In this video, learn how to zoom up to 25k feet to take a look at what the full process usually looks like as you put all of these pieces together.
- [Instructor] Welcome back … to the final chapter of this course, … where we're actually going to dive in and get our hands dirty … by building an end to end machine learning pipeline. … Before we dive into the code, … I first want to lay out the high level process … that we'll walk through in the next few lessons. … Nothing here should surprise you. … We've talked about every component so far, … this is just pulling all the pieces together, and aligning … on what the rest of this chapter will look like. … You should be familiar with this diagram … that we looked at previously. … We have our training set … that represents 60 percent of the full data set, … and that's split into 5 segments … for the purpose of fivefold cross-validation. … Then we have our validation set, … which represents 20 percent of our data. … And lastly, we have our test dataset, … which represents the final 20 percent of our full dataset. … The first step is going to be exploring the data … to really understand the type of features we have, …
Author
Released
5/10/2019- What is machine learning (ML)?
- ML vs. deep learning vs. AI
- Handling common challenges in ML
- Plotting continuous features
- Continuous and categorical data cleaning
- Measuring success
- Overfitting and underfitting
- Tuning hyperparameters
- Evaluating a model
Skill Level Beginner
Duration
Views
Related Courses
-
Deploying Scalable Machine Learning for Data Science
with Dan Sullivan1h 43m Intermediate
-
Introduction
-
Leveraging machine learning1m 57s
-
What you should know1m 6s
-
Using the exercise files1m 24s
-
-
1. Machine Learning Basics
-
Why Python?5m 49s
-
Common challenges6m 4s
-
2. Exploratory Data Analysis and Data Cleaning
-
Plotting continuous features7m 35s
-
Continuous data cleaning5m 44s
-
Categorical data cleaning4m 33s
-
3. Measuring Success
-
Why do we split up our data?5m 54s
-
-
4. Optimizing a Model
-
What is underfitting?2m 26s
-
What is overfitting?2m 47s
-
Finding the optimal tradeoff3m 16s
-
Hyperparameter tuning6m 22s
-
Regularization2m 31s
-
5. End-to-End Pipeline
-
Overview of the process1m 48s
-
Clean categorical features4m 18s
-
Tune hyperparameters6m 34s
-
-
Conclusion
-
Next steps1m 23s
-
- Mark as unwatched
- Mark all as unwatched
Are you sure you want to mark all the videos in this course as unwatched?
This will not affect your course history, your reports, or your certificates of completion for this course.
CancelTake notes with your new membership!
Type in the entry box, then click Enter to save your note.
1:30Press on any video thumbnail to jump immediately to the timecode shown.
Notes are saved with you account but can also be exported as plain text, MS Word, PDF, Google Doc, or Evernote.
Share this video
Embed this video
Video: Overview of the process