End-to-end Machine Learning: Polynomial Regression
4.1 (30 ratings)
280 students enrolled

# End-to-end Machine Learning: Polynomial Regression

Build a dog breed selector in python
4.1 (30 ratings)
280 students enrolled
Created by Brandon Rohrer
Last updated 3/2019
English
English [Auto-generated]
Price: \$49.99
30-Day Money-Back Guarantee
This course includes
• 2 hours on-demand video
• Access on mobile and TV
• Certificate of Completion
Training 5 or more people?

What you'll learn
• Fit polynomial functions to a data set, including linear regression, quadratic regression, and higher order polynomial regression, using scikit-learn's optimize package.

• ### Choose the best model from among several candidates.

• Choose appropriate cost functions for optimization.
• Clean a dataset, handling missing and corrupted values.
• Perform non-linear operations to transform data into domain-relevant features.
• Create scatterplots and function plots in matplotlib.
• Build a command-line user interface in python.
• Create classes and use object-oriented programming concepts in python.
Course content
Expand all 18 lectures 01:55:21
+ Get the data
6 lectures 29:48
Preview 03:28
Preview 02:14
Preview 04:09
Preview 05:53
Define the question
08:06
Create the interface
05:58
+ Interpret the data
4 lectures 25:00
03:31
Plot the data
05:50
Get results by size and build
06:31
Get build data
09:08
+ Build a dog sizing model
5 lectures 37:08
Fit the curve
06:41
Compare model candidates
07:37
Train a model
05:43
Define the loss function
08:13
Create a polynomial model class
08:54
+ Complete the dog sizer
3 lectures 23:25
Find the best model
10:09
Test dog sizer
09:27
Wrap up
03:49
Requirements
• Some experience with python is helpful, but not required.
Description

In this course, we will walk  through the process of using machine learning to solve the problem of which puppy to adopt. We’ll go all the way from defining a good question to building and testing a program to answer it. Along the way, we’ll get to explore and repair a data set, deep dive into model selection and optimization, create some plots of the results, and build a command line interface for getting answers. The star of the show will be a polynomial regression algorithm that we will write from scratch. When you’re done you’ll know how to create a polynomial regressor of any order--linear, quadratic, cubic, or higher--and how to automatically choose the one that best fits your data set.

Who this course is for:
• Intermediate machine learning students and data scientists looking to round out their skill set on a realistic problem.