories.

Data Lake Architecture will explain how to build a useful data lake, where data scientists and data analysts can solve business challenges and identify new business opportunities

Bookdown

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Author : Yihui Xie
language : en
Publisher: CRC Press
Release Date : 2016-12-12

Bookdown written by Yihui Xie and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-12 with Mathematics categories.

bookdown: Authoring Books and Technical Documents with R Markdown presents a much easier way to write books and technical publications than traditional tools such as LaTeX and Word. The bookdown package inherits the simplicity of syntax and flexibility for data analysis from R Markdown, and extends R Markdown for technical writing, so that you can make better use of document elements such as figures, tables, equations, theorems, citations, and references. Similar to LaTeX, you can number and cross-reference these elements with bookdown. Your document can even include live examples so readers can interact with them while reading the book. The book can be rendered to multiple output formats, including LaTeX/PDF, HTML, EPUB, and Word, thus making it easy to put your documents online. The style and theme of these output formats can be customized. We used books and R primarily for examples in this book, but bookdown is not only for books or R. Most features introduced in this book also apply to other types of publications: journal papers, reports, dissertations, course handouts, study notes, and even novels. You do not have to use R, either. Other choices of computing languages include Python, C, C++, SQL, Bash, Stan, JavaScript, and so on, although R is best supported. You can also leave out computing, for example, to write a fiction. This book itself is an example of publishing with bookdown and R Markdown, and its source is fully available on GitHub.

Electronic Commerce

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Author : Richard T. Watson
language : en
Publisher: Orange Grove Texts Plus
Release Date : 2009

Electronic Commerce written by Richard T. Watson and has been published by Orange Grove Texts Plus this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Electronic commerce categories.

This textbook provides a strategic marketing and managerial perspective of electronic commerce. The research of the four authors provides the basis for the book, allowing for first-hand experience, varied viewpoints, and relevance. Contents: 1) Electronic commerce: An introduction. 2) Electronic commerce technology. 3) Web strategy: Attracting and retaining visitors. 4) Promotion: Integrated Web communications. 5) Promotion & purchase: Measuring effectiveness. 6) Distribution. 7) Service. 8) Pricing. 9) Post-Modernism and the Web: Societal effects.

Text Mining With R

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Author : Julia Silge
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-06-12

Text Mining With R written by Julia Silge and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-12 with Computers categories.

Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective. The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You’ll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media. Learn how to apply the tidy text format to NLP Use sentiment analysis to mine the emotional content of text Identify a document’s most important terms with frequency measurements Explore relationships and connections between words with the ggraph and widyr packages Convert back and forth between R’s tidy and non-tidy text formats Use topic modeling to classify document collections into natural groups Examine case studies that compare Twitter archives, dig into NASA metadata, and analyze thousands of Usenet messages