Data Science Solutions with Python Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn
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- Taschenbuch ausgewählt
- eBook
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Sprache:Englisch
Fr. 52.90
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
26.10.2021
Verlag
APressSeitenzahl
119
Maße (L/B/H)
25.4/17.8/0.8 cm
Gewicht
271 g
Auflage
1st edition
Sprache
Englisch
ISBN
978-1-4842-7761-4
The book covers an in-memory, distributed cluster computing framework known as PySpark, machine learning framework platforms known as scikit-learn, PySpark MLlib, H2O, and XGBoost, and a deep learning (DL) framework known as Keras.
The book starts off presenting supervised and unsupervised ML and DL models, and then it examines big data frameworks along with ML and DL frameworks. Author Tshepo Chris Nokeri considers a parametric model known as the Generalized Linear Model and a survival regression model known as the Cox Proportional Hazards model along with Accelerated Failure Time (AFT). Also presented is a binary classification model (logistic regression) and an ensemble model (Gradient Boosted Trees). The book introduces DL and an artificial neural network known as the Multilayer Perceptron (MLP) classifier. A way of performing cluster analysis using the K-Means model is covered. Dimension reduction techniques such as Principal Components Analysis and Linear Discriminant Analysis are explored. And automated machine learning is unpacked.
This book is for intermediate-level data scientists and machine learning engineers who want to learn how to apply key big data frameworks and ML and DL frameworks. You will need prior knowledge of the basics of statistics, Python programming, probability theories, and predictive analytics.
What You Will Learn
- Understand widespread supervised and unsupervised learning, including key dimension reduction techniques
- Know the big data analytics layers such as data visualization, advanced statistics, predictive analytics, machine learning, and deep learning
- Integrate big data frameworks with a hybrid of machine learning frameworks and deep learning frameworks
- Design, build, test, and validate skilled machine models and deep learning models
- Optimize model performance using data transformation, regularization, outlier remedying, hyperparameter optimization, and data split ratio alteration
Who This Book Is For
Data scientists and machine learning engineers with basic knowledge and understanding of Python programming, probability theories, and predictive analytics
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