Advanced Data Analytics Using Python : With Architectural Patterns, Text and Image Classification, and Optimization Techniques / / by Sayan Mukhopadhyay, Pratip Samanta
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| Autore: |
Mukhopadhyay Sayan
|
| Titolo: |
Advanced Data Analytics Using Python : With Architectural Patterns, Text and Image Classification, and Optimization Techniques / / by Sayan Mukhopadhyay, Pratip Samanta
|
| Pubblicazione: | Berkeley, CA : , : Apress : , : Imprint : Apress, , 2023 |
| Edizione: | 2nd ed. 2023. |
| Descrizione fisica: | 1 online resource (259 pages) |
| Disciplina: | 006.312 |
| Soggetto topico: | Python (Computer program language) |
| Machine learning | |
| Data mining | |
| Persona (resp. second.): | SamantaPratip |
| Note generali: | Includes index. |
| Nota di contenuto: | 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. |
| Sommario/riassunto: | 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. 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. Advanced Analytics with Python, 2nd edition features a chapter on clustering with a neural network, regularization techniques, and algorithmic design patterns in data analytics with reinforcement learning. Finally, the recommender system in PySpark explains how to optimize models for a specific application. You will: Build intelligent systems for enterprise Review time series analysis, classifications, regression, and clustering Explore supervised learning, unsupervised learning, reinforcement learning, and transfer learning Use cloud platforms like GCP and AWS in data analytics Understand Covers design patterns in Python . |
| Titolo autorizzato: | Advanced Data Analytics Using Python |
| ISBN: | 1-4842-8005-9 |
| Formato: | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9910632475203321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |