Beginner's Guide to Machine Learning

A structured, beginner-friendly guide to foundational machine learning algorithms from supervised learning (like regression and perceptrons) to reinforcement learning and unsupervised clustering.

Each concept is explained with:

• Intuitive Markdown tutorials
• Formulas and equations
• Real-world analogies
• ASCII diagrams and flowcharts
• Python Code

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Topics Covered

1. Supervised Learning

Linear Regression

• Understand how machines learn to predict numerical outcomes.
• Formula breakdown and error minimization.
• Visuals for gradients and cost functions.

Perceptron Learning

• The foundation of neural networks.
• Learn how a binary classifier updates weights using errors.

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2. Reinforcement Learning

Markov Decision Processes (MDP)

• Explore how agents navigate environments using rewards and penalties.
• Concepts of states, actions, transitions, and reward functions.

Q-Learning

• Learn how agents estimate future rewards to act optimally.
• Update rules, ε-greedy exploration, and real-world examples.

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3. Unsupervised Learning

Clustering

• Learn how algorithms group similar data without labels.
• Applications in genetics, image segmentation, and more.

k-Means Clustering

• Step-by-step process: initialization → assignment → update → convergence.
• Visual examples using 3 cluster states for clarity.

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