Now that you know all about the kNN algorithm, you’re ready to start building performant predictive models in Python. These sorts of predictive models can save you lots of time, whether you’re working with data about sea snails or something else.

**In this video course, you learned how to:**

- Understand the mathematical foundations behind the
**kNN algorithm** - Code the kNN algorithm
**from scratch**in**NumPy** - Use the
**scikit-learn**implementation to fit a kNN with a minimal amount of code

To continue your machine learning journey, check out the Machine Learning Learning Path, and feel free to leave a comment to share any questions or remarks that you may have.

**Further Investigation:**

- K-Means Clustering in Python: A Practical Guide
- Using pandas and Python to Explore Your Dataset
- Splitting Datasets With scikit-learn and
`train_test_split()`

- Setting Up Python for Machine Learning on Windows
- Starting With Linear Regression in Python
- Logistic Regression in Python
- NumPy, SciPy, and pandas: Correlation With Python
- Python AI: How to Build a Neural Network & Make Predictions
- PyTorch vs TensorFlow for Your Python Deep Learning Project

Or maybe you’d like to learn more about abalones!

**Congratulations, you made it to the end of the course!** What’s your #1 takeaway or favorite thing you learned? How are you going to put your newfound skills to use? Leave a comment in the *discussion* section and let us know.

Jerry Con May 24, 2023This model might help me in my audio classification project. But I wonder if a range of audio frequencies (consider 200 Hz to 8 kHz) would greatly slow processing. Or could the total audio spectrum be condensed by this model. Think of phase shift and time of arrival and distance as parameters. Any comments?