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 Parametric Method.
The Non-parametric method.
Ensemble Methods.
Cluster Analysis.
Dimension Reduction.
Conclusion.
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.
Big Data.
Characteristics of Big Data.
Impact of Big Data on Business and People.
Better Customer Relationships.
Refined Product Development.
Improved Decision-Making.
Big Data ETL.
Big Data Frameworks.
Apache Spark.
Resilient Distributed Datasets.
Spark Configuration.
ML Frameworks.
SciKit-Learn.
XGBoost.
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.
Regression in practice.
SciKit-Learn in action.
Spark MLlib in action.
Conclusion.
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.
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.
Spark MLlib in Action.
H2O 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.
Deep Belief Networks.
Keras in action.
H2O in action.
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外文書商品之書封，為出版社提供之樣本。實際出貨商品，以出版社所提供之現有版本為主。部份書籍，因出版社供應狀況特殊，匯率將依實際狀況做調整。
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