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Machine Learning for Mobile

  • Course Code: Data Science - Machine Learning for Mobile
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This course is geared for those who wants to Leverage the power of machine learning on mobiles and build intelligent mobile applications with ease.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Machine Learning for Mobile skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Leverage the power of machine learning on mobiles and build intelligent mobile applications with ease. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Machine learning presents an entirely unique opportunity in software development. It allows smartphones to produce an enormous amount of useful data that can be mined, analyzed, and used to make predictions. This course will help you master machine learning for mobile devices with easy-to-follow, practical examples. You will begin with an introduction to machine learning on mobiles and grasp the fundamentals so you become well-acquainted with the subject. You will master supervised and unsupervised learning algorithms, and then learn how to build a machine learning model using mobile-based libraries such as Core ML, TensorFlow Lite, ML Kit, and Fritz on Android and iOS platforms. In doing so, you will also tackle some common and not-so-common machine learning problems with regard to Computer Vision and other real-world domains. By the end of this course, you will have explored machine learning in depth and implemented on-device machine learning with ease, thereby gaining a thorough understanding of how to run, create, and build real-time machine-learning applications on your mobile devices. 

Working in a hands-on learning environment, led by our Machine Learning for Mobile expert instructor, students will learn about and explore: 

  • Build smart mobile applications for Android and iOS devices 
  • Use popular machine learning toolkits such as Core ML and TensorFlow Lite 
  • Explore cloud services for machine learning that can be used in mobile apps 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Build intelligent machine learning models that run on Android and iOS 
  • Use machine learning toolkits such as Core ML, TensorFlow Lite, and more 
  • Learn how to use Google Mobile Vision in your mobile apps 
  • Build a spam message detection system using Linear SVM 
  • Using Core ML to implement a regression model for iOS devices 
  • Build image classification systems using TensorFlow Lite and Core ML 

Audience & Pre-Requisites 

This course is designed for developers wants to Leverage the power of machine learning on mobiles and build intelligent mobile applications with ease 

Pre-Requisites:  Students should have familiar with  

  • Basics of ML 
  • Knowledge of Python is assumed. 

Course Agenda / Topics 

  1. Introduction to Machine Learning on Mobile 
  • Introduction to Machine Learning on Mobile 
  • Definition of machine learning 
  • The machine learning process 
  • Types of learning 
  • Why use machine learning on mobile devices? 
  1. Supervised and Unsupervised Learning Algorithms 
  • Supervised and Unsupervised Learning Algorithms 
  • Introduction to supervised learning algorithms 
  • Deep dive into supervised learning algorithms 
  • Introduction to unsupervised learning algorithms 
  • Deep dive into unsupervised learning algorithms 
  1. Random Forest on iOS 
  • Random Forest on iOS 
  • Introduction to algorithms 
  • Solving the problem using random forest in Core ML 
  1. TensorFlow Mobile in Android 
  • TensorFlow Mobile in Android 
  • An introduction to TensorFlow 
  • The architecture of a mobile machine learning application 
  • Writing the mobile application using the TensorFlow model 
  1. Regression Using Core ML in iOS 
  • Regression Using Core ML in iOS 
  • Introduction to regression 
  • Understanding the basics of Core ML 
  • Solving the problem using regression in Core ML 
  1. The ML Kit SDK 
  • The ML Kit SDK 
  • Understanding ML Kit 
  • Creating a text recognition app using Firebase on-device APIs 
  • Creating a text recognition app using Firebase on-cloud APIs 
  • Face detection using ML Kit 
  1. Spam Message Detection 
  • Spam Message Detection 
  • Understanding NLP 
  • Understanding linear SVM algorithm 
  • Solving the problem using linear SVM in Core ML 
  1. Fritz 
  • Fritz 
  • Introduction to Fritz 
  • Hand-on samples using Fritz 
  1. Neural Networks on Mobile 
  • Neural Networks on Mobile 
  • Introduction to neural networks 
  • Image recognition solution 
  • Creating a TensorFlow image recognition model 
  • Handwritten digit recognition solution 
  • Introduction to Keras 
  • Installing Keras 
  • Solving the problem 
  1. Mobile Application Using Google Vision 
  • Mobile Application Using Google Vision 
  • Features of Google Cloud Vision 
  • Sample mobile application using Google Cloud Vision 
  1. The Future of ML on Mobile Applications 
  • The Future of ML on Mobile Applications 
  • Key ML mobile applications  
  • Key innovation areas 
  • Opportunities for stakeholders 
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