Learn how NumPy and pandas are used to load data and work with it efficiently.
- [Instructor] Python is a popular programming language for machine learning. Aside from being a really great and easy to use language, Python is so popular because many of the best machine learning libraries are built for it. For this class, we're going to use three of those libraries. First, we'll use NumPy. NumPy is a library that allows you to efficiently load and work with large datasets and memory. It's free, open source, and widely used in many commercial systems in Silicon Valley. It's the foundation on which many other machine learning libraries are built.
Next, we'll use SciPy. SciPy provides many basic scientific computing functions. We'll use its numerical optimization features to help calculate recommended products for users. Finally, we'll also use pandas. Pandas lets you represent your data as a virtual spreadsheet. It provides many of the same features you find in Microsoft Excel for quickly editing your data and performing calculations. It makes it really easy to work with data storage and CSV files. The name pandas comes from the term panel data because it represents your data as a series of panels or spreadsheet pages.
It's also free, open source, and widely used. The best part is that all of these libraries work perfectly together. SciPy provides the basic numerical optimization routines, NumPy lets us load and work with our dataset efficiently, and pandas gives us extra capabilities that make it easy to do calculations on our dataset.
Recommendation 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