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Intermediate🖼️ 20 slides⏱ 4 minMachine Learning

⚙️ Machine Learning Algorithms

Machine learning algorithms are the methods models use to learn from data. This visual guide tours key algorithms — linear and logistic regression, decision trees, random forests, SVM, K-means — and how to choose the right one.

Why Algorithms Matter — Machine Learning Algorithms visual note slide 1

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Why Algorithms Matter

The algorithm shapes how a model learns.

Supervised vs Unsupervised — Machine Learning Algorithms visual note slide 2

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Supervised vs Unsupervised

Choose based on whether you have labels.

Linear Regression — Machine Learning Algorithms visual note slide 3

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Linear Regression

Predict numbers with a straight line.

Logistic Regression — Machine Learning Algorithms visual note slide 4

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Logistic Regression

Predict class probabilities.

Decision Trees — Machine Learning Algorithms visual note slide 5

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Decision Trees

Yes/no splits lead to predictions.

Random Forest — Machine Learning Algorithms visual note slide 6

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Random Forest

Many trees vote for better accuracy.

Gradient Boosting — Machine Learning Algorithms visual note slide 7

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Gradient Boosting

Build trees that fix prior errors (XGBoost).

k-Nearest Neighbors — Machine Learning Algorithms visual note slide 8

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k-Nearest Neighbors

Classify by closest examples.

Support Vector Machines — Machine Learning Algorithms visual note slide 9

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Support Vector Machines

Find the best separating boundary.

Naive Bayes — Machine Learning Algorithms visual note slide 10

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Naive Bayes

Fast classification using probabilities.

K-Means — Machine Learning Algorithms visual note slide 11

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K-Means

Group unlabeled data into clusters.

PCA — Machine Learning Algorithms visual note slide 12

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PCA

Reduce features while keeping information.

Neural Networks — Machine Learning Algorithms visual note slide 13

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Neural Networks

Layers learn complex patterns.

Ensemble Methods — Machine Learning Algorithms visual note slide 14

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Ensemble Methods

Combine models for better results.

Bias-Variance Tradeoff — Machine Learning Algorithms visual note slide 15

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Bias-Variance Tradeoff

Balance simplicity and flexibility.

Hyperparameter Tuning — Machine Learning Algorithms visual note slide 16

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Hyperparameter Tuning

Adjust settings to improve models.

Choosing an Algorithm — Machine Learning Algorithms visual note slide 17

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Choosing an Algorithm

Depends on data, size, and goal.

Interpretability — Machine Learning Algorithms visual note slide 18

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Interpretability

Simple models are easier to explain.

No Free Lunch — Machine Learning Algorithms visual note slide 19

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No Free Lunch

No single algorithm wins everywhere.

Getting Started — Machine Learning Algorithms visual note slide 20

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Getting Started

Try a few algorithms and compare results.

Frequently Asked Questions

They fall into supervised (regression, classification), unsupervised (clustering, dimensionality reduction), and reinforcement learning algorithms.

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📈 Machine Learning Explained

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Unsupervised Learning — visual learning note

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🕹️ Reinforcement Learning

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