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Machine Learning Projects for Mobile Applications

  • Course Code: Data Science - Machine Learning Projects for Mobile Applications
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 2 Days Audience: This course is geared for Python experienced developers, analysts or others who wants to Build Android and iOS applications using TensorFlow Lite and Core ML

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Machine Learning skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who wants to Build Android and iOS applications using TensorFlow Lite and Core ML 
  • 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 is a technique that focuses on developing computer programs that can be modified when exposed to new data. We can make use of it for our mobile applications and this course will show you how to do so. The course starts with the basics of machine learning concepts for mobile applications and how to get well equipped for further tasks. You will start by developing an app to classify age and gender using Core ML and Tensorflow Lite. You will explore neural style transfer and get familiar with how deep CNNs work. We will also take a closer look at Google’s ML Kit for the Firebase SDK for mobile applications. You will learn how to detect handwritten text on mobile. You will also learn how to create your own Snapchat filter by making use of facial attributes and OpenCV. You will learn how to train your own food classification model on your mobile; all of this will be done with the help of deep learning techniques. Lastly, you will build an image classifier on your mobile, compare its performance, and analyze the results on both mobile and cloud using TensorFlow Lite with an RCNN. By the end of this course, you will not only have mastered the concepts of machine learning but also learned how to resolve problems faced while building powerful apps on mobiles using TensorFlow Lite, Caffe2, and Core ML. 

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

  • Explore machine learning using classification, analytics, and detection tasks. 
  • Work with image, text and video datasets to delve into real-world tasks 
  • Build apps for Android and iOS using Caffe, Core ML and Tensorflow Lite 

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

  • Demystify the machine learning landscape on mobile 
  • Age and gender detection using TensorFlow Lite and Core ML 
  • Use ML Kit for Firebase for in-text detection, face detection, and barcode scanning 
  • Create a digit classifier using adversarial learning 
  • Build a cross-platform application with face filters using OpenCV 
  • Classify food using deep CNNs and TensorFlow Lite on iOS 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to Build Android and iOS applications using TensorFlow Lite and Core ML 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills. Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them. 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Mobile Landscapes in Machine Learning 
  • Mobile Landscapes in Machine Learning 
  • Machine learning basics 
  • TensorFlow Lite and Core ML 
  • TensorFlow Lite 
  • Core ML 
  1. CNN Based Age and Gender Identification Using Core ML 
  • CNN Based Age and Gender Identification Using Core ML 
  • Age, gender, and emotion prediction 
  • Convolutional Neural Networks  
  • The implementation on iOS using Core ML 
  1. Applying Neural Style Transfer on Photos 
  • Applying Neural Style Transfer on Photos 
  • Artistic neural style transfer 
  • Building the applications 
  1. Deep Diving into the ML Kit with Firebase 
  • Deep Diving into the ML Kit with Firebase 
  • ML Kit basics 
  • Face detection 
  • Barcode scanner 
  • Text recognition 
  1. A Snapchat-Like AR Filter on Android 
  • A Snapchat-Like AR Filter on Android 
  • MobileNet models 
  • Building the Android application 
  1. Handwritten Digit Classifier Using Adversarial Learning 
  • Handwritten Digit Classifier Using Adversarial Learning 
  • Generative Adversarial Networks 
  • Understanding the MNIST database 
  • Building the TensorFlow model 
  • Training the neural network 
  1. Face-Swapping with Your Friends Using OpenCV 
  • Face-Swapping with Your Friends Using OpenCV 
  • Understanding face-swapping 
  1. Classifying Food Using Transfer Learning 
  • Classifying Food Using Transfer Learning 
  • Transfer learning 
  • Training our own TensorFlow model  
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