Langkah Mudah Belajar Membuat Aplikasi Data Interaktif Dengan Streamlit Python Untuk Pemula
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Langkah Mudah Belajar Membuat Aplikasi Data Interaktif Dengan Streamlit Python Untuk Pemula
Author: Randi Adrika Putra
language: id
Publisher: Anak Hebat Indonesia
Release Date: 2024-11-12
Streamlit merupakan suatu framework open-source yang dirancang khusus untuk membangun aplikasi web data interaktif dengan menggunakan Python. Sejak pertama kali dirilis, Streamlit telah menjadi pilihan populer di kalangan data scientist, analis data, dan developer yang ingin membuat aplikasi data-driven tanpa perlu menjadi ahli dalam pengembangan web. Melalui buku ini, Anda akan belajar membuat aplikasi data interaktif menggunakan Streamlit Python dengan mudah dan cepat. Buku ini akan membahasa mengenai berbagai hal, mulai dari berkenalan dengan aplikasi data interaktif, dasar-dasar bahasa pemrograman Python, menuliskan kode Streamlit, melakukan analisis data dengan Streamlit, hingga contoh studi kasus membuat aplikasi dashboard interaktif untuk analisis data penjualan menggunakan Streamlit. Nah, bagi Anda yang tertarik belajar membuat aplikasi data interaktif menggunakan Streamlit Python dengan mudah dan cepat, Anda bisa belajar melalui buku ini!
Introduction to Machine Learning with Python
Author: Andreas C. Müller
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2016-09-26
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
Big Data, Cloud Computing, Data Science & Engineering
This book presents the outcomes of the 3rd IEEE/ACIS International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD 2018), which was held on July 10–12, 2018 in Kanazawa. The aim of the conference was to bring together researchers and scientists, businesspeople and entrepreneurs, teachers, engineers, computer users, and students to discuss the various fields of computer science, to share their experiences, and to exchange new ideas and information in a meaningful way. All aspects (theory, applications and tools) of computer and information science, the practical challenges encountered along the way, and the solutions adopted to solve them are all explored here. The conference organizers selected the best papers from among those accepted for presentation. The papers were chosen on the basis of review scores submitted by members of the program committee and subsequently underwent further rigorous review. Following this second round of review, 13 of the conference’s most promising papers were selected for this Springer (SCI) book. We eagerly await the important contributions that we know these authors will make to the field of computer and information science.