Practitioner's Guide to Data Science:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Delhi
BPB Publications
2022
|
| Ausgabe: | 1st ed |
| Schlagworte: | |
| Online-Zugang: | HWR01 |
| Beschreibung: | 1 Online-Ressource (223 Seiten) |
| ISBN: | 9789391392956 9789391392871 |
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| 245 | 1 | 0 | |a Practitioner's Guide to Data Science |
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| 505 | 8 | |a Cover Page -- Title Page -- Copyright Page -- Foreword -- Dedication Page -- About the Author -- About the Reviewer -- Acknowledgement -- Preface -- Errata -- Table of Contents -- 1. Data Science for Business -- Structure -- Objectives -- Application programmer to Data Science professional -- What is Data Science? -- The unprecedented scope of Data Science -- Data Science application -- Big Data, DM, ML, DL, AI, and Data Science -- Legal, ethical, and security aspects of Data Science -- Methodology used in organizing this book -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 2. Data Science Project Methodologies and Team Processes -- Structure -- Objectives -- What is a process and its importance? -- Data Science from a process perspective -- Software engineering and Data Science -- Data Science project methodologies and processes -- Knowledge Discovery in Databases -- CCC Big Data pipeline -- CRoss-Industry Standard Process for Data Mining -- Domino's Data Science Life Cycle -- Microsoft's Team Data Science Process -- Data Science lifecycle -- Standardized project structure -- Infrastructure and resources -- Tools and utilities -- Sample, Explore, Modify, Model, and Assess -- Data-Driven Scrum (DDS) -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 3. Business Understanding and Its Data Landscape -- Structure -- Objectives -- What is involved in business understanding? -- CRISP-DM guidelines -- Microsoft TDSP guidelines -- Business problem types and Data Science solutions -- Reliability and validity of business data -- Hands-on use case -- Project charter -- Business background -- Project scope -- Project team -- Evaluation metrics -- Project plan -- Solution architecture -- Communication plan -- Data sources -- Data dictionary | |
| 505 | 8 | |a Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 4. Acquire, Explore, and Analyze Data -- Structure -- Objectives -- Development environment options -- Guidelines for data acquisition and understanding -- CRISP-DM -- Microsoft TDSP -- Data acquisition and sampling -- Essential considerations -- Use case data -- Down-sampling the use case data -- Down-sampling for rate spread use case -- Data exploration and visualization -- Essential considerations -- Explore and visualize HMDA use case data -- HMDA use case data distribution -- Data relations (bivariate) -- Categorical variables -- Data relations (multivariate) -- Data quality report and decision checkpoint -- Data quality -- Decision checkpoint -- Conclusion -- Points to remember -- Multiple choice question -- Answers -- Questions -- Key terms -- 5. Pre-processing and Preparing Data -- Structure -- Objectives -- Guidelines for data preparation -- CRISP-DM for data preparation -- Selection of data -- Cleaning of data -- Construction of data -- Integration of data -- Data formatting -- Microsoft TDSP for data preparation -- Data pre-processing concept -- Data health screening -- Data pre-processing major tasks -- Feature engineering -- Data pre-processing and cleaning -- Feature engineering -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 6. Developing a Machine Learning Model -- Structure -- Objectives -- Guidelines for model development -- CRISP-DM -- Selection of modeling technique -- Generation of test design -- Model building -- Model assessment -- Microsoft TDSP -- Goals -- Tasks -- Deliverables -- Modeling algorithms and evaluation -- What is a model? -- How to choose an algorithm? -- Metrics for model evaluation -- Classification metrics -- Regression metrics | |
| 505 | 8 | |a Model development procedure -- Modeling for HMDA use case -- Choosing an algorithm -- Modeling scenario-1 -- Modeling scenario-2 -- Model tuning -- Feature selection -- Dimensionality reduction -- Cross-validation -- Regularization -- Bagging and boosting -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 7. Lap Around Azure ML Service -- Structure -- Objectives -- Azure ML Service overview -- Architecture and key concepts -- Workspace -- Compute -- Managed compute -- Un-managed compute -- Datasets and datastores -- Environments -- Experiments -- Runs -- Run configurations -- Snapshots -- Pipelines -- Models -- Model registry -- Deployment -- Endpoints -- Web service endpoint -- IoT module endpoints -- Getting started: signup and provisioning -- AutoML in Azure ML Service -- Model development with Azure ML Service -- Azure ML Designer -- AutoML using ML Studio UI -- AutoML using Python SDK -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 8. Deploying and Managing Models -- Structure -- Objectives -- Guidelines for deployment and evaluation -- CRISP-DM -- Microsoft TDSP -- Model lifecycle management -- Model lifecycle using Azure ML SDK -- Training the model -- Registering model -- Deploying the model -- Testing/consuming deployed model -- Retraining a model -- Model lifecycle using Azure ML Studio UI -- MLOps with Azure Pipelines -- Pre-requisites -- Azure DevOps project -- Project repository -- Azure Subscription -- Azure Service connection -- Creating a build pipeline -- Creating a release pipeline -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- Index | |
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| author | Mirza, Nasir Ali |
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| contents | Cover Page -- Title Page -- Copyright Page -- Foreword -- Dedication Page -- About the Author -- About the Reviewer -- Acknowledgement -- Preface -- Errata -- Table of Contents -- 1. Data Science for Business -- Structure -- Objectives -- Application programmer to Data Science professional -- What is Data Science? -- The unprecedented scope of Data Science -- Data Science application -- Big Data, DM, ML, DL, AI, and Data Science -- Legal, ethical, and security aspects of Data Science -- Methodology used in organizing this book -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 2. Data Science Project Methodologies and Team Processes -- Structure -- Objectives -- What is a process and its importance? -- Data Science from a process perspective -- Software engineering and Data Science -- Data Science project methodologies and processes -- Knowledge Discovery in Databases -- CCC Big Data pipeline -- CRoss-Industry Standard Process for Data Mining -- Domino's Data Science Life Cycle -- Microsoft's Team Data Science Process -- Data Science lifecycle -- Standardized project structure -- Infrastructure and resources -- Tools and utilities -- Sample, Explore, Modify, Model, and Assess -- Data-Driven Scrum (DDS) -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 3. Business Understanding and Its Data Landscape -- Structure -- Objectives -- What is involved in business understanding? -- CRISP-DM guidelines -- Microsoft TDSP guidelines -- Business problem types and Data Science solutions -- Reliability and validity of business data -- Hands-on use case -- Project charter -- Business background -- Project scope -- Project team -- Evaluation metrics -- Project plan -- Solution architecture -- Communication plan -- Data sources -- Data dictionary Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 4. Acquire, Explore, and Analyze Data -- Structure -- Objectives -- Development environment options -- Guidelines for data acquisition and understanding -- CRISP-DM -- Microsoft TDSP -- Data acquisition and sampling -- Essential considerations -- Use case data -- Down-sampling the use case data -- Down-sampling for rate spread use case -- Data exploration and visualization -- Essential considerations -- Explore and visualize HMDA use case data -- HMDA use case data distribution -- Data relations (bivariate) -- Categorical variables -- Data relations (multivariate) -- Data quality report and decision checkpoint -- Data quality -- Decision checkpoint -- Conclusion -- Points to remember -- Multiple choice question -- Answers -- Questions -- Key terms -- 5. Pre-processing and Preparing Data -- Structure -- Objectives -- Guidelines for data preparation -- CRISP-DM for data preparation -- Selection of data -- Cleaning of data -- Construction of data -- Integration of data -- Data formatting -- Microsoft TDSP for data preparation -- Data pre-processing concept -- Data health screening -- Data pre-processing major tasks -- Feature engineering -- Data pre-processing and cleaning -- Feature engineering -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 6. Developing a Machine Learning Model -- Structure -- Objectives -- Guidelines for model development -- CRISP-DM -- Selection of modeling technique -- Generation of test design -- Model building -- Model assessment -- Microsoft TDSP -- Goals -- Tasks -- Deliverables -- Modeling algorithms and evaluation -- What is a model? -- How to choose an algorithm? -- Metrics for model evaluation -- Classification metrics -- Regression metrics Model development procedure -- Modeling for HMDA use case -- Choosing an algorithm -- Modeling scenario-1 -- Modeling scenario-2 -- Model tuning -- Feature selection -- Dimensionality reduction -- Cross-validation -- Regularization -- Bagging and boosting -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 7. Lap Around Azure ML Service -- Structure -- Objectives -- Azure ML Service overview -- Architecture and key concepts -- Workspace -- Compute -- Managed compute -- Un-managed compute -- Datasets and datastores -- Environments -- Experiments -- Runs -- Run configurations -- Snapshots -- Pipelines -- Models -- Model registry -- Deployment -- Endpoints -- Web service endpoint -- IoT module endpoints -- Getting started: signup and provisioning -- AutoML in Azure ML Service -- Model development with Azure ML Service -- Azure ML Designer -- AutoML using ML Studio UI -- AutoML using Python SDK -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 8. Deploying and Managing Models -- Structure -- Objectives -- Guidelines for deployment and evaluation -- CRISP-DM -- Microsoft TDSP -- Model lifecycle management -- Model lifecycle using Azure ML SDK -- Training the model -- Registering model -- Deploying the model -- Testing/consuming deployed model -- Retraining a model -- Model lifecycle using Azure ML Studio UI -- MLOps with Azure Pipelines -- Pre-requisites -- Azure DevOps project -- Project repository -- Azure Subscription -- Azure Service connection -- Creating a build pipeline -- Creating a release pipeline -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- Index |
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| edition | 1st ed |
| format | Electronic eBook |
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Data Science for Business -- Structure -- Objectives -- Application programmer to Data Science professional -- What is Data Science? -- The unprecedented scope of Data Science -- Data Science application -- Big Data, DM, ML, DL, AI, and Data Science -- Legal, ethical, and security aspects of Data Science -- Methodology used in organizing this book -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 2. Data Science Project Methodologies and Team Processes -- Structure -- Objectives -- What is a process and its importance? -- Data Science from a process perspective -- Software engineering and Data Science -- Data Science project methodologies and processes -- Knowledge Discovery in Databases -- CCC Big Data pipeline -- CRoss-Industry Standard Process for Data Mining -- Domino's Data Science Life Cycle -- Microsoft's Team Data Science Process -- Data Science lifecycle -- Standardized project structure -- Infrastructure and resources -- Tools and utilities -- Sample, Explore, Modify, Model, and Assess -- Data-Driven Scrum (DDS) -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 3. Business Understanding and Its Data Landscape -- Structure -- Objectives -- What is involved in business understanding? -- CRISP-DM guidelines -- Microsoft TDSP guidelines -- Business problem types and Data Science solutions -- Reliability and validity of business data -- Hands-on use case -- Project charter -- Business background -- Project scope -- Project team -- Evaluation metrics -- Project plan -- Solution architecture -- Communication plan -- Data sources -- Data dictionary</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 4. Acquire, Explore, and Analyze Data -- Structure -- Objectives -- Development environment options -- Guidelines for data acquisition and understanding -- CRISP-DM -- Microsoft TDSP -- Data acquisition and sampling -- Essential considerations -- Use case data -- Down-sampling the use case data -- Down-sampling for rate spread use case -- Data exploration and visualization -- Essential considerations -- Explore and visualize HMDA use case data -- HMDA use case data distribution -- Data relations (bivariate) -- Categorical variables -- Data relations (multivariate) -- Data quality report and decision checkpoint -- Data quality -- Decision checkpoint -- Conclusion -- Points to remember -- Multiple choice question -- Answers -- Questions -- Key terms -- 5. Pre-processing and Preparing Data -- Structure -- Objectives -- Guidelines for data preparation -- CRISP-DM for data preparation -- Selection of data -- Cleaning of data -- Construction of data -- Integration of data -- Data formatting -- Microsoft TDSP for data preparation -- Data pre-processing concept -- Data health screening -- Data pre-processing major tasks -- Feature engineering -- Data pre-processing and cleaning -- Feature engineering -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 6. Developing a Machine Learning Model -- Structure -- Objectives -- Guidelines for model development -- CRISP-DM -- Selection of modeling technique -- Generation of test design -- Model building -- Model assessment -- Microsoft TDSP -- Goals -- Tasks -- Deliverables -- Modeling algorithms and evaluation -- What is a model? -- How to choose an algorithm? -- Metrics for model evaluation -- Classification metrics -- Regression metrics</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Model development procedure -- Modeling for HMDA use case -- Choosing an algorithm -- Modeling scenario-1 -- Modeling scenario-2 -- Model tuning -- Feature selection -- Dimensionality reduction -- Cross-validation -- Regularization -- Bagging and boosting -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 7. Lap Around Azure ML Service -- Structure -- Objectives -- Azure ML Service overview -- Architecture and key concepts -- Workspace -- Compute -- Managed compute -- Un-managed compute -- Datasets and datastores -- Environments -- Experiments -- Runs -- Run configurations -- Snapshots -- Pipelines -- Models -- Model registry -- Deployment -- Endpoints -- Web service endpoint -- IoT module endpoints -- Getting started: signup and provisioning -- AutoML in Azure ML Service -- Model development with Azure ML Service -- Azure ML Designer -- AutoML using ML Studio UI -- AutoML using Python SDK -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 8. 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| id | DE-604.BV048631945 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T21:16:05Z |
| indexdate | 2024-07-10T09:44:32Z |
| institution | BVB |
| isbn | 9789391392956 9789391392871 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034006965 |
| oclc_num | 1302009956 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (223 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | BPB Publications |
| record_format | marc |
| spelling | Mirza, Nasir Ali Verfasser aut Practitioner's Guide to Data Science 1st ed Delhi BPB Publications 2022 ©2022 1 Online-Ressource (223 Seiten) txt rdacontent c rdamedia cr rdacarrier Cover Page -- Title Page -- Copyright Page -- Foreword -- Dedication Page -- About the Author -- About the Reviewer -- Acknowledgement -- Preface -- Errata -- Table of Contents -- 1. Data Science for Business -- Structure -- Objectives -- Application programmer to Data Science professional -- What is Data Science? -- The unprecedented scope of Data Science -- Data Science application -- Big Data, DM, ML, DL, AI, and Data Science -- Legal, ethical, and security aspects of Data Science -- Methodology used in organizing this book -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 2. Data Science Project Methodologies and Team Processes -- Structure -- Objectives -- What is a process and its importance? -- Data Science from a process perspective -- Software engineering and Data Science -- Data Science project methodologies and processes -- Knowledge Discovery in Databases -- CCC Big Data pipeline -- CRoss-Industry Standard Process for Data Mining -- Domino's Data Science Life Cycle -- Microsoft's Team Data Science Process -- Data Science lifecycle -- Standardized project structure -- Infrastructure and resources -- Tools and utilities -- Sample, Explore, Modify, Model, and Assess -- Data-Driven Scrum (DDS) -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 3. Business Understanding and Its Data Landscape -- Structure -- Objectives -- What is involved in business understanding? -- CRISP-DM guidelines -- Microsoft TDSP guidelines -- Business problem types and Data Science solutions -- Reliability and validity of business data -- Hands-on use case -- Project charter -- Business background -- Project scope -- Project team -- Evaluation metrics -- Project plan -- Solution architecture -- Communication plan -- Data sources -- Data dictionary Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 4. Acquire, Explore, and Analyze Data -- Structure -- Objectives -- Development environment options -- Guidelines for data acquisition and understanding -- CRISP-DM -- Microsoft TDSP -- Data acquisition and sampling -- Essential considerations -- Use case data -- Down-sampling the use case data -- Down-sampling for rate spread use case -- Data exploration and visualization -- Essential considerations -- Explore and visualize HMDA use case data -- HMDA use case data distribution -- Data relations (bivariate) -- Categorical variables -- Data relations (multivariate) -- Data quality report and decision checkpoint -- Data quality -- Decision checkpoint -- Conclusion -- Points to remember -- Multiple choice question -- Answers -- Questions -- Key terms -- 5. Pre-processing and Preparing Data -- Structure -- Objectives -- Guidelines for data preparation -- CRISP-DM for data preparation -- Selection of data -- Cleaning of data -- Construction of data -- Integration of data -- Data formatting -- Microsoft TDSP for data preparation -- Data pre-processing concept -- Data health screening -- Data pre-processing major tasks -- Feature engineering -- Data pre-processing and cleaning -- Feature engineering -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 6. Developing a Machine Learning Model -- Structure -- Objectives -- Guidelines for model development -- CRISP-DM -- Selection of modeling technique -- Generation of test design -- Model building -- Model assessment -- Microsoft TDSP -- Goals -- Tasks -- Deliverables -- Modeling algorithms and evaluation -- What is a model? -- How to choose an algorithm? -- Metrics for model evaluation -- Classification metrics -- Regression metrics Model development procedure -- Modeling for HMDA use case -- Choosing an algorithm -- Modeling scenario-1 -- Modeling scenario-2 -- Model tuning -- Feature selection -- Dimensionality reduction -- Cross-validation -- Regularization -- Bagging and boosting -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 7. Lap Around Azure ML Service -- Structure -- Objectives -- Azure ML Service overview -- Architecture and key concepts -- Workspace -- Compute -- Managed compute -- Un-managed compute -- Datasets and datastores -- Environments -- Experiments -- Runs -- Run configurations -- Snapshots -- Pipelines -- Models -- Model registry -- Deployment -- Endpoints -- Web service endpoint -- IoT module endpoints -- Getting started: signup and provisioning -- AutoML in Azure ML Service -- Model development with Azure ML Service -- Azure ML Designer -- AutoML using ML Studio UI -- AutoML using Python SDK -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 8. Deploying and Managing Models -- Structure -- Objectives -- Guidelines for deployment and evaluation -- CRISP-DM -- Microsoft TDSP -- Model lifecycle management -- Model lifecycle using Azure ML SDK -- Training the model -- Registering model -- Deploying the model -- Testing/consuming deployed model -- Retraining a model -- Model lifecycle using Azure ML Studio UI -- MLOps with Azure Pipelines -- Pre-requisites -- Azure DevOps project -- Project repository -- Azure Subscription -- Azure Service connection -- Creating a build pipeline -- Creating a release pipeline -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- Index Big data Electronic books Erscheint auch als Druck-Ausgabe Mirza, Nasir Ali Practitioner's Guide to Data Science Delhi : BPB Publications,c2022 9789391392871 |
| spellingShingle | Mirza, Nasir Ali Practitioner's Guide to Data Science Cover Page -- Title Page -- Copyright Page -- Foreword -- Dedication Page -- About the Author -- About the Reviewer -- Acknowledgement -- Preface -- Errata -- Table of Contents -- 1. Data Science for Business -- Structure -- Objectives -- Application programmer to Data Science professional -- What is Data Science? -- The unprecedented scope of Data Science -- Data Science application -- Big Data, DM, ML, DL, AI, and Data Science -- Legal, ethical, and security aspects of Data Science -- Methodology used in organizing this book -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 2. Data Science Project Methodologies and Team Processes -- Structure -- Objectives -- What is a process and its importance? -- Data Science from a process perspective -- Software engineering and Data Science -- Data Science project methodologies and processes -- Knowledge Discovery in Databases -- CCC Big Data pipeline -- CRoss-Industry Standard Process for Data Mining -- Domino's Data Science Life Cycle -- Microsoft's Team Data Science Process -- Data Science lifecycle -- Standardized project structure -- Infrastructure and resources -- Tools and utilities -- Sample, Explore, Modify, Model, and Assess -- Data-Driven Scrum (DDS) -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 3. Business Understanding and Its Data Landscape -- Structure -- Objectives -- What is involved in business understanding? -- CRISP-DM guidelines -- Microsoft TDSP guidelines -- Business problem types and Data Science solutions -- Reliability and validity of business data -- Hands-on use case -- Project charter -- Business background -- Project scope -- Project team -- Evaluation metrics -- Project plan -- Solution architecture -- Communication plan -- Data sources -- Data dictionary Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 4. Acquire, Explore, and Analyze Data -- Structure -- Objectives -- Development environment options -- Guidelines for data acquisition and understanding -- CRISP-DM -- Microsoft TDSP -- Data acquisition and sampling -- Essential considerations -- Use case data -- Down-sampling the use case data -- Down-sampling for rate spread use case -- Data exploration and visualization -- Essential considerations -- Explore and visualize HMDA use case data -- HMDA use case data distribution -- Data relations (bivariate) -- Categorical variables -- Data relations (multivariate) -- Data quality report and decision checkpoint -- Data quality -- Decision checkpoint -- Conclusion -- Points to remember -- Multiple choice question -- Answers -- Questions -- Key terms -- 5. Pre-processing and Preparing Data -- Structure -- Objectives -- Guidelines for data preparation -- CRISP-DM for data preparation -- Selection of data -- Cleaning of data -- Construction of data -- Integration of data -- Data formatting -- Microsoft TDSP for data preparation -- Data pre-processing concept -- Data health screening -- Data pre-processing major tasks -- Feature engineering -- Data pre-processing and cleaning -- Feature engineering -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 6. Developing a Machine Learning Model -- Structure -- Objectives -- Guidelines for model development -- CRISP-DM -- Selection of modeling technique -- Generation of test design -- Model building -- Model assessment -- Microsoft TDSP -- Goals -- Tasks -- Deliverables -- Modeling algorithms and evaluation -- What is a model? -- How to choose an algorithm? -- Metrics for model evaluation -- Classification metrics -- Regression metrics Model development procedure -- Modeling for HMDA use case -- Choosing an algorithm -- Modeling scenario-1 -- Modeling scenario-2 -- Model tuning -- Feature selection -- Dimensionality reduction -- Cross-validation -- Regularization -- Bagging and boosting -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 7. Lap Around Azure ML Service -- Structure -- Objectives -- Azure ML Service overview -- Architecture and key concepts -- Workspace -- Compute -- Managed compute -- Un-managed compute -- Datasets and datastores -- Environments -- Experiments -- Runs -- Run configurations -- Snapshots -- Pipelines -- Models -- Model registry -- Deployment -- Endpoints -- Web service endpoint -- IoT module endpoints -- Getting started: signup and provisioning -- AutoML in Azure ML Service -- Model development with Azure ML Service -- Azure ML Designer -- AutoML using ML Studio UI -- AutoML using Python SDK -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- 8. Deploying and Managing Models -- Structure -- Objectives -- Guidelines for deployment and evaluation -- CRISP-DM -- Microsoft TDSP -- Model lifecycle management -- Model lifecycle using Azure ML SDK -- Training the model -- Registering model -- Deploying the model -- Testing/consuming deployed model -- Retraining a model -- Model lifecycle using Azure ML Studio UI -- MLOps with Azure Pipelines -- Pre-requisites -- Azure DevOps project -- Project repository -- Azure Subscription -- Azure Service connection -- Creating a build pipeline -- Creating a release pipeline -- Conclusion -- Points to remember -- Multiple choice questions -- Answers -- Questions -- Key terms -- Index Big data |
| title | Practitioner's Guide to Data Science |
| title_auth | Practitioner's Guide to Data Science |
| title_exact_search | Practitioner's Guide to Data Science |
| title_exact_search_txtP | Practitioner's Guide to Data Science |
| title_full | Practitioner's Guide to Data Science |
| title_fullStr | Practitioner's Guide to Data Science |
| title_full_unstemmed | Practitioner's Guide to Data Science |
| title_short | Practitioner's Guide to Data Science |
| title_sort | practitioner s guide to data science |
| topic | Big data |
| topic_facet | Big data |
| work_keys_str_mv | AT mirzanasirali practitionersguidetodatascience |