Advanced data analytics using Python: with architectural patterns, text and image classification, and optimization techniques
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, an...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Apress
[2023]
|
| Ausgabe: | Second edition |
| Schlagworte: | |
| Online-Zugang: | FHD01 |
| Zusammenfassung: | 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 |
| Beschreibung: | 1 Online-Ressource (xvii, 249 Seiten) |
Internformat
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| 505 | 8 | |a Intro -- Table of Contents -- About the Authors -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: A Birds Eye View to AI System -- OOP in Python -- Calling Other Languages in Python -- Exposing the Python Model as a Microservice -- High-Performance API and Concurrent Programming -- Choosing the Right Database -- Summary -- Chapter 2: ETL with Python -- MySQL -- How to Install MySQLdb? -- Database Connection -- INSERT Operation -- READ Operation -- DELETE Operation -- UPDATE Operation -- COMMIT Operation -- ROLL-BACK Operation -- Normal Forms | |
| 505 | 8 | |a First Normal Form -- Second Normal Form -- Third Normal Form -- Elasticsearch -- Connection Layer API -- Neo4j Python Driver -- neo4j-rest-client -- In-Memory Database -- MongoDB (Python Edition) -- Import Data into the Collection -- Create a Connection Using pymongo -- Access Database Objects -- Insert Data -- Update Data -- Remove Data -- Cloud Databases -- Pandas -- ETL with Python (Unstructured Data) -- Email Parsing -- Topical Crawling -- Crawling Algorithms -- Summary -- Chapter 3: Feature Engineering and Supervised Learning -- Dimensionality Reduction with Python -- Correlation Analysis | |
| 505 | 8 | |a Principal Component Analysis -- Mutual Information -- Classifications with Python -- Semi-Supervised Learning -- Decision Tree -- Which Attribute Comes First? -- Random Forest Classifier -- Naïve Bayes Classifier -- Support Vector Machine -- Nearest Neighbor Classifier -- Sentiment Analysis -- Image Recognition -- Regression with Python -- Least Square Estimation -- Logistic Regression -- Classification and Regression -- Intentionally Bias the Model to Over-Fit or Under-Fit -- Dealing with Categorical Data -- Summary -- Chapter 4: Unsupervised Learning: Clustering -- K-Means Clustering | |
| 505 | 8 | |a Choosing K: The Elbow Method -- Silhouette Analysis -- Distance or Similarity Measure -- Properties -- General and Euclidean Distance -- Squared Euclidean Distance -- Distance Between String-Edit Distance -- Levenshtein Distance -- Needleman-Wunsch Algorithm -- Similarity in the Context of a Document -- Types of Similarity -- Example of K-Means in Images -- Preparing the Cluster -- Thresholding -- Time to Cluster -- Revealing the Current Cluster -- Hierarchical Clustering -- Bottom-Up Approach -- Distance Between Clusters -- Single Linkage Method -- Complete Linkage Method | |
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| contents | Intro -- Table of Contents -- About the Authors -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: A Birds Eye View to AI System -- OOP in Python -- Calling Other Languages in Python -- Exposing the Python Model as a Microservice -- High-Performance API and Concurrent Programming -- Choosing the Right Database -- Summary -- Chapter 2: ETL with Python -- MySQL -- How to Install MySQLdb? -- Database Connection -- INSERT Operation -- READ Operation -- DELETE Operation -- UPDATE Operation -- COMMIT Operation -- ROLL-BACK Operation -- Normal Forms First Normal Form -- Second Normal Form -- Third Normal Form -- Elasticsearch -- Connection Layer API -- Neo4j Python Driver -- neo4j-rest-client -- In-Memory Database -- MongoDB (Python Edition) -- Import Data into the Collection -- Create a Connection Using pymongo -- Access Database Objects -- Insert Data -- Update Data -- Remove Data -- Cloud Databases -- Pandas -- ETL with Python (Unstructured Data) -- Email Parsing -- Topical Crawling -- Crawling Algorithms -- Summary -- Chapter 3: Feature Engineering and Supervised Learning -- Dimensionality Reduction with Python -- Correlation Analysis Principal Component Analysis -- Mutual Information -- Classifications with Python -- Semi-Supervised Learning -- Decision Tree -- Which Attribute Comes First? -- Random Forest Classifier -- Naïve Bayes Classifier -- Support Vector Machine -- Nearest Neighbor Classifier -- Sentiment Analysis -- Image Recognition -- Regression with Python -- Least Square Estimation -- Logistic Regression -- Classification and Regression -- Intentionally Bias the Model to Over-Fit or Under-Fit -- Dealing with Categorical Data -- Summary -- Chapter 4: Unsupervised Learning: Clustering -- K-Means Clustering Choosing K: The Elbow Method -- Silhouette Analysis -- Distance or Similarity Measure -- Properties -- General and Euclidean Distance -- Squared Euclidean Distance -- Distance Between String-Edit Distance -- Levenshtein Distance -- Needleman-Wunsch Algorithm -- Similarity in the Context of a Document -- Types of Similarity -- Example of K-Means in Images -- Preparing the Cluster -- Thresholding -- Time to Cluster -- Revealing the Current Cluster -- Hierarchical Clustering -- Bottom-Up Approach -- Distance Between Clusters -- Single Linkage Method -- Complete Linkage Method |
| ctrlnum | (OCoLC)1355307470 (DE-599)BVBBV048640090 |
| edition | Second edition |
| format | Electronic eBook |
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| index_date | 2024-07-03T21:17:23Z |
| indexdate | 2024-07-10T09:44:48Z |
| institution | BVB |
| language | English |
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| physical | 1 Online-Ressource (xvii, 249 Seiten) |
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| publishDate | 2023 |
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| publisher | Apress |
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| spelling | Mukhopadhyay, Sayan Verfasser (DE-588)115695861X aut Advanced data analytics using Python with architectural patterns, text and image classification, and optimization techniques Sayan Mukhopadhyay, Pratip Samanta Second edition New York, NY Apress [2023] 1 Online-Ressource (xvii, 249 Seiten) txt rdacontent c rdamedia cr rdacarrier Intro -- Table of Contents -- About the Authors -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: A Birds Eye View to AI System -- OOP in Python -- Calling Other Languages in Python -- Exposing the Python Model as a Microservice -- High-Performance API and Concurrent Programming -- Choosing the Right Database -- Summary -- Chapter 2: ETL with Python -- MySQL -- How to Install MySQLdb? -- Database Connection -- INSERT Operation -- READ Operation -- DELETE Operation -- UPDATE Operation -- COMMIT Operation -- ROLL-BACK Operation -- Normal Forms First Normal Form -- Second Normal Form -- Third Normal Form -- Elasticsearch -- Connection Layer API -- Neo4j Python Driver -- neo4j-rest-client -- In-Memory Database -- MongoDB (Python Edition) -- Import Data into the Collection -- Create a Connection Using pymongo -- Access Database Objects -- Insert Data -- Update Data -- Remove Data -- Cloud Databases -- Pandas -- ETL with Python (Unstructured Data) -- Email Parsing -- Topical Crawling -- Crawling Algorithms -- Summary -- Chapter 3: Feature Engineering and Supervised Learning -- Dimensionality Reduction with Python -- Correlation Analysis Principal Component Analysis -- Mutual Information -- Classifications with Python -- Semi-Supervised Learning -- Decision Tree -- Which Attribute Comes First? -- Random Forest Classifier -- Naïve Bayes Classifier -- Support Vector Machine -- Nearest Neighbor Classifier -- Sentiment Analysis -- Image Recognition -- Regression with Python -- Least Square Estimation -- Logistic Regression -- Classification and Regression -- Intentionally Bias the Model to Over-Fit or Under-Fit -- Dealing with Categorical Data -- Summary -- Chapter 4: Unsupervised Learning: Clustering -- K-Means Clustering Choosing K: The Elbow Method -- Silhouette Analysis -- Distance or Similarity Measure -- Properties -- General and Euclidean Distance -- Squared Euclidean Distance -- Distance Between String-Edit Distance -- Levenshtein Distance -- Needleman-Wunsch Algorithm -- Similarity in the Context of a Document -- Types of Similarity -- Example of K-Means in Images -- Preparing the Cluster -- Thresholding -- Time to Cluster -- Revealing the Current Cluster -- Hierarchical Clustering -- Bottom-Up Approach -- Distance Between Clusters -- Single Linkage Method -- Complete Linkage Method 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 Python (Computer program language) Machine learning Data mining Data mining fast Machine learning fast Python (Computer program language) fast Samanta, Pratip Verfasser aut Erscheint auch als Druck-Ausgabe 978-1-4842-8004-1 |
| spellingShingle | Mukhopadhyay, Sayan Samanta, Pratip Advanced data analytics using Python with architectural patterns, text and image classification, and optimization techniques Intro -- Table of Contents -- About the Authors -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: A Birds Eye View to AI System -- OOP in Python -- Calling Other Languages in Python -- Exposing the Python Model as a Microservice -- High-Performance API and Concurrent Programming -- Choosing the Right Database -- Summary -- Chapter 2: ETL with Python -- MySQL -- How to Install MySQLdb? -- Database Connection -- INSERT Operation -- READ Operation -- DELETE Operation -- UPDATE Operation -- COMMIT Operation -- ROLL-BACK Operation -- Normal Forms First Normal Form -- Second Normal Form -- Third Normal Form -- Elasticsearch -- Connection Layer API -- Neo4j Python Driver -- neo4j-rest-client -- In-Memory Database -- MongoDB (Python Edition) -- Import Data into the Collection -- Create a Connection Using pymongo -- Access Database Objects -- Insert Data -- Update Data -- Remove Data -- Cloud Databases -- Pandas -- ETL with Python (Unstructured Data) -- Email Parsing -- Topical Crawling -- Crawling Algorithms -- Summary -- Chapter 3: Feature Engineering and Supervised Learning -- Dimensionality Reduction with Python -- Correlation Analysis Principal Component Analysis -- Mutual Information -- Classifications with Python -- Semi-Supervised Learning -- Decision Tree -- Which Attribute Comes First? -- Random Forest Classifier -- Naïve Bayes Classifier -- Support Vector Machine -- Nearest Neighbor Classifier -- Sentiment Analysis -- Image Recognition -- Regression with Python -- Least Square Estimation -- Logistic Regression -- Classification and Regression -- Intentionally Bias the Model to Over-Fit or Under-Fit -- Dealing with Categorical Data -- Summary -- Chapter 4: Unsupervised Learning: Clustering -- K-Means Clustering Choosing K: The Elbow Method -- Silhouette Analysis -- Distance or Similarity Measure -- Properties -- General and Euclidean Distance -- Squared Euclidean Distance -- Distance Between String-Edit Distance -- Levenshtein Distance -- Needleman-Wunsch Algorithm -- Similarity in the Context of a Document -- Types of Similarity -- Example of K-Means in Images -- Preparing the Cluster -- Thresholding -- Time to Cluster -- Revealing the Current Cluster -- Hierarchical Clustering -- Bottom-Up Approach -- Distance Between Clusters -- Single Linkage Method -- Complete Linkage Method Python (Computer program language) Machine learning Data mining Data mining fast Machine learning fast Python (Computer program language) fast |
| title | Advanced data analytics using Python with architectural patterns, text and image classification, and optimization techniques |
| title_auth | Advanced data analytics using Python with architectural patterns, text and image classification, and optimization techniques |
| title_exact_search | Advanced data analytics using Python with architectural patterns, text and image classification, and optimization techniques |
| title_exact_search_txtP | Advanced data analytics using Python with architectural patterns, text and image classification, and optimization techniques |
| title_full | Advanced data analytics using Python with architectural patterns, text and image classification, and optimization techniques Sayan Mukhopadhyay, Pratip Samanta |
| title_fullStr | Advanced data analytics using Python with architectural patterns, text and image classification, and optimization techniques Sayan Mukhopadhyay, Pratip Samanta |
| title_full_unstemmed | Advanced data analytics using Python with architectural patterns, text and image classification, and optimization techniques Sayan Mukhopadhyay, Pratip Samanta |
| title_short | Advanced data analytics using Python |
| title_sort | advanced data analytics using python with architectural patterns text and image classification and optimization techniques |
| title_sub | with architectural patterns, text and image classification, and optimization techniques |
| topic | Python (Computer program language) Machine learning Data mining Data mining fast Machine learning fast Python (Computer program language) fast |
| topic_facet | Python (Computer program language) Machine learning Data mining |
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