
Develop a breast imaging reporting and data system in swift with a tab bar controller, and learn hybrid iOS development using python for big data on Myriad's dataset.
Students will be introduced to Anaconda, Jupyter, iPython, as it is taught from an iOS Developers point of view! Students will learn KNN, a supervised algorithmic classification used in Data Science.
Using the Wisconsin Breast Cancer Data Set, students will build on their knowledge of Supervised Deep Learning with the Logistic Regression algorithm. The algorithm will be used in the final Swift Project, BIRADS.
Explore BIRADS UI in Swift, featuring a tab bar with three controllers, nine sliders with labels, and neural network and dataset views for the breast imaging reporting and data system.
Learn to build a user interface from scratch with a tab app and first and second view controllers, configuring the main storyboard, iPad layout, and icons/assets in Swift.
Design the second controller user interface for the breast imaging reporting and data system, with an image view, two labels, two buttons, and options to display neural network outputs.
Name and wire sliders to output labels in Interface Builder, connect outlets and actions, and integrate random forest, neural networks, and logistic regression for the BIRADS code app.
BI-RADS DATA SCIENCE FOR SWIFT/PYTHON HACKERS, is a course designed by an iOS Developer for iOS and Python Developers. In this course you will delve into Data Science on a level past using coreML for Machine Learning.
You will learn iPython enough to implement algorithms used in Data Science with little effort. As a Swift Programmer you will find the syntax needed to flow through iPython in Jupyter a breeze!!!
After grasping a thorough knowledge of supervised learning in the first two sections, you will dive into xCode and write a Logistic Regression Binary Based application.
In your final project, you will build BIRADS, a Breast Imaging-Reporting and Data System that takes the output data from a Neural Network and assigns a BI-RADS Category given input from the following features...
Sample code number ID number
2. Clump Thickness 1 - 10
3. Uniformity of Cell Size 1 - 10
4. Uniformity of Cell Shape 1 - 10
5. Marginal Adhesion 1 - 10
6. Single Epithelial Cell Size 1 - 10
7. Bare Nuclei 1 - 10
8. Bland Chromatin 1 - 10
9. Normal Nucleoli 1 - 10
10. Mitoses 1 - 10
11. Class: (1 for benign, 0 for malignant)
FULL SOURCE CODE APPLICATION INCLUDED!