How to implement Random Forest from scratch with Python

In the fifth lesson of the Machine Learning from Scratch course, we will learn how to implement Random Forests. Thanks to all the code we developed for Decision Trees, this implementation will be quite a bit shorter. You can find the code here: https://github.com/AssemblyAI-Examples/Machine-Learning-From-Scratch Previous lesson: https://youtu.be/NxEHSAfFlK8 Next lesson: https://youtu.be/TLInuAorxqE 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=scratch05 🐦 Twitter: https://twitter.com/AssemblyAI 🦾 Discord: https://discord.gg/Cd8MyVJAXd ▶️ Subscribe: https://www.youtube.com/c/AssemblyAI?sub_confirmation=1 🔥 We're hiring! Check our open roles: https://www.assemblyai.com/careers ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ #MachineLearning #DeepLearning