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
Top comments (0)