Advanced Data Analytics Using Python
With Machine Learning, Deep Learning and NLP Examples
Seiten
2018
|
1st ed.
Apress (Verlag)
978-1-4842-3449-5 (ISBN)
Apress (Verlag)
978-1-4842-3449-5 (ISBN)
- Lieferbar
- Versandkostenfrei
- Auch auf Rechnung
- Artikel merken
Zu diesem Artikel existiert eine Nachauflage
Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You’ll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis.
After reading this book you will have experience of every technical aspect of an analytics project. You’ll get to know the concepts using Python code, giving you samples to use in your own projects.
What You Will Learn
Work with data analysis techniques such as classification, clustering, regression, and forecasting
Handle structured and unstructured data, ETL techniques, and different kinds of databases such as Neo4j, Elasticsearch, MongoDB, and MySQL
Examine the different big data frameworks, including Hadoop and Spark
Discover advanced machine learning concepts such as semi-supervised learning, deep learning, and NLP
Who This Book Is For
Data scientists and software developers interested in the field of data analytics.
After reading this book you will have experience of every technical aspect of an analytics project. You’ll get to know the concepts using Python code, giving you samples to use in your own projects.
What You Will Learn
Work with data analysis techniques such as classification, clustering, regression, and forecasting
Handle structured and unstructured data, ETL techniques, and different kinds of databases such as Neo4j, Elasticsearch, MongoDB, and MySQL
Examine the different big data frameworks, including Hadoop and Spark
Discover advanced machine learning concepts such as semi-supervised learning, deep learning, and NLP
Who This Book Is For
Data scientists and software developers interested in the field of data analytics.
Sayan Mukhopadhyay in his 13+ years industry experience has been associated with companies such as Credit-Suisse, PayPal, CA Technology, CSC, and Mphasis. He has a deep understanding of the applications of data analysis in domains such as investment banking, online payments, online advertising, IT infrastructure, and retail. His area of expertise is applied high-performance computing in distributed and data-driven environments such as real-time analysis and high-frequency trading.
Chapter 1: Introduction
Chapter 2: ETL with Python
Chapter 3: Supervised Learning with Python
Chapter 4: Unsupervised Learning with Python
Chapter 5: Deep Learning & Neural Networks
Chapter 6: Time Series Analysis
Chapter 7: Python in Emerging Technologies
| Erscheinungsdatum | 07.04.2018 |
|---|---|
| Zusatzinfo | 18 Illustrations, black and white; XV, 186 p. 18 illus. |
| Verlagsort | Berkley |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 454 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Informatik ► Programmiersprachen / -werkzeuge ► Python | |
| Schlagworte | Analytics • Apache Spark • Deep learning • Elastic Search • Hadoop • machine learning • Neo4j • Python • Storm • Time Series |
| ISBN-10 | 1-4842-3449-9 / 1484234499 |
| ISBN-13 | 978-1-4842-3449-5 / 9781484234495 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
Mehr entdecken
aus dem Bereich
aus dem Bereich
eine praktische, projektbasierte Programmiereinführung
Buch | Softcover (2023)
dpunkt (Verlag)
CHF 45,95
Grundlagen und Praxis der Python-Programmierung
Buch | Softcover (2024)
O'Reilly (Verlag)
CHF 69,85
