Learn about the purpose of the scikit-learn and pandas libraries.
- [Instructor] Python is one of the most widely-used programming languages for machine learning. Aside from being a really great and easy-to-use programming language, Python is so popular because many great machine learning libraries are available for it. We are going to use three of the most popular 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 real systems in Silicon Valley. It's the foundation on which many other machine learning libraries are built. Next, we'll use scikit-learn.
Scikit-learn is a very popular machine learning library. Think of it as a Swiss army knife for machine learning. It provides easy-to-use implementations of many of the most popular machine learning algorithms. Finally, we'll also use pandas. Pandas lets you represent your data as a virtual spreadsheet that you can control with code. It has many of the same features you find in Microsoft Excel for quickly editing your data and performing calculations and so on. It makes it really easy to work with data exported as CSV files. The name pandas comes from the term 'panel data' because it represents your data as a series of panels which are like pages in a spreadsheet.
The best part is that all of these libraries work together perfectly. NumPy provides the basic ability to load and work with a dataset, pandas provides the extra capabilities to make it easy to clean up and do calculations on the dataset, and scikit-learn provides the actual machine learning algorithms we'll run on the data.
- Setting up the development environment
- Building a simple home value estimator
- Finding the best weights automatically
- Working with large data sets efficiently
- Training a supervised machine learning model
- Exploring a home value data set
- Deciding how much data is needed
- Preparing the features
- Training the value estimator
- Measuring accuracy with mean absolute error
- Improving a system
- Using the machine learning model to make predictions