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▼a 1789347505
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▼2 bisacsh |
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▼a 006.31
▼2 23 |
| 100 | 1 |
▼a Mengle, Saket S. R.,
▼e author. |
| 245 | 10 |
▼a Mastering machine learning on AWS :
▼b advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow /
▼c Saket S.R. Mengle, Maximo Gurmendez. |
| 246 | 3 |
▼a Mastering machine learning on Amazon Web Services |
| 260 | |
▼a Birmingham, UK :
▼b Packt Publishing, Limited,
▼c 2019. |
| 300 | |
▼a 1 online resource (293 pages) |
| 336 | |
▼a text
▼b txt
▼2 rdacontent |
| 337 | |
▼a computer
▼b c
▼2 rdamedia |
| 338 | |
▼a online resource
▼b cr
▼2 rdacarrier |
| 505 | 0 |
▼a Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Section 1: Machine Learning on AWS; Chapter 1: Getting Started with Machine Learning for AWS; How AWS empowers data scientists; Using AWS tools for machine learning; Identifying candidate problems that can be solved using machine learning; Machine learning project life cycle; Data gathering; Evaluation metrics; Algorithm selection; Deploying models; Summary; Exercise; Section 2: Implementing Machine Learning Algorithms at Scale on AWS |
| 505 | 8 |
▼a Chapter 2: Classifying Twitter Feeds with Naive BayesClassification algorithms; Feature types; Nominal features; Ordinal features; Continuous features; Naive Bayes classifier; Bayes' theorem; Posterior; Likelihood; Prior probability; Evidence; How the Naive Bayes algorithm works; Classifying text with language models; Collecting the tweets; Preparing the data; Building a Naive Bayes model through SageMaker notebooks; Nai?ve Bayes model on SageMaker notebooks using Apache Spark; Using SageMaker's BlazingText built-in ML service; Naive Bayes - pros and cons; Summary; Exercises |
| 505 | 8 |
▼a Chapter 3: Predicting House Value with Regression AlgorithmsPredicting the price of houses; Understanding linear regression; Linear least squares estimation; Maximum likelihood estimation; Gradient descent; Evaluating regression models; Mean absolute error; Mean squared error; Root mean squared error; R-squared; Implementing linear regression through scikit-learn; Implementing linear regression through Apache Spark; Implementing linear regression through SageMaker's linear Learner; Understanding logistic regression; Logistic regression in Spark; Pros and cons of linear models; Summary |
| 505 | 8 |
▼a Chapter 4: Predicting User Behavior with Tree-Based MethodsUnderstanding decision trees; Recursive splitting; Types of decision trees; Cost functions; Gini Impurity; Information gain; Criteria to stop splitting trees; Understanding random forest algorithms; Understanding gradient boosting algorithms; Predicting clicks on log streams; Introduction to Elastic Map Reduce (EMR); Training with Apache Spark on EMR; Getting the data; Preparing the data; Categorical encoding; One-hot encoding; Training a model; Evaluating our model; Area Under ROC Curve; Area under the precision-recall curve; Training tree ensembles on EMR Training gradient-boosted trees with the SageMaker services; Preparing the data; Training with SageMaker XGBoost; Applying and evaluating the model; Summary; Exercises |
| 505 | 8 |
▼a Chapter 5: Customer Segmentation Using Clustering Algorithms; Understanding How Clustering Algorithms Work; k-means clustering; Euclidean distance; Manhattan distance; Hierarchical clustering; Agglomerative clustering; Divisive clustering; Clustering with Apache Spark on EMR; Clustering with Spark and SageMaker on EMR; Understanding the purpose of the IAM role; Summary; Exercises; Chapter 6: Analyzing Visitor Patterns to Make Recommendations |
| 520 | |
▼a This book will help you master your skills in various artificial intelligence and machine learning services available on AWS. Through practical hands-on examples, you'll learn how to use these services to generate impressive results. You will have a tremendous understanding of how to use a wide range of AWS services in your own organization. |
| 588 | |
▼a Description based on print version record. |
| 590 | |
▼a Master record variable field(s) change: 072, 082 |
| 650 | 0 |
▼a Machine learning. |
| 650 | 0 |
▼a Python (Computer program language) |
| 650 | 0 |
▼a Data mining. |
| 650 | 7 |
▼a COMPUTERS / General.
▼2 bisacsh |
| 655 | 4 |
▼a Electronic books. |
| 700 | 1 |
▼a Gurmendez, Maximo,
▼e author. |
| 776 | 08 |
▼i Print version:
▼a Mengle, Saket S. R.
▼t Mastering machine learning on AWS : advanced machine learning in Python Using SageMaker, Apache Spark, and TensorFlow
▼d Birmingham : Packt Publishing, Limited, 짤2019
▼z 9781789349795 |
| 856 | 40 |
▼3 EBSCOhost
▼u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2142587 |
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▼a EBL - Ebook Library
▼b EBLB
▼n EBL5778831 |
| 938 | |
▼a EBSCOhost
▼b EBSC
▼n 2142587 |
| 990 | |
▼a 관리자 |
| 994 | |
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