Details
Advanced Data Analytics Using Python
With Architectural Patterns, Text and Image Classification, and Optimization Techniques2nd ed.
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46,99 € |
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| Verlag: | Apress |
| Format: | |
| Veröffentl.: | 25.11.2022 |
| ISBN/EAN: | 9781484280058 |
| Sprache: | englisch |
Dieses eBook enthält ein Wasserzeichen.
Beschreibungen
<div><div>Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark using Python. This book covers architectural patterns in data analytics, text and image classification, optimization techniques, natural language processing, and computer vision in the cloud environment.</div><div><br></div><div>Generic design patterns in Python programming is clearly explained, emphasizing architectural practices such as hot potato anti-patterns. You'll review recent advances in databases such as Neo4j, Elasticsearch, and MongoDB. You'll then study feature engineering in images and texts with implementing business logic and see how to build machine learning and deep learning models using transfer learning. </div><div><div><p><i>Advanced Analytics with Python, 2nd edition </i>features a chapter on clustering with a neural network, regularization techniques, and algorithmic design patterns in data analyticswith reinforcement learning. Finally, the recommender system in PySpark explains how to optimize models for a specific application. </p></div></div><div><b>What You'll Learn</b><br></div><div><div><ul><li><div>Build intelligent systems for enterprise</div></li><li><div>Review time series analysis, classifications, regression, and clustering</div></li><li><div>Explore supervised learning, unsupervised learning, reinforcement learning, and transfer learning </div></li><li><div>Use cloud platforms like GCP and AWS in data analytics</div></li><li><div>Understand Covers design patterns in Python </div></li></ul></div></div><div><b>Who This Book Is For</b><br></div></div><div><b><br></b></div><div>Data scientists and software developers interested in the field of data analytics.</div>
<div><div><div>Chapter 1: Overview of Python Language.- Chapter 2: ETL with Python.- Chapter 3: Supervised Learning and Unsupervised Learning with Python.- Chapter 4: Clustering with Python.- Chapter 5: Deep Learning & Neural Networks.- Chapter 6: Time Series Analysis.- Chapter 7: Analytics in Scale.</div></div></div>
<div><b>Sayan Mukhopadhyay</b> is a data scientist with more than 13 years of experience. He has been associated with companies such as Credit-Suisse, PayPal, CA Technology, CSC, and Mphasis. He has a deep understanding of data analysis applications 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.</div><div><br></div><div><b>Pratip Samanta</b> is a Principal AI engineer/researcher having more than 11 years of experience. He worked in different software companies and research institutions. He has published conference papers and granted patents in AI and Natural Language Processing. He is also passionate about gardening and teaching. <br></div>
<div><div>Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark using Python. This book covers architectural patterns in data analytics, text and image classification, optimization techniques, natural language processing, and computer vision in the cloud environment.</div><div><br></div><div>Generic design patterns in Python programming is clearly explained, emphasizing architectural practices such as hot potato anti-patterns. You'll review recent advances in databases such as Neo4j, Elasticsearch, and MongoDB. You'll then study feature engineering in images and texts with implementing business logic and see how to build machine learning and deep learning models using transfer learning. </div><div><p><i>Advanced Analytics with Python, 2nd edition </i>features a chapter on clustering with a neural network, regularization techniques, and algorithmic design patterns in data analytics withreinforcement learning. Finally, the recommender system in PySpark explains how to optimize models for a specific application. </p></div></div><div>You will:<br></div><div><ul><li>Build intelligent systems for enterprise</li><li>Review time series analysis, classifications, regression, and clustering</li><li>Explore supervised learning, unsupervised learning, reinforcement learning, and transfer learning </li><li>Use cloud platforms like GCP and AWS in data analytics</li><li>Understand Covers design patterns in Python </li></ul></div>
Explains recommendation system, algorithm trading, and PySpark with use cases and hands-on coding Explains feature engineering in images and texts with Python Covers recent advances in databases such as Neo4j, Elasticsearch, and MongoDB
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