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Core AI & Engineering
Building AI models and transforming them into real-world AI systems.
LEARNING
Supervised Learning, Unsupervised Learning, Regression, Classification, Clustering, Feature Engineering, Model Selection, Model Evaluation, Hyperparameter Tuning, Ensemble Methods.
ALGORITHMS
Linear Regression, Logistic Regression, Decision Tree, Random Forest, SVM, KNN, XGBoost, Clustering.
OUTPUT
Prediction system, Classification system, ML experiment, Research, Prototype AI.
LEARNING
Neural Network, Forward & Backpropagation, Optimization, CNN, RNN, LSTM, Transformer, Transfer Learning, Fine-tuning, Model Training.
FRAMEWORKS
PyTorch, TensorFlow.
OUTPUT
Deep learning experiment, Trained model, Research, AI prototype.
LEARNING
AI Application Architecture, Model Integration, AI API, Inference, Model Serving, AI Pipeline, AI Backend, Prompt Engineering, AI Evaluation, AI System Design, Production AI, AI Security.
OUTPUT
AI application, AI API, AI-powered product, AI system, Production prototype.
LEARNING
Model Deployment, Model Serving, CI/CD, Model Versioning, Experiment Tracking, Monitoring, Model Registry, Infrastructure, Containerization, Docker, Cloud Deployment.
TOOLS
Docker, Git/GitHub, MLflow, Kubernetes, Cloud Platform.
OUTPUT
Deployable AI model, ML pipeline, AI infrastructure, Monitoring system.