All Machine Learning algorithms intuitively explained in 17 min

In this video I will go through all machine learning algorithms in less than 17 minutes to get you an intuitive understanding of how they work and how they relate to each other as well as help you decide how to pick the right one for your problem. Going all the way from Linear Regression to Neural Networks / Deep Learning and Unsupervised Learning.

Also Watch: How to Learn Machine Learning in 2024 (7 step roadmap) https://youtu.be/jwTaBztqTZ0

Chapters:

00:00 - Intro: What is Machine Learning?
00:59 - Supervised Learning
01:37 - Unsupervised Learning
02:20 - Linear Regression
04:04 - Logistic Regression
04:53 - K Nearest Neighbors (KNN)
06:10 - Support Vector Machine (SVM)
07:51 - Naive Bayes Classifier
08:37 - Decision Trees
09:11 - Ensemble Algorithms
09:24 - Bagging & Random Forests
09:53 - Boosting & Strong Learners
10:26 - Neural Networks / Deep Learning
12:42 - Unsupervised Learning (again)
12:57 - Clustering / K-means
14:35 - Dimensionality Reduction
15:15 - Principal Component Analysis (PCA)

YouTube

Tim, a data scientist with over 10 years of experience, offers an intuitive overview of critical machine learning algorithms to help you choose the right one for your problem. The post covers supervised learning (like regression and classification), unsupervised learning (like clustering), and dives into specific algorithms such as linear regression, logistic regression, K-nearest neighbors (KNN), support vector machine (SVM), naive Bayes classifier, decision trees, random forests, boosting, neural networks, and dimensionality reduction. Each algorithm is explained with examples to build an intuitive understanding of their functions and applications.

All Machine Learning algorithms explained in 17 min