End-to-end Machine Learning: Polynomial Regression
What you will learn
Fit polynomial functions to a data set, including linear regression, quadratic regression, and higher order polynomial regression, using scikit-learn's optimize package.
Build an optimization algorithm from scratch, using Monte Carlo cross validation.
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.
Section 1: Get the data
Section 2: Interpret the data
Section 3: How optimization for machine learning works
Section 4: How to pick a machine learning model
Section 5: Build a dog sizing model
Section 6: Complete the dog sizer
Build a dog breed selector in python
- Some experience with python is helpful, but not required.
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.