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Viser: Practical Java Machine Learning - Projects with Google Cloud Platform and Amazon Web Services
Practical Java Machine Learning Vital Source e-bog
Mark Wickham
(2018)
Practical Java Machine Learning
Projects with Google Cloud Platform and Amazon Web Services
Mark Wickham
(2018)
Sprog: Engelsk
Detaljer om varen
- Vital Source searchable e-book (Reflowable pages)
- Udgiver: Springer Nature (Oktober 2018)
- ISBN: 9781484239513
Bookshelf online: 5 år fra købsdato.
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Detaljer om varen
- Paperback
- Udgiver: Apress L. P. (Oktober 2018)
- ISBN: 9781484239506
Practical Java Machine Learning includes multiple projects, with particular focus on the Android mobile platform and features such as sensors, camera, and connectivity, each of which produce data that can power unique machine learning solutions. You will learn to build a variety of applications that demonstrate the capabilities of the Google Cloud Platform machine learning API, including data visualization for Java; document classification using the Weka ML environment; audio file classification for Android using ML with spectrogram voice data; and machine learning using device sensor data.
After reading this book, you will come away with case study examples and projects that you can take away as templates for re-use and exploration for your own machine learning programming projects with Java.
What You Will Learn
- Identify, organize, and architect the data required for ML projects
- Deploy ML solutions in conjunction with cloud providers such as Google and Amazon
- Determine which algorithm is the most appropriate for a specific ML problem
- Implement Java ML solutions on Android mobile devices
- Create Java ML solutions to work with sensor data
- Build Java streaming based solutions
Experienced Java developers who have not implemented machine learning techniques before.
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