For those of you who are looking forward to starting with Machine Learning or have an interest in it:

Machine learning (ML) has rapidly evolved, becoming an integral part of various fields such as data science, artificial intelligence, and even competitive programming. This blog explores the core algorithms that constitute the backbone of machine learning. For names of almost all machine learning algorithms, you can download the accompanying PDF.

1. Supervised Learning

Supervised learning involves training a model on a labeled dataset, where the model learns to map inputs to outputs.

• Linear Regression
• Logistic Regression
• Decision Trees
• Support Vector Machines (SVM)
• k-Nearest Neighbors (k-NN)

2. Unsupervised Learning

Unsupervised learning deals with data without labeled responses, focusing on finding hidden patterns.

• k-Means Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA)

3. Reinforcement Learning

Reinforcement learning involves agents learning to make decisions by taking actions in an environment to maximize cumulative rewards.

• Q-Learning
• Deep Q-Networks (DQN)

4. Deep Learning

Deep learning, a subset of machine learning, uses neural networks with many layers to model complex patterns in data.

• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)

This blog provides a snapshot of the diverse landscape of machine learning algorithms. For a complete list of these algorithms, including additional resources and details, download the PDF from the provided link.