Get an overview of popular Machine Learning algorithms like Linear Regression, Logistic Regression, Decision Trees, SVMs, K-Means, and Neural Networks. Understand their basic concepts and use cases.
Explore unsupervised learning, where algorithms find hidden patterns in unlabeled data. Learn about clustering, dimensionality reduction, and their applications.
Supervised learning is a cornerstone of ML. This guide explains what it is, how it works with labeled data, common algorithms (like regression and classification), and real-world examples.
Feature engineering is both an art and a science. Discover what it is, why it's crucial for model performance, and explore common techniques to transform raw data into powerful features.
Dirty data can lead to flawed insights. Learn why data cleaning is a crucial first step in any data science or machine learning project and explore common cleaning techniques.
Understand the fundamental building blocks of Machine Learning: What are features, labels, and models? A clear explanation with simple examples for beginners.