Cargando…
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.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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
- 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
- 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
- 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
- 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