Decoding AI: A Guide to AI Frameworks and Algorithms
Artificial Intelligence (AI) and Machine Learning (ML) are no longer futuristic buzzwords—they’re the engines behind Netflix recommendations, self-driving cars, fraud detection, and even chatbots like the one you’re reading right now.
But here’s the catch: between frameworks, algorithms, and learning paradigms, the AI landscape can feel like alphabet soup. Terms like “supervised,” “unsupervised,” “linear,” or “non-linear” often float around without clear context.
This blog breaks it all down with a visual matrix that shows how frameworks, algorithms, and paradigms connect—giving you the big picture of AI at a glance.
AI & ML Frameworks
Frameworks are the toolkits that make AI development possible. They handle the heavy lifting so you can focus on building models, not reinventing the wheel.
Scikit-learn: The go-to library for classical ML (regression, classification, clustering).
XGBoost, LightGBM, CatBoost: Optimised gradient boosting for tabular data.
OpenAI Gym & RLlib: Popular for reinforcement learning experimentation.
How Algorithms Are Classified
Algorithms can be categorised in several ways:
1. Linear vs. Non-linear
Linear
Assume straight-line relationships
Logistic Regression
Linear SVM
Non-linear
Capture complex, curved, or multi-dimensional relationships.
Neural Networks
k-NN
2. Row-wise vs. Column-wise
Row-wise
Work on individual instances
Regression
Column-wise
Work on features collectively
Feature Importance
3. Learning Paradigms
Supervised
Learn from labelled data. (Regression, Classification)
Semi-supervised
Mix of labelled + unlabelled data. (Self-training, GANs with few labels)
Unsupervised
Discover hidden patterns in unlabelled data. (Clustering, Dimensionality Reduction)
Reinforcement Learning
Learn through rewards and penalties. (Q-learning, DQN, Policy Gradients)
The Machine Learning Matrix
To simplify, here’s a matrix mapping algorithms across linear vs. non-linear and row-wise vs. column-wise, within each learning paradigm:
| Learning Paradigm | Linear + Row-wise | Non-linear + Row-wise | Linear + Column-wise | Non-linear + Column-wise |
|---|---|---|---|---|
| Supervised | • Linear Regression • Logistic Regression • Linear SVM |
• Decision Trees • Random Forests • Gradient Boosting • Neural Networks • k-NN |
• Ridge/Lasso Regression • Linear Discriminant Analysis (LDA) |
• Kernel SVM • Kernel PCA • Deep Autoencoders • Feature Importance |
| Unsupervised | • k-Means • Linear PCA |
• DBSCAN • Hierarchical Clustering • Gaussian Mixture Models • Non-linear embeddings (t-SNE, UMAP) |
• Factor Analysis • Singular Value Decomposition (SVD) |
• Kernel PCA • Independent Component Analysis (ICA) • Deep Embeddings (Autoencoders, VAEs) |
| Semi-supervised | • Self-training with linear classifiers • Semi-supervised SVMs |
• Label Propagation • Graph-based methods |
• Sparse Coding with partial labels | • Semi-supervised GANs • Deep Generative Models (VAEs, GANs with few labels) |
| Reinforcement | • Linear Q-Learning (tabular methods) | • Deep Q-Networks (DQN) • Policy Gradient Methods • Actor-Critic Algorithms |
• Feature-based Q • Value Approximation |
• Representation Learning in RL • CNN+RL, Graph RL • AlphaZero-style Algorithms |
Key Insights
Frameworks provide the infrastructure; algorithms drive the learning.
Linear vs. Non-linear tells us about the complexity of relationships captured.
Row-wise vs. Column-wise separates instance-based vs. feature-based methods.
Learning paradigms (Supervised, Unsupervised, Semi-supervised, Reinforcement) describe how data guides learning.
A matrix view makes it easy to see how these categories interact in practice.
Where to Learn More
AI isn’t just math—it’s about connecting frameworks, algorithms, and learning paradigms into a coherent map. With this matrix, you now have a clear lens to decode AI and start building smarter applications.
The future belongs to those who embrace AI not just as a tool, but as a strategic partner in innovation.
At REVA Academy for Corporate Excellence (RACE), REVA University, our specialised research groups from MTech/MSc in Artificial Intelligence are advancing real-world AI projects in collaboration with leading organisations. Through our bespoke programs in Artificial Intelligence, you can deepen your expertise, join a vibrant research community, and build future-ready capabilities to drive transformation across industries. The question is—how will you shape the future of AI?
The question is—how will you shape the future of AI?
Call: +91 89040 58866 Email: race@reva.edu.in
