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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.

Course Access

Unlimited Duration

Last Updated

July 29, 2021

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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.

Course Curriculum

    • Mobile Landscapes in Machine Learning 00:00:00
    • Machine learning basics 00:00:00
    • TensorFlow Lite and Core ML 00:00:00
    • TensorFlow Lite 00:00:00
    • Core ML 00:00:00
    • CNN Based Age and Gender Identification Using Core ML 00:00:00
    • Age, gender, and emotion prediction 00:00:00
    • Convolutional Neural Networks 00:00:00
    • The implementation on iOS using Core ML 00:00:00
    • Applying Neural Style Transfer on Photos 00:00:00
    • Artistic neural style transfer 00:00:00
    • Building the applications 00:00:00
    • Deep Diving into the ML Kit with Firebase 00:00:00
    • ML Kit basics 00:00:00
    • Face detection 00:00:00
    • Barcode scanner 00:00:00
    • Text recognition 00:00:00
    • A Snapchat-Like AR Filter on Android 00:00:00
    • MobileNet models 00:00:00
    • Building the Android application 00:00:00
    • Handwritten Digit Classifier Using Adversarial Learning 00:00:00
    • Generative Adversarial Networks 00:00:00
    • Understanding the MNIST database 00:00:00
    • Building the TensorFlow model 00:00:00
    • Training the neural network 00:00:00
    • Face-Swapping with Your Friends Using OpenCV 00:00:00
    • Understanding face-swapping 00:00:00
    • Classifying Food Using Transfer Learning 00:00:00
    • Transfer learning 00:00:00
    • Training our own TensorFlow model 00:00:00

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