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Cancer Genomics | Neural Networks vs k-NN Classifiers
Rating: 3.8 out of 5(71 ratings)
1,729 students

Cancer Genomics | Neural Networks vs k-NN Classifiers

Machine Learning for Python Hackers
Created byBrian Rouse
Last updated 11/2017
English

What you'll learn

  • Use Anaconda IDE
  • Use Jupyter IDE
  • Machine Learning
  • Cancer Genomics
  • k-NN Classifier
  • Neural Networks
  • Deep Learning
  • mglearn Library for Visualization

Course content

4 sections • 7 lectures • 1h 23m total length

  • Introduction10:02

    Explore how abnormal signal transduction via growth factor receptors drives cancer genomics, including gene amplification, receptor overexpression, and targeted therapies.

Requirements

  • Student should have basic OOPL skills.

Description

Cancer Genomics | Neural Networks vs k-NN Classifiers : Machine Learning for Python Hackers is a crash course in Data Science and Cancer Genomics for anyone interested in cancer research. The course starts out with loading up a cancer dataset to split train and test. This course is unique in Data Science in that it uses the mglearn library for better visualization and is dedicated to providing details as such so the student can follow along with no ambiguity.

  • k-NN Classifications with detailed visualization
  • Neural Network built from scratch with line by line explanation and visualization!
  • Build a GC  :Content Calculator! 


Who this course is for:

  • Aspired Data Scientist
  • Python Programmers interested in Cancer Genomics

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