Command Palette
Search for a command to run...
Comments
Join the discussionNo comments yet. Be the first to comment.
Machine Learning
Part 14 of 20
The aim of the series is to consolidate a foundational understanding of Machine Learning.
Section 10: Random Forests & Boosted Trees
Sections 9 and 10 are on tree-based methods. There are three main methods: Decision Trees (Section 9) Random Forests (Section 10) Boosted Trees (Section 10) Each of these methods stems from the basic decision tree algorithm. Fundamentally, tree-b...
More from this blog
8 tips when building a high fidelity prototype in Figma
Resources: Figma's documentation of best practices Figma's Community Page Do browse the community page for UI kits to quickly bootstrap your project. Decide early in the project the color palette and fonts to facilitate efficient collaborative de...
May 10, 2022
Section 15: Principal Component Analysis (PCA)
References: An Introduction to Statistical Learning (Download free pdf) Jose Portilla's 2021 Python for Machine Learning & Data Science Masterclass
Oct 2, 2021
Section 14: Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
References: An Introduction to Statistical Learning (Download free pdf) Jose Portilla's 2021 Python for Machine Learning & Data Science Masterclass
Oct 2, 2021
Section 13: Hierarchical Clustering
References: An Introduction to Statistical Learning (Download free pdf) Jose Portilla's 2021 Python for Machine Learning & Data Science Masterclass
Oct 1, 2021
Section 12: K-Means Clustering
So far we have done supervised learning. The remaining sections will be on unsupervised learning. Below is a quick guide on how to pick the estimator: Source: scikit-learn Unsupervised Learning: (1) Clustering: Using features, group together data r...
Sep 30, 2021
N
Nur Fadhilah
21 posts
