Machine Learning Regression Masterclass in Python

Machine Learning Regression Masterclass in Python
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz

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
Learn how to train network weights and biases and select the proper transfer functions
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
Description
The course provides students with practical hands-on experience in training machine learning regression models using real-world dataset. This course covers several technique in a practical manner, including

· Simple Linear Regression

· Multiple Linear Regression

· Polynomial Regression

· Logistic Regression

· Decision trees regression

· Ridge Regression

· Lasso Regression

· Regression Key performance indicators

The course is targeted towards students wanting to gain a fundamental understanding of machine learning regression models. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this course will master machine learning regression models and can directly apply these skills to solve real world challenging problems.

Who this course is for
Data Scientists who want to apply their knowledge on Real World Case Studies
Machine Learning Enthusiasts who look to add more projects to their Portfolio