Data Science Solutions with Python, 1st ed. Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn
Auteur : Nokeri Tshepo Chris
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.
- 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
Chapter 1: Understanding Machine Learning and Deep Learning.
Chapter goal: It carefully presents supervised and unsupervised ML and DL models and their application in the real world.
Understanding Machine Learning.
Supervised Learning.
The Non-parametric method.
Ensemble Methods.
- Unsupervised Learning.
Cluster Analysis.
Dimension Reduction.
- Exploring Deep Learning.
Conclusion.
The Parametric Method.
Chapter goal: It explains a big data framework recognized as PySpark, machine learning frameworks like SciKit-Learn, XGBoost, and H2O, and a deep learning framework called Keras.
Big Data Frameworks and ML and DL Frameworks.
Characteristics of Big Data.
Impact of Big Data on Business and People.
Refined Product Development.
Improved Decision-Making.
- Big Data Warehousing.
Big Data ETL.
Big Data Frameworks.
Resilient Distributed Datasets.
Spark Configuration.
- Spark Frameworks.
ML Frameworks.
SciKit-Learn.
- H2O.
XGBoost.
Big Data.
Better Customer Relationships.
Apache Spark.
DL Frameworks.
Keras.
Conclusion.
Chapter 3: The Parametric Method – Linear Regression.
Chapter goal: It considers the most popular parametric model – the Generalized Linear Model.
Regression Analysis.
SciKit-Learn in action.
Spark MLlib in action.
- H2O in action.
Conclusion.
Regression in practice.
Chapter goal: It covers two main survival regression analysis models, the Cox Proportional Hazards and Accelerated Failure Time model.
Cox Proportional Hazards.
Lifeline in action.
Spark MLlib in Action.
Conclusion.
Chapter 5: The Non-Parametric Method - Classification.
Chapter goal: It covers a binary classification model, recognized as Logistic Regression, using SciKit-Learn, Keras, PySpark MLlib, and H2O.
Logistic Regression.
SciKit-Learn in action.
Spark MLlib in Action.
- H2O in action.
Conclusion.
Chapter goal: It covers two main ensemble methods, the decision tree model and the gradient boost model.
Decision Tree.
SciKit-Learn in action.
Gradient Boosting.
XGBoost in action.
H2O in action.
Spark MLlib in Action.
Conclusion.
Chapter 7: Artificial Neural Networks.
Chapter goal: It covers deep learning and its application in the real world. It shows ways of designing, building, and testing an MLP classifier using the SciKit-Learn framework and an artificial neural network using the Keras framework.
Deep Learning.
Restricted Boltzmann Machine.
Multi-Layer Perception Neural Network.
SciKit-Learn in action.
Keras in action.
H2O in action.
Deep Belief Networks.
Chapter 8: Cluster Analysis using K-Means.
Chapter goal: It covers a technique of finding k, modelling and evaluating a cluster model known as K-Means using frameworks like SciKit-Learn, PySpark MLlib and H2O.- K-Means.
K-Mean in practice.
SciKit-Learn in action.
- Spark MLlib in Action.
H2O in action.
Conclusion.
Chapter 9: Dimension Reduction – Principal Components Analysis.
Chapter goal: It covers a technique for reducing data into few components using the Principal Components Analysis.
Principal Components Analysis.
Spark MLlib in Action.
H2O in Action.
SciKit-Learn in action.
Chapter 10: Automated Machine Learning.
Chapter goal: Acquaint the reader with the H2O AutoML model.Automated Machine Learning.
H2O in Action.
Conclusions.
Explains techniques for integrating frameworks for high model performance
Presents a hybrid approach for rapid prototyping models, deploying and scaling them
Bridges the gap between machine and deep learning frameworks
Date de parution : 10-2021
Ouvrage de 119 p.
17.8x25.4 cm
Disponible chez l'éditeur (délai d'approvisionnement : 15 jours).
Prix indicatif 36,91 €
Ajouter au panierThèmes de Data Science Solutions with Python :
Mots-clés :
Big Data Analytics; Machine Learning; Deep Learning; Python; Python Frameworks; Keras; Scikit-learn; PySpark; H2O; MLib; XGBoost