Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Hybrid Python3|Swift4 Applications
Rating: 4.2 out of 5(12 ratings)
4,721 students

Hybrid Python3|Swift4 Applications

Data Science for Swift/Python Hackers.
Created byBrian Rouse
Last updated 10/2017
English

What you'll learn

  • Anaconda
  • Jupyter
  • iPython
  • scikit-learn
  • Accelerate Framework
  • Basic Neural Network Subroutines (BNNS)
  • Build Cancer Predicting Neural Network
  • Build Swift BIRADS App!

Course content

5 sections9 lectures1h 13m total length
  • Course Introduction2:11

Requirements

  • Students should have an intermediate knowledge of an OOPL.

Description

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! 

Who this course is for:

  • iOS Developers interested in Data Science && Machine Learning on a greater level than coreML.