Understand the goal of our project: creating a system to recommend products to users.
- [Instructor] To work with the code examples in this course, we need to download and install the Python 3 programming language and the PyCharm development environment. First we're going to install Python 3. We're at the Python.org website, and at the top of the page, click on downloads, and we'll see the newest version of Python 3 available for our operating system. I'm using Mac, so I'm going to download the Mac version, but Python 3 is also available for Windows and Linux. Click to download Python 3, great it's done. Now click on the download file to launch the installer.
We can accept all the default options, so just click continue, and agree, and install. Great, the installation is complete. Now we need to install PyCharm. PyCharm is an integrated development environment for Python, let's go to jetbrains.com/pycharm, and here we're going to click download now. Since I'm on a Mac, I'm downloading the Mac version of PyCharm, but PyCharm is also available for Windows and Linux.
We have the choice of downloading either the community edition or the professional edition, but the community edition has all the features we need and it's free, so it's the one we'll download. Click download. When the download completes, click on the file to install it. Now, to install PyCharm, just drag it to the applications folder, and now we can open PyCharm from the applications folder. And click open. Now we're ready to create our first project in PyCharm. Click create new project, for the location, we're going to choose the exercise files for this course, if you have them available.
I have them on my desktop, so I'll click there, click open. For the interpretor, we want to make sure that we use the version of Python 3 that we just installed, here I've selected Python 3.6, and click create, and yes. Okay. And as soon as we try to open one of the exercise files, in this case I'll open up vectors part one, PyCharm'll tell us that we're missing some of the package requirements, these are the machine learning libraries that we'll use in this course. We need numpy, pandas, and scipy.
To install them click install requirements, and click install. And you can watch the progress in the installation bar at the bottom, and they should install pretty quickly. And it's complete.
Author
Adam Geitgey
Released
4/10/2017Recommendation systems are a key part of almost every modern consumer website. The systems help drive customer interaction and sales by helping customers discover products and services they might not ever find themselves. The course uses the free, open source tools Python 3.5, pandas, and numpy. By the end of the course, you'll be equipped to use machine learning yourself to solve recommendation problems. What you learn can then be directly applied to your own projects.
- Building a machine learning system
- Training a machine learning system
- Refining the accuracy of the machine learning system
- Evaluating the recommendations received
Skill Level Intermediate
Duration
Views
-
Introduction
-
Welcome1m 1s
-
Set up environment2m 15s
-
-
1. The Basics of Making Recommendations
-
2. Ways of Making Recommendations
-
3. Getting to Know Our Tools
-
4. Building the Framework for Our Recommendation System
-
5. Collaborative Filtering with Matrix Factorization
-
6. Testing Our System
-
Use regularization1m 52s
-
7. Using the Recommendation System in a Real World Program
-
Find similar products1m 59s
-
Conclusion
-
Wrap up47s
-
Take notes with your new membership!
Type in the entry box, then click Enter to save your note.
Press 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: Set up environment