ML ALGOS List

1️⃣ Supervised Learning Algorithms

(Labelled data hota hai)

🔹 Regression Algorithms

• Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Ridge Regression
• Lasso Regression
• Elastic Net
• Bayesian Regression
• Quantile Regression
• Support Vector Regression (SVR)
• Decision Tree Regression
• Random Forest Regression
• Gradient Boosting Regression
• XGBoost Regression
• LightGBM Regression
• CatBoost Regression

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🔹 Classification Algorithms

• Logistic Regression
• Naive Bayes
  • Gaussian NB
  • Multinomial NB
  • Bernoulli NB
• K-Nearest Neighbors (KNN)
• Support Vector Machine (SVM)
• Decision Tree
• Random Forest
• Extra Trees (Extremely Randomized Trees)
• Gradient Boosting
• AdaBoost
• XGBoost
• LightGBM
• CatBoost
• Linear Discriminant Analysis (LDA)
• Quadratic Discriminant Analysis (QDA)
• Perceptron
• Passive Aggressive Classifier

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2️⃣ Unsupervised Learning Algorithms

(Label nahi hota)

🔹 Clustering Algorithms

• K-Means
• K-Medoids
• Hierarchical Clustering
  • Agglomerative
  • Divisive
• DBSCAN
• HDBSCAN
• Mean Shift
• Gaussian Mixture Model (GMM)
• Spectral Clustering
• OPTICS
• BIRCH
• Affinity Propagation

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🔹 Dimensionality Reduction

• Principal Component Analysis (PCA)
• Kernel PCA
• Linear Discriminant Analysis (LDA)
• t-SNE
• UMAP
• Independent Component Analysis (ICA)
• Autoencoders
• Factor Analysis
• Truncated SVD

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🔹 Association Rule Learning

• Apriori Algorithm
• FP-Growth
• Eclat

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3️⃣ Semi-Supervised Learning

• Label Propagation
• Label Spreading
• Self Training
• Co-Training
• Semi-Supervised SVM

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4️⃣ Reinforcement Learning Algorithms

🔹 Value-Based

• Q-Learning
• SARSA
• Deep Q Network (DQN)
• Double DQN
• Dueling DQN

🔹 Policy-Based

• REINFORCE
• Policy Gradient

🔹 Actor-Critic

• A2C
• A3C
• DDPG
• TD3
• PPO
• SAC

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5️⃣ Ensemble Learning Techniques

• Bagging
• Boosting
• Stacking
• Voting Classifier
• Random Forest
• Gradient Boosting

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6️⃣ Neural Network Based Algorithms

(Deep Learning ML ka hi part hai)

🔹 Basic

• Artificial Neural Network (ANN)
• Multilayer Perceptron (MLP)

🔹 Advanced

• Convolutional Neural Network (CNN)
• Recurrent Neural Network (RNN)
• LSTM
• GRU
• Transformer
• Graph Neural Network (GNN)

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7️⃣ Anomaly / Outlier Detection

• Isolation Forest
• One-Class SVM
• Local Outlier Factor (LOF)
• Elliptic Envelope
• Autoencoder based anomaly detection

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8️⃣ Probabilistic & Statistical Models

• Hidden Markov Model (HMM)
• Conditional Random Field (CRF)
• Bayesian Networks
• Markov Chain

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9️⃣ Online Learning Algorithms

• Stochastic Gradient Descent (SGD)
• Online Perceptron
• Hoeffding Tree

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📌 Note:

• Industry me sabse zyada use hone wale: 👉 Linear/Logistic Regression, Random Forest, XGBoost, SVM, Neural Networks
• Exams / Interviews ke liye: 👉 KNN, Naive Bayes, Decision Tree, Clustering, PCA

Agar aap chaho to main:

• 🔹 sirf Deep Learning algorithms
• 🔹 exam-oriented short list
• 🔹 Python ke sath algorithms
• 🔹 kaunsa algorithm kab use kare

bhi bana sakta hoon 🙂

Prompt:

kaunsa algorithm kab use kare

ye bhi batatao

Response:

ठीक है 🙂

नीचे “kaunsa Machine Learning algorithm kab use kare” ko simple decision guide + examples ke saath samjha raha hoon.

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1️⃣ Data ke type ke hisaab se algorithm

🔹 Numerical data (numbers)

• Linear Regression → relation linear ho
• Polynomial Regression → curve type relation
• Random Forest / XGBoost → complex patterns, best accuracy

👉 Example: House price prediction

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🔹 Categorical data (class / label)

• Logistic Regression → simple & fast
• Naive Bayes → text data (spam detection)
• Decision Tree → explainable model
• Random Forest / XGBoost → high accuracy

👉 Example: Spam vs Not Spam

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2️⃣ Dataset size ke hisaab se

🔹 Chhota dataset (<10k rows)

• KNN
• Naive Bayes
• SVM
• Decision Tree

⚠️ Deep Learning avoid karo (overfitting hota hai)

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🔹 Medium dataset (10k – 1M)

• Random Forest
• Gradient Boosting
• XGBoost
• LightGBM

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🔹 Bahut bada dataset (>1M)

• SGD
• Linear / Logistic Regression
• Neural Networks
• Online Learning algorithms

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3️⃣ Problem type ke hisaab se

🔹 Regression problem (number predict karna)

Situation	Best Algorithm
Linear relation	Linear Regression
Outliers present	Ridge / Lasso
Non-linear	Random Forest
High accuracy chahiye	XGBoost

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🔹 Classification problem (class predict)

Condition	Algorithm
Binary classification	Logistic Regression
Text classification	Naive Bayes
High dimension	SVM
Interpretability	Decision Tree
Best accuracy	XGBoost / RF

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4️⃣ Unlabelled data (Clustering)

🔹 Clustering

Data	Algorithm
Well-separated clusters	K-Means
Noise present	DBSCAN
Unknown clusters	Hierarchical
Probabilistic	GMM

👉 Example: Customer segmentation

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5️⃣ Dimensionality zyada ho

• PCA → features kam karne ke liye
• t-SNE / UMAP → visualization
• Autoencoders → deep features

👉 Example: Image / gene data

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6️⃣ Time series data

• ARIMA
• SARIMA
• LSTM
• GRU
• Prophet

👉 Example: Stock price, weather

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7️⃣ Text / NLP problems

• Naive Bayes
• Logistic Regression + TF-IDF
• Word2Vec
• LSTM
• Transformers (BERT, GPT)

👉 Example: Sentiment analysis

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8️⃣ Image data

• CNN
• ResNet
• EfficientNet
• Vision Transformers

👉 Example: Face recognition

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9️⃣ Real-time / streaming data

• Online SGD
• Hoeffding Tree
• Reinforcement Learning

👉 Example: Recommendation system

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🔟 Reinforcement Learning kab use kare?

• Environment + reward ho
• Agent decision leta ho

👉 Example: Game AI, Robotics

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🧠 Golden Rule (Interview Friendly)

1️⃣ Simple model se start karo

2️⃣ Overfitting check karo

3️⃣ Ensemble try karo

4️⃣ Deep Learning tab hi jab data zyada ho