Practical Java Machine Learning: Projects with Google Cloud Platform and Amazon Web Services - Tapa blanda
Sinopsis
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
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