|
|
|
|
| LEADER |
02506nmm a2200313 u 4500 |
| 001 |
EB001800829 |
| 003 |
EBX01000000000000000974327 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
180405 ||| eng |
| 020 |
|
|
|a 9781484234501
|
| 100 |
1 |
|
|a Mukhopadhyay, Sayan
|
| 245 |
0 |
0 |
|a Advanced Data Analytics Using Python
|h Elektronische Ressource
|b With Machine Learning, Deep Learning and NLP Examples
|c by Sayan Mukhopadhyay
|
| 250 |
|
|
|a 1st ed. 2018
|
| 260 |
|
|
|a Berkeley, CA
|b Apress
|c 2018, 2018
|
| 300 |
|
|
|a XV, 186 p. 18 illus
|b online resource
|
| 505 |
0 |
|
|a 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
|
| 653 |
|
|
|a Big data
|
| 653 |
|
|
|a Open source software
|
| 653 |
|
|
|a Python
|
| 653 |
|
|
|a Big Data
|
| 653 |
|
|
|a Open Source
|
| 653 |
|
|
|a Python (Computer program language)
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b Springer
|a Springer eBooks 2005-
|
| 028 |
5 |
0 |
|a 10.1007/978-1-4842-3450-1
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-1-4842-3450-1?nosfx=y
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 005.133
|
| 520 |
|
|
|a 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. You will: 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.
|