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Course Skill Level:


Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Developers with basic Python & machine learning experience

Who should attend & recommended skills

  • This course is geared for developers who want to Leverage the power of machine learning on mobiles and build intelligent mobile applications with ease.
  • Skill-level: Foundation-level Machine Learning for Mobile skills for Intermediate skilled team members. This is not a basic class.
  • Machine Learning: Basic (1-2 years’ experience)
  • Python: Basic (1-2 years’ experience

About this course

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.
Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This course is your key to solving any kind of ML problem you might come across in your job. You’ll encounter a set of simple to complex problems while building ML models, and you’ll not only resolve these problems, but you’ll also learn how to build projects based on each problem, with a practical approach and easy-to-follow examples. The course includes a wide range of applications: from analytics and NLP to computer vision domains. Some of the applications you will be working on include stock price prediction, a recommendation engine, building a chat-bot, a facial expression recognition system, and many more. The problem examples we cover include identifying the right algorithm for your dataset and use cases, creating and labeling datasets, getting enough clean data to carry out processing, identifying outliers, overfitting datasets, hyperparameter tuning, and more. Here, you’ll also learn to make more timely and accurate predictions. In addition, you’ll deal with more advanced use cases, such as building a gaming bot, building an extractive summarization tool for medical documents, and you’ll also tackle the problems faced while building an ML model. By the end of this course, you’ll be able to fine-tune your models as per your needs to deliver maximum productivity.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning for Mobile expert instructor, students will learn about and explore:
  • Building smart mobile applications for Android and iOS devices
  • Using popular machine learning toolkits such as Core ML and TensorFlow Lite
  • Exploring cloud services for machine learning that can be used in mobile apps
  • Building intelligent machine learning models that run on Android and iOS
  • Using machine learning toolkits such as Core ML, TensorFlow Lite, and more
  • How to use Google Mobile Vision in your mobile apps
  • Building a spam message detection system using Linear SVM
  • Using Core ML to implement a regression model for iOS devices
  • Building image classification systems using TensorFlow Lite and Core ML

Course breakdown / modules

  • Definition of machine learning
  • The machine learning process
  • Types of learning
  • Why use machine learning on mobile devices?

  • Introduction to supervised learning algorithms
  • Deep dive into supervised learning algorithms
  • Introduction to unsupervised learning algorithms
  • Deep dive into unsupervised learning algorithms

  • Introduction to algorithms
  • Solving the problem using random forest in Core ML

  • An introduction to TensorFlow
  • The architecture of a mobile machine learning application
  • Writing the mobile application using the TensorFlow model

  • Introduction to regression
  • Understanding the basics of Core ML
  • Solving the problem using regression in Core ML

  • 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

  • Understanding NLP
  • Understanding linear SVM algorithm
  • Solving the problem using linear SVM in Core ML

  • Introduction to Fritz
  • Hand-on samples using Fritz

  • 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

  • Features of Google Cloud Vision
  • Sample mobile application using Google Cloud Vision

  • Key ML mobile applications
  • Key innovation areas
  • Opportunities for stakeholders