🧠 Method → Model → Algorithm

• Method
  The broad approach to learning.
  Example: Supervised Learning

• Model
  The structure or framework built using the method.
  Example: Decision Tree

• Algorithm
  The specific mathematical procedure or technique used to train or construct the model.
  Example: ID3, CART

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📘 AI/ML Methods → Models → Algorithms Mapping

🔍 Method	🧠 Model	⚙️ Algorithm
Supervised Learning	Linear Regression	Ordinary Least Squares
	Logistic Regression	Maximum Likelihood Estimation
	Decision Tree	CART / ID3 / C4.5
	Random Forest	Ensemble of Decision Trees
	Support Vector Machine (SVM)	Kernel Trick + Optimization
	K-Nearest Neighbors (KNN)	Distance-Based Search
	Naive Bayes Classifier	Bayes’ Theorem
	Neural Network	Backpropagation
	CNN (Convolutional NN)	Convolution + Pooling + Dense
	RNN (Recurrent NN)	Backpropagation Through Time (BPTT)
	LSTM	Gated RNNs
Unsupervised Learning	K-Means Clustering	Lloyd’s Algorithm
	Hierarchical Clustering	Agglomerative / Divisive
	PCA (Dimensionality Reduction)	Eigen Decomposition
	DBSCAN	Density-Based Clustering
	Autoencoder	Encoder-Decoder
	GAN (Generative Adversarial)	Generator + Discriminator Game
Reinforcement Learning	Q-Learning	Value Iteration
	DQN (Deep Q-Network)	Q-Learning + Neural Networks
	PPO	Policy Optimization
Self-Supervised Learning	BERT	Masked Language Modeling
	SimCLR	Contrastive Loss
Generative / Deep Learning	GPT	Transformer Decoder + Language Loss

🎯 Algorithm vs Method

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🧠 Algorithm

💡 Definition:
A step-by-step, rule-based procedure to solve a specific problem or complete a task.

🔍 Example:
🧮 Merge Sort – An algorithm that divides the list, sorts parts, and merges them.

📌 Key Highlights:

• 🧾 Clear sequence of instructions
• ⚙️ Designed to transform input into desired output
• 🔁 Repeatable and predictable

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🛠️ Method

💡 Definition:
A general technique or approach used to solve a class of problems, often involving one or more algorithms.

🔍 Example:
📉 Gradient Descent – A method for minimizing loss functions in ML using different algorithm variants like SGD or Batch GD.

📌 Key Highlights:

• 🧭 Broader in scope than algorithms
• 🔧 Can include multiple algorithms
• 🧪 Often used in practical applications and model training

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🆚 Key Differences

Aspect	🧮 Algorithm	🛠️ Method
📘 Definition	Specific step-by-step process	General approach/technique
🔁 Scope	Narrow, problem-specific	Broad, task-oriented
🧱 Examples	Merge Sort, K-Means, A* Search	Gradient Descent, Supervised Learning
⚙️ Relationship	Can be part of a method	May use multiple algorithms

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🔗 How They Work Together in ML

• Method: Supervised Learning 🧑‍🏫
• Algorithm: Decision Tree, SVM, or Linear Regression 🌳📊

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🧵 In Summary:

• 🎲 Algorithm = Exact steps 🧮
• 🧰 Method = General strategy, may use algorithms 🛠️

🔗 Think of a method as a toolbox 🧰, and each algorithm is a tool 🧲 you pick based on the task!

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🤖 Algorithm vs Model

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🔁 Algorithm

• A step-by-step procedure or set of rules used to solve a problem or perform a task.
• In AI/ML, an algorithm is used to train a model.
• Think of it like a recipe.

🧪 Example: Linear Regression algorithm tells you how to find the best-fitting line.

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🧠 Model

• The output or result of running an algorithm on data.
• It’s the trained representation that can make predictions or decisions.
• Think of it like the dish made using the recipe.

🎯 Example: The linear equation y = 3x + 2 is a model learned by applying the linear regression algorithm to your data.

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⚡ Analogy Table

Concept	Analogy
Algorithm	🍳 Cooking recipe
Model	🍽️ Final dish (after cooking)
Method → Model → Algorithm

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