In this episode, we break down 10 essential Machine Learning algorithms making it the perfect crash course for data science enthusiasts, beginners, and aspiring ML professionals. What you’ll learn:
Regression Algorithms – Understand how Linear Regression models trends using a best-fit line.
-Classification Algorithms – Dive into Logistic Regression, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM) with real-world insights.
-Tree-Based Models – Explore how Decision Trees split data using rules and how Random Forests improve accuracy by averaging predictions.
-Boosting Techniques – Learn how AdaBoost and Gradient Boosting enhance learning sequentially for better performance.
-Unsupervised Learning – Discover how K-Means Clustering identifies natural data groupings and how Collaborative Filtering powers recommendation systems. Whether you’re preparing for interviews, building your ML foundation, or revising core concepts, this episode offers a concise, beginner-friendly overview with simple math, visuals, and real-world relevance
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