Learn to build Simpsons image classifier iPhone app using Apple's crate ML and core ML SDK. Deep learning is popular where a machine can be trained to detect objects in images. Once trained, it can be used to detect objects in any image. The app does not require any wifi or cellular connectivity. It uses deep learning to train the model from scratch on your own image dataset. The model can then be used inside an mobile app using Apple's coreML SDK. We'll build this app in this course. Since the app does not send your images or vides to remote service, it maintains your privacy and data secured.
Build a strong foundation in pose detection engines with this tutorial for beginners.
Understanding fundamentals of CreateML and CoreML
Understanding fundamentals of deep learning and CNN
Train a model on your own dataset using create ML SDK and XCode
Build a real life object detection mobile application using coreml and swift
A Powerful Skill at Your Fingertips Learning the fundamentals of object detection puts a powerful and very useful tool at your fingertips. swift, create ml and coreml are free, easy to learn, has excellent documentation.
No prior knowledge of CNN or deep learning is assumed. I'll be covering topics like CNN from scratch.
Jobs in computer vision area are plentiful, and being able to learn object detection will give you a strong edge.
Learning object detection will help you become a computer vision developer which is in high demand.
Content and Overview
This course teaches you on how to build object detection engine using open source create ml, coreml and swift . You will work along with me step by step to build following answers
Train Object Detection model
Build Mobile object detection app using trained model
What am I going to get from this course?
Learn object detection from professional trainer from your own desk.
Over 10 lectures teaching you how to build object detection engine
Suitable for beginner programmers and ideal for users who learn faster when shown.
Visual training method, offering users increased retention and accelerated learning.
Breaks even the most complex applications down into simplistic steps.
Offers challenges to students to enable reinforcement of concepts. Also solutions are described to validate the challenges.