नीचे Machine Learning ke lagbhag sabhi major algorithms ko category-wise list kiya gaya hai. (Practical duniya me “sabhi” ka matlab hota hai commonly used + academically important algorithms.)
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
🔹 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
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
🔹 Dimensionality Reduction
- Principal Component Analysis (PCA)
- Kernel PCA
- Linear Discriminant Analysis (LDA)
- t-SNE
- UMAP
- Independent Component Analysis (ICA)
- Autoencoders
- Factor Analysis
- Truncated SVD
🔹 Association Rule Learning
- Apriori Algorithm
- FP-Growth
- Eclat
3️⃣ Semi-Supervised Learning
- Label Propagation
- Label Spreading
- Self Training
- Co-Training
- Semi-Supervised SVM
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
5️⃣ Ensemble Learning Techniques
- Bagging
- Boosting
- Stacking
- Voting Classifier
- Random Forest
- Gradient Boosting
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)
7️⃣ Anomaly / Outlier Detection
- Isolation Forest
- One-Class SVM
- Local Outlier Factor (LOF)
- Elliptic Envelope
- Autoencoder based anomaly detection
8️⃣ Probabilistic & Statistical Models
- Hidden Markov Model (HMM)
- Conditional Random Field (CRF)
- Bayesian Networks
- Markov Chain
9️⃣ Online Learning Algorithms
- Stochastic Gradient Descent (SGD)
- Online Perceptron
- Hoeffding Tree
📌 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.
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
🔹 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
2️⃣ Dataset size ke hisaab se
🔹 Chhota dataset (<10k rows)
- KNN
- Naive Bayes
- SVM
- Decision Tree
⚠️ Deep Learning avoid karo (overfitting hota hai)
🔹 Medium dataset (10k – 1M)
- Random Forest
- Gradient Boosting
- XGBoost
- LightGBM
🔹 Bahut bada dataset (>1M)
- SGD
- Linear / Logistic Regression
- Neural Networks
- Online Learning algorithms
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 |
🔹 Classification problem (class predict)
| Condition | Algorithm |
|---|---|
| Binary classification | Logistic Regression |
| Text classification | Naive Bayes |
| High dimension | SVM |
| Interpretability | Decision Tree |
| Best accuracy | XGBoost / RF |
4️⃣ Unlabelled data (Clustering)
🔹 Clustering
| Data | Algorithm |
|---|---|
| Well-separated clusters | K-Means |
| Noise present | DBSCAN |
| Unknown clusters | Hierarchical |
| Probabilistic | GMM |
👉 Example: Customer segmentation
5️⃣ Dimensionality zyada ho
- PCA → features kam karne ke liye
- t-SNE / UMAP → visualization
- Autoencoders → deep features
👉 Example: Image / gene data
6️⃣ Time series data
- ARIMA
- SARIMA
- LSTM
- GRU
- Prophet
👉 Example: Stock price, weather
7️⃣ Text / NLP problems
- Naive Bayes
- Logistic Regression + TF-IDF
- Word2Vec
- LSTM
- Transformers (BERT, GPT)
👉 Example: Sentiment analysis
8️⃣ Image data
- CNN
- ResNet
- EfficientNet
- Vision Transformers
👉 Example: Face recognition
9️⃣ Real-time / streaming data
- Online SGD
- Hoeffding Tree
- Reinforcement Learning
👉 Example: Recommendation system
🔟 Reinforcement Learning kab use kare?
- Environment + reward ho
- Agent decision leta ho
👉 Example: Game AI, Robotics
🧠 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
Agar chaho to main:
- ✅ Flowchart bana du (algorithm selection)
- ✅ Real-world projects ke examples
- ✅ Interview Q&A
- ✅ Python code examples
bas bolo 👍
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