Machine Learning    

 

 

 

Use Case / Category

Artificial intelligence and machine learning are quietly transforming our daily lives by powering a wide range of technologies that solve complex problems in practical ways. For instance, these tools help classify emails as spam or important, predict traffic patterns to suggest the fastest route to work, or cluster shopping habits to recommend products on your favorite online store. They can simplify vast amounts of data—like reducing the complexity of social media trends to identify key interests—or detect unusual activity, such as flagging a suspicious charge on your credit card. AI also enables sequence-based predictions, like suggesting the next word as you type a text message, and processes natural language to power virtual assistants that understand your voice commands. In computer vision, it helps unlock your phone with facial recognition or identify objects in photos you share online. For intelligent control, AI drives features like adaptive cruise control in cars or optimizes delivery routes for your pizza order. From personalizing your streaming recommendations to enhancing security systems, this versatile toolkit of AI and ML techniques is seamlessly integrated into everyday experiences, making technology more intuitive and efficient in our daily routines.

Following is the summary of common use cases in a tabular format.

Use-Case

AI/ML Models

Classification
  • Logistic Regression
  • Decision Trees
  • Random Forest
  • Support Vector Machines (SVM)
  • K-Nearest Neighbors (KNN)
  • Neural Networks
    • Perceptron
    • Multi-Layer Perceptron (MLP)
    • Convolutional Neural Networks (CNN) - for image classification
    • Recurrent Neural Networks (RNN) - for sequence classification
    • Long Short Term Memory (LSTM) - for sequence classification
    • Transformer Networks (like BERT, GPT-3) - for text classification

Regression (Prediction)

Linear Regression, Polynomial Regression, Ridge Regression, Lasso Regression, Elastic Net

Clustering

K-means, Hierarchical clustering, DBSCAN, Mean-shift

Dimensionality Reduction

Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), Autoencoders

Recommendation

Collaborative Filtering, Content-Based Filtering, Hybrid recommendation systems

Anomaly Detection

Isolation Forest, One-Class SVM, Local Outlier Factor (LOF)

Sequence Prediction

Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU)

Natural Language Processing (NLP)

Transformer Models (BERT, GPT-3, GPT-4), Seq2Seq Models, RNN and LSTM

Computer Vision

Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), YOLO, SSD (for object detection)

Action/Control
  • Reinforcement Learning
    • Q-Learning
    • Deep Q-Networks (DQN)
    • SARSA (State-Action-Reward-State-Action)
    • Policy Gradients
    • Actor-Critic methods (e.g., A2C, A3C)
    • Proximal Policy Optimization (PPO)
    • Deep Deterministic Policy Gradient (DDPG)
    • Monte Carlo methods