[2019] MACHINE LEARNING REGRESSION MASTERCLASS IN PYTHON

[2019] MACHINE LEARNING REGRESSION MASTERCLASS IN PYTHON

[2019] MACHINE LEARNING REGRESSION MASTERCLASS IN PYTHON
[2019] MACHINE LEARNING REGRESSION MASTERCLASS IN PYTHON, Build 8+ Practical Projects and Master Machine Learning Regression Techniques Using Python, Scikit Learn and Keras
  • BESTSELLER
  • Created by Dr. Ryan Ahmed, Ph.D., MBA, Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, Mitchell Bouchard
  • English
  • English [Auto-generated]
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What you'll learn

  • Master Python programming and Scikit learn as applied to machine learning regression
  • Understand the underlying theory behind simple and multiple linear regression techniques
  • Apply simple linear regression techniques to predict product sales volume and vehicle fuel economy
  • Apply multiple linear regression to predict stock prices and Universities acceptance rate
  • Cover the basics and underlying theory of polynomial regression
  • Apply polynomial regression to predict employees’ salary and commodity prices
  • Understand the theory behind logistic regression
  • Apply logistic regression to predict the probability that customer will purchase a product on Amazon using customer features
  • Understand the underlying theory and mathematics behind Artificial Neural Networks
  • Learn how to train network weights and biases and select the proper transfer functions
  • Train Artificial Neural Networks (ANNs) using back propagation and gradient descent methods
  • Optimize ANNs hyper parameters such as number of hidden layers and neurons to enhance network performance
  • Apply ANNs to predict house prices given parameters such as area, number of rooms..etc
  • Assess the performance of trained Machine learning models using KPI (Key Performance indicators) such as Mean Absolute error, Mean squared Error, and Root Mean Squared Error intuition, R-Squared intuition, Adjusted R-Squared and F-Test
  • Understand the underlying theory and intuition behind Lasso and Ridge regression techniques
  • Sample real-world, practical projects
  • Requirements
  • Machine Learning basics
  • PC with Internet connetion

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