Practical Java Machine Learning: Projects with Google Cloud Platform and Amazon Web Services

1. Introduction IDE Setup - Eclipse IDE Setup - Android Studio Java Setup Machine Learning Performance with Java Importance of Analytics Initiatives Corporate ML Objectives Business Case for Deploying ML Machine Learning Concerns Developing an ML Methodology State of the Art: Monitoring Research Papers
2. Data: The Fuel for Machine Learning Think Like a Data Scientist Data Pre-Processing JSON and NoSQL Databases ARFF and CSV Files Finding Public Data Creating your Own Data Data Visualization with Java + Javascript Project: DataViz
3. Leveraging Cloud Platforms Google Cloud Platform Amazon AWS Using Machine Learning API's Project: GCP API Leveraging Cloud Platforms to Create Models
4. Algorithms: The Brains of Machine Learning Overview of Algorithms Supervised Learning Unsupervised Learning Linear Models for Prediction and Classification Naive Bayes for Document Classification Clustering Decision Trees Choosing the Right Algorithm Creating Your Competitve Advantage
5. Java Machine Learning Environments Overview Choosing a Java Environment Deep dive: The Weka Workbench Weka Capabilities Weka Add-ons Rapidminer Overview Project: Document Classification with Weka
6. Integrating Models