Data Science Solutions with Python: Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn

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.

• Unsupervised Learning.

• Cluster Analysis.

• Dimension Reduction.

• Exploring Deep Learning.

• Conclusion.

Chapter 2: Big Data Frameworks and ML and DL Frameworks.

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 Warehousing.

• Big Data ETL.

• Big Data Frameworks.

Apache Spark.

• Resilient Distributed Datasets.

• Spark Configuration.

• Spark Frameworks.

• ML Frameworks.

• SciKit-Learn.

• H2O.

• 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.

• H2O in action.

• Conclusion.

Chapter 4: Survival Regression Analysis.

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.

Accelerated Failure Time (AFT) model.

• 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.

Logistic Regression in Practice.

• SciKit-Learn in action.

• Spark MLlib in Action.

• H2O in action.

Conclusion.

Chapter 6: Tree-based Modelling and Gradient Boosting.

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.

Conclusion.

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