Curriculum

Machine Learning
for Mobile Dev

Machine Learning for Mobile App development (iOS or Android)

About the Program

In the Machine Learning for Mobile App Development course, students will learn how to integrate machine learning (ML) models into mobile applications on their chosen platform—either Android (using Java) or iOS (using Swift). The course will focus on utilizing machine learning to create intelligent, user-friendly apps that can perform tasks such as image recognition, sentiment analysis, and personalized recommendations, all powered by on-device ML.

Students will use platform-specific tools and frameworks such as Core ML and Create ML (for iOS) or TensorFlow Lite and ML Kit (for Android) to implement these features into their apps. By the end of the course, students will have gained the skills to create mobile applications that leverage machine learning to enhance user experience, improve app functionality, and make data-driven decisions directly on the device.

Course Objectives:

  1. Understand Machine Learning Fundamentals for Mobile Development:
    • Grasp machine learning concepts, such as supervised learning, regression, classification, and deep learning.
    • Learn how to apply machine learning techniques in mobile environments using Core ML and TensorFlow Lite/ML Kit.
  2. Integrate Machine Learning Models into Mobile Apps:
    • Use Core ML (iOS) or TensorFlow Lite (Android) to integrate pre-trained machine learning models.
    • Build and deploy applications that leverage on-device machine learning for real-time predictions and insights.
  3. Optimize Models for Mobile Devices:
    • Learn how to optimize machine learning models for performance and memory consumption on mobile devices.
    • Address common mobile app challenges like latency and resource constraints in machine learning applications.
  4. Develop Practical Machine Learning Features for Mobile Apps:
    • Implement machine learning features such as image recognition, text analysis, recommendation systems, and predictive analytics on mobile devices.
  5. Deploy and Fine-tune Machine Learning Models:
    • Learn how to deploy machine learning models on mobile apps and fine-tune them based on real-time data.
    • Understand how to update and maintain models in production environments.
Requirements

At least a core i5 computer, 8GB RAM. Prior experience with Java or Swift is required.

Student to Teacher Ratio of 10:1
Machine Learning for Mobile App development

Curriculum

1. Introduction to Machine Learning for Mobile
  1. Overview of mobile machine learning and its impact on app development.
  2. Key concepts in machine learning (supervised, unsupervised, deep learning).
  3. Understanding Core ML (iOS) or TensorFlow Lite and ML Kit (Android).
  4. Setting up the development environment for mobile ML.
  5. Exploring real-world mobile app examples powered by machine learning.
  1. Introduction to Core ML for iOS or TensorFlow Lite for Android.
  2. Exploring ML Kit and Firebase ML for Android.
  3. Understanding pre-trained models and how to integrate them into mobile apps.
  4. Hands-on: Integrating a basic ML model (image or text) into an app.
  1. Data collection and cleaning for machine learning on mobile.
  2. Preprocessing data (images, text, etc.) for ML model input.
  3. Converting and normalizing data to fit mobile ML models.
  4. Hands-on: Prepare a dataset for image recognition or sentiment analysis.
  1. Overview of image classification models (e.g., MobileNet, ResNet).
  2. Integrating Core ML for iOS or TensorFlow Lite for Android to classify images.
  3. Using the mobile device’s camera to capture and classify images in real-time.
  4. Hands-on: Build a simple image recognition app for iOS or Android.
  1. Introduction to Natural Language Processing (NLP) for mobile applications.
  2. Implementing text classification and sentiment analysis on mobile apps.
  3. Using Core ML (iOS) or ML Kit (Android) for text analysis.
  4. Hands-on: Build a sentiment analysis app for iOS or Android.
  1. Overview of recommendation systems: collaborative filtering vs. content-based.
  2. Using machine learning to build a personalized recommendation system.
  3. Hands-on: Implement a recommendation engine for mobile apps (e.g., for e-commerce or media apps).
  1. Overview of object detection and Augmented Reality (AR) using machine learning.
  2. Implementing Core ML or TensorFlow Lite for real-time object detection.
  3. Enhancing apps with AR using on-device machine learning models.
  4. Hands-on: Build an object detection app or AR feature using mobile ML.
  1. Optimizing ML models for mobile devices: model size, inference speed, and memory.
  2. Techniques such as quantization, pruning, and model compression for mobile apps.
  3. Hands-on: Optimize a pre-trained model for mobile use (iOS or Android).
  1. Deploying ML models to mobile devices for real-time predictions.
  2. Fine-tuning models based on user feedback and new data.
  3. Managing models in production and handling model updates.
  4. Hands-on: Deploy and fine-tune a model based on real-time data.
  1. Objective: Use everything you’ve learned to build a full-fledged machine learning mobile app that solves a real-world problem.
  2. Project Ideas:
    • Build a fitness app that predicts the user’s workout or suggests personalized routines.
    • Create a mobile app that classifies plant species using images captured by the camera.
    • Develop a recommendation system for movie or product recommendations.
    • Build a smart photo organizer app that sorts and categorizes photos based on content.
  3. Deliverables:
    • A fully functional mobile app with integrated machine learning.
    • Optimized model for mobile deployment.
    • Code and documentation for the app and model.


Program Expectations

By the end of this course, here’s what you’ll be able to achieve:

  1. Be able to integrate machine learning models into mobile apps for both iOS (Swift) and Android (Java).
  2. Understand how to optimize models for mobile environments and improve app performance.
  3. Be capable of developing real-world mobile applications with machine learning features such as image recognition, sentiment analysis, and recommendation systems.
  4. Gain hands-on experience deploying and fine-tuning machine learning models directly on mobile devices for real-time predictions.

This course is ideal for mobile app developers who want to enhance their apps with intelligent, data-driven features and build advanced, machine learning-powered mobile applications for their chosen platform.

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