10 Essential Machine Learning algorithms for 2025 in 17 minutes - A Visualisation & Intuition

A visualisation and intuition for the ten 10 most important Machine Learning Algorithms in less than 17 minutes. Links to related playlists: Machine Learning https://www.youtube.com/playlist?list=PLaJCKi8Nk1hwklH8zGMpAbATwfZ4b2pgD Ensembles https://www.youtube.com/playlist?list=PLaJCKi8Nk1hxRtzY-8M2r3nDnRcyXU99Z Machine Learning Shorts https://www.youtube.com/playlist?list=PLaJCKi8Nk1hzrIQ17UknBGl5NlHc15c6F Chapters: Support Vector Machine (SVM) 1:02 Linear Regression: 2:45 Logistic Regression: 3:48 K-Nearest Neighbours (KNN): 5:11 Decision Trees: 6:43 Random Forests: 8:11 Gradient Boosting (AdaBoost): 9:41 Naive Bayes: 11:49 Principal Component Analysis (PCA): 13:01 t-Distributed Stochastic Neighbour Embedding (t-SNE): 14:21 In today's data-driven world, machine learning is no longer a luxury - it's a necessity. As more and more industries embrace AI and data science, understanding the key machine learning algorithms is becoming essential for professionals across fields. Whether you're a business leader looking to make better decisions, a product manager aiming to build smarter products, or a data analyst seeking to extract insights from complex datasets, mastering these algorithms is a game-changer. But the impact goes beyond individual careers. Companies that effectively leverage machine learning are seeing significant economic benefits. According to a recent McKinsey report, AI will deliver an additional economic output of around $13 trillion by 2030, boosting global GDP by about 1.2% a year. This means that organisations investing in machine learning talent and technology are positioning themselves for a substantial competitive advantage. Unlock the power of machine learning with this comprehensive overview of the 10 most important ML algorithms. In just 17 minutes, you'll gain a solid understanding of key techniques like Support Vector Machines (SVM), Linear Regression, Logistic Regression, K-Nearest Neighbors (KNN), Decision Trees, Random Forests, Gr