Data Science Solutions with Python: Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn
商品資訊
ISBN13：9781484277614
出版社：Apress
作者：Tshepo Chris Nokeri
出版日：2021/11/09
裝訂：平裝
規格：25.4cm*17.8cm*0.7cm (高/寬/厚)
商品簡介
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 framework
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