Andrea Giussani - Building Machine Learning Pipelines with scikit-learn - Part Two

This course is the second in a two-part series that covers how to build machine learning pipelines using scikit-learn, a library for the Python programming language. This is a hands-on course containing demonstrations that you can follow along with to build your own machine learning models.
Learning Objectives
Explore supervised-learning techniques used to train a model in scikit-learn by using a simple regression model
Understand the concept of the bias-variance trade-off and regularized ML models
Explore linear models for classification and how to evaluate them
Learn how to choose a model and fit that model to a dataset
Intended Audience
This course is intended for anyone interested in machine learning with Python.
Prerequisites
To get the most out of this course, you should have first taken Part One of this two-part series.
Resources
The resources related to this course can be found in the following GitHub repo:https://github.com/cloudacademy/ca-machine-learning-with-scikit-learn