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Mastering machine learning on AWS : advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing, Limited,
2019.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 1 | 0 | |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 | |
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing, Limited, |c 2019. | |
| 300 | |a 1 online resource (293 pages) | ||
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| 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; Naïve 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. | ||
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| 650 | 2 | |a Machine Learning | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a Exploration de données (Informatique) | |
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| 650 | 7 | |a Neural networks & fuzzy systems. |2 bicssc | |
| 650 | 7 | |a Artificial intelligence. |2 bicssc | |
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| 700 | 1 | |a Gurmendez, Maximo, |e author. | |
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