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.
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.
Impact of Big Data on Business and People.
Better Customer Relationships.
Refined Product Development.
Improved Decision-Making.
- Big Data Warehousing.
Big Data Frameworks.
ML Frameworks.
SciKit-Learn.
- H2O.
XGBoost.
DL Frameworks.
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.
Accelerated Failure Time (AFT) model.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.
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.
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
