Pickling and Scaling 
Pickling and Scaling
by Harrison Kinsley
Video Lecture 6 of 42
Not yet rated
Views: 678
Date Added: August 11, 2016

Lecture Description

In the previous Machine Learning with Python tutorial we finished up making a forecast of stock prices using regression, and then visualizing the forecast with Matplotlib. In this tutorial, we'll talk about some next steps.

I remember the first time that I was trying to learn about machine learning, and most examples were only covering up to the training and testing part, totally skipping the prediction part. Of the tutorials that did the training, testing, and predicting part, I did not find a single one that explained saving the algorithm. With examples, data is generally pretty small overall, so the training, testing, and prediction process is relatively fast. In the real world, however, data is likely to be larger, and take much longer for processing. Since no one really talked about this important stage, I wanted to definitely include some information on processing time and saving your algorithm.

While our machine learning classifier takes a few seconds to train, there may be cases where it takes hours or even days to train a classifier. Imagine needing to do that every day you wanted to forecast prices, or whatever. This is not necessary, as we can just save the classifier using the Pickle module.

pythonprogramming.net
twitter.com/sentdex
www.facebook.com/pythonprogramming.net/
plus.google.com/+sentdex

Course Index

Course Description

The objective of this course is to give you a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms.

In this series, we'll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks.

Comments

There are no comments. Be the first to post one.
  Post comment as a guest user.
Click to login or register:
Your name:
Your email:
(will not appear)
Your comment:
(max. 1000 characters)
Are you human? (Sorry)