Machine Learning From Scratch - How to implement KNN from scratch with Python

In the first lesson of the Machine Learning from Scratch course, we will learn how to implement the K-Nearest Neighbours algorithm. Being one of the simpler ML algorithms, it is a great way to kick off our deep dive into ML algorithms. You can find the code here: https://github.com/AssemblyAI-Examples/Machine-Learning-From-Scratch Previous lesson: https://youtu.be/p1hGz0w_OCo Next lesson: https://youtu.be/ltXSoduiVwY Welcome to the Machine Learning from Scratch course by AssemblyAI. Thanks to libraries like Scikit-learn we can use most ML algorithms with a couple of lines of code. But knowing how these algorithms work inside is very important. Implementing them hands-on is a great way to achieve this. And mostly, they are easier than you’d think to implement. In this course, we will learn how to implement these 10 algorithms. We will quickly go through how the algorithms work and then implement them in Python using the help of NumPy. ▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT ▬▬▬▬▬▬▬▬▬▬▬▬ 🖥️ Website: https://www.assemblyai.com/?utm_source=youtube&utm_medium=referral&utm_campaign=scratch01 🐦 Twitter: https://twitter.com/AssemblyAI 👽 Reddit: https://reddit.com/r/assemblyai ▶️ Subscribe: https://www.youtube.com/c/AssemblyAI?sub_confirmation=1 🔥 We're hiring! Check our open roles: https://www.assemblyai.com/careers ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Are KNN and K-means the same thing? No. KNN is a supervised learning algorithm whereas K-means is a clustering algorithm. #MachineLearning #DeepLearning