Machine learning has become the hot topic across so many different fields. It involves using different mathematical models to find patterns in data, often to make predictions for new data. It has seen great success for images, speech recognition, and a vast number of applications that we use everyday. But what does it actually involve? And how do you know if you’re doing it well?
You’re going to get stuck into some data and start making predictions using the scikit-learn machine learning library.
Prerequisites
This lesson assumes that you have some knowledge of the Python language. We will use the Google Colab environment, so there is no need to install any software.
| Setup | Download files used in the lesson. | |
| 00:00 | 1. Introduction |
What is machine learning? And what is AI? How does statistics fit into this?
What is Google Colab? |
| 00:15 | 2. Data |
How do I load data?
How do I triage data? |
| 00:45 | 3. Classification | How do you train a classifier? |
| 01:25 | 4. Evaluation | How can I tell if my classifier is good? |
| 01:50 | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.