Book Image

Getting Started with Machine Learning in Python [Video]

By : James Cross
Book Image

Getting Started with Machine Learning in Python [Video]

By: James Cross

Overview of this book

Machine Learning is a hot topic. And you want to get involved! From developers to analysts, this course aims to bring Machine Learning to those with coding experience and numerical skills. In this course, we introduce, via intuition rather than theory, the core of what makes Machine Learning work. Learn how to use labeled datasets to classify objects or predict future values, so that you can provide more accurate and valuable analysis. Use unlabelled datasets to do segmentation and clustering, so that you can separate a large dataset into sensible groups. You will learn to understand and estimate the value of your dataset. We guide you through creating the best performance metric for your task at hand, and how that takes you to the correct model to solve your problem. Understand how to clean data for your application, and how to recognize which Machine Learning task you are dealing with. If you want to move past Excel and if-then-else into automatically learned ML solutions, this course is for you! All the code and the supporting files are available on GitHub at - https://github.com/PacktPublishing/Getting-Started-with-Machine-Learning-in-Python- This course uses Python 3.6, while not the latest version available, it provides relevant and informative content for legacy users of Python.
Table of Contents (6 chapters)
Chapter 6
Modeling Complex Relationships with Nonlinear Models
Content Locked
Section 3
Reduce the Number of Learned Rules with Regularization
In this video, we will reduce the number of learned rules with regularization. - Learn about Regularization - Learn what does LI, L2 and alpha mean - Implement regularized linear regression