Please use this identifier to cite or link to this item:
http://localhost/handle/Hannan/1398
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Mukhopadhyay, Sayan. ; | en_US |
dc.date.accessioned | 2013 | en_US |
dc.date.accessioned | 2020-05-17T08:28:28Z | - |
dc.date.available | 2020-05-17T08:28:28Z | - |
dc.date.issued | 2018 | en_US |
dc.identifier.isbn | 9781484234501 ; | en_US |
dc.identifier.isbn | 9781484234495 (print) ; | en_US |
dc.identifier.uri | http://localhost/handle/Hannan/1398 | - |
dc.description | en_US | |
dc.description | SpringerLink (Online service) ; | en_US |
dc.description | en_US | |
dc.description | en_US | |
dc.description | QA76 | en_US |
dc.description | Printed edition: ; 9781484234495. ; | en_US |
dc.description | en_US | |
dc.description | en_US | |
dc.description | en_US | |
dc.description.abstract | 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. Youeell also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP.eAdvanced Data Analytics Using Pythonealso covers important traditional data analysis techniques such as time series and principal component analysis.e After reading this book you will have experience of every technical aspect of an analytics project. Youeell 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. ; | en_US |
dc.description.statementofresponsibility | by Sayan Mukhopadhyay. | en_US |
dc.description.tableofcontents | 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. ; | en_US |
dc.format.extent | XV, 186 p. 18 illus. ; online resource. ; | en_US |
dc.publisher | Apress : | en_US |
dc.publisher | Imprint: Apress, | en_US |
dc.relation.haspart | 9781484234495.pdf | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Python. ; | en_US |
dc.subject | Big Data. ; | en_US |
dc.subject | Open Source. ; | en_US |
dc.title | Advanced Data Analytics Using Python | en_US |
dc.title.alternative | With Machine Learning, Deep Learning and NLP Examples / | en_US |
dc.type | Book | en_US |
dc.publisher.place | Berkeley, CA : | en_US |
Appears in Collections: | مدیریت فناوری اطلاعات |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
9781484234495.pdf | 2.24 MB | Adobe PDF | Preview File |
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Mukhopadhyay, Sayan. ; | en_US |
dc.date.accessioned | 2013 | en_US |
dc.date.accessioned | 2020-05-17T08:28:28Z | - |
dc.date.available | 2020-05-17T08:28:28Z | - |
dc.date.issued | 2018 | en_US |
dc.identifier.isbn | 9781484234501 ; | en_US |
dc.identifier.isbn | 9781484234495 (print) ; | en_US |
dc.identifier.uri | http://localhost/handle/Hannan/1398 | - |
dc.description | en_US | |
dc.description | SpringerLink (Online service) ; | en_US |
dc.description | en_US | |
dc.description | en_US | |
dc.description | QA76 | en_US |
dc.description | Printed edition: ; 9781484234495. ; | en_US |
dc.description | en_US | |
dc.description | en_US | |
dc.description | en_US | |
dc.description.abstract | 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. Youeell also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP.eAdvanced Data Analytics Using Pythonealso covers important traditional data analysis techniques such as time series and principal component analysis.e After reading this book you will have experience of every technical aspect of an analytics project. Youeell 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. ; | en_US |
dc.description.statementofresponsibility | by Sayan Mukhopadhyay. | en_US |
dc.description.tableofcontents | 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. ; | en_US |
dc.format.extent | XV, 186 p. 18 illus. ; online resource. ; | en_US |
dc.publisher | Apress : | en_US |
dc.publisher | Imprint: Apress, | en_US |
dc.relation.haspart | 9781484234495.pdf | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Python. ; | en_US |
dc.subject | Big Data. ; | en_US |
dc.subject | Open Source. ; | en_US |
dc.title | Advanced Data Analytics Using Python | en_US |
dc.title.alternative | With Machine Learning, Deep Learning and NLP Examples / | en_US |
dc.type | Book | en_US |
dc.publisher.place | Berkeley, CA : | en_US |
Appears in Collections: | مدیریت فناوری اطلاعات |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
9781484234495.pdf | 2.24 MB | Adobe PDF | Preview File |
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Mukhopadhyay, Sayan. ; | en_US |
dc.date.accessioned | 2013 | en_US |
dc.date.accessioned | 2020-05-17T08:28:28Z | - |
dc.date.available | 2020-05-17T08:28:28Z | - |
dc.date.issued | 2018 | en_US |
dc.identifier.isbn | 9781484234501 ; | en_US |
dc.identifier.isbn | 9781484234495 (print) ; | en_US |
dc.identifier.uri | http://localhost/handle/Hannan/1398 | - |
dc.description | en_US | |
dc.description | SpringerLink (Online service) ; | en_US |
dc.description | en_US | |
dc.description | en_US | |
dc.description | QA76 | en_US |
dc.description | Printed edition: ; 9781484234495. ; | en_US |
dc.description | en_US | |
dc.description | en_US | |
dc.description | en_US | |
dc.description.abstract | 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. Youeell also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP.eAdvanced Data Analytics Using Pythonealso covers important traditional data analysis techniques such as time series and principal component analysis.e After reading this book you will have experience of every technical aspect of an analytics project. Youeell 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. ; | en_US |
dc.description.statementofresponsibility | by Sayan Mukhopadhyay. | en_US |
dc.description.tableofcontents | 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. ; | en_US |
dc.format.extent | XV, 186 p. 18 illus. ; online resource. ; | en_US |
dc.publisher | Apress : | en_US |
dc.publisher | Imprint: Apress, | en_US |
dc.relation.haspart | 9781484234495.pdf | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Python. ; | en_US |
dc.subject | Big Data. ; | en_US |
dc.subject | Open Source. ; | en_US |
dc.title | Advanced Data Analytics Using Python | en_US |
dc.title.alternative | With Machine Learning, Deep Learning and NLP Examples / | en_US |
dc.type | Book | en_US |
dc.publisher.place | Berkeley, CA : | en_US |
Appears in Collections: | مدیریت فناوری اطلاعات |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
9781484234495.pdf | 2.24 MB | Adobe PDF | Preview File |