AI/ML Model Types & Use Cases Guide
1. Regression / Prediction
Purpose: Predict continuous numerical values.
Use Cases:
- House price prediction
- Stock market forecasting
- Weather prediction
- Sales forecasting
Models: Linear Regression, Polynomial Regression, Decision Trees, Neural Networks
Libraries/Frameworks:
- Python: scikit-learn, statsmodels, PyTorch, TensorFlow
- Java: Weka, Deeplearning4j, Smile, Encog
- JavaScript: TensorFlow.js, brain.js, ml.js
2. Classification
Purpose: Predict discrete categories.
Use Cases:
- Spam detection
- Sentiment analysis
- Disease diagnosis
- Fraud detection
Models: Logistic Regression, Random Forest, SVM, Neural Networks, Transformers
Libraries/Frameworks:
- Python: scikit-learn, XGBoost, PyTorch, HuggingFace Transformers
- Java: Weka, Deeplearning4j, Smile
- JavaScript: TensorFlow.js, brain.js, ml5.js
3. Clustering / Unsupervised Learning
Purpose: Find patterns or groupings without labeled outputs.
Use Cases:
- Customer segmentation
- Anomaly detection
- Document clustering
Models: K-Means, DBSCAN, Hierarchical Clustering, Autoencoders
Libraries/Frameworks:
- Python: scikit-learn, PyTorch, TensorFlow
- Java: Weka, Smile
- JavaScript: ml.js, clustering.js, TensorFlow.js
4. Natural Language Processing (NLP)
Purpose: Understand and generate text.
Use Cases:
- Chatbots and conversational AI
- Q&A systems
- Text summarization and translation
- Sentiment analysis
Models: Bag-of-Words, RNN/LSTM, Transformers (BERT, GPT, T5), RAG
Libraries/Frameworks:
- Python: HuggingFace Transformers, spaCy, NLTK, SentenceTransformers, LangChain
- Java: DL4J + ND4J, Stanford NLP, Apache OpenNLP
- JavaScript: TensorFlow.js, HuggingFace Transformers.js, compromise
5. Computer Vision
Purpose: Process and understand images/videos.
Use Cases:
- Object detection (self-driving cars)
- Image classification (medical images)
- Image segmentation
- Face recognition
Models: CNNs, YOLO, Vision Transformers
Libraries/Frameworks:
- Python: PyTorch, TensorFlow/Keras, OpenCV, Detectron2
- Java: Deeplearning4j, OpenCV Java, BoofCV
- JavaScript: TensorFlow.js, ml5.js, opencv.js
6. Reinforcement Learning (RL)
Purpose: Learn by interacting with an environment.
Use Cases:
- Game AI (chess, Go)
- Robotics
- Autonomous driving
- Resource optimization
Models: Q-Learning, DQN, Policy Gradient, Actor-Critic
Libraries/Frameworks:
- Python: Stable-Baselines3, OpenAI Gym, RLlib, PyTorch, TensorFlow
- Java: RL4J (part of Deeplearning4j)
- JavaScript: reinforce-js, TensorFlow.js (custom RL)
7. Generative Models / Advanced AI
Purpose: Generate new data, images, or text.
Use Cases:
- Text generation
- Image/audio generation
- Content creation
Models: GANs, VAEs, Transformers, RAG
Libraries/Frameworks:
- Python: PyTorch, TensorFlow, HuggingFace Transformers, diffusers
- Java: Deeplearning4j (GANs, autoencoders), Smile
- JavaScript: TensorFlow.js, ml5.js
8. Rule of Thumb
| Problem Type | Recommended Model(s) |
|---|---|
| Predict numbers | Linear Regression, Neural Networks |
| Predict categories | Logistic Regression, Random Forest, BERT |
| Unsupervised patterns | K-Means, Autoencoders |
| Text understanding/generation | LSTM, Transformers, RAG |
| Image recognition | CNNs, ViTs |
| Decision making / control | RL (DQN, PPO) |
| Knowledge-based Q&A | RAG, Retrieval + Transformer |
| Generative content | GANs, VAEs, Transformers |