Machine Learning Regression Masterclass in Python
Ditulis pada: August 11, 2021
[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