Supervised learning: regression (linear, logistic), classification, and model tuning

Unsupervised learning: clustering (KMeans, GMM, DBSCAN), dimensionality reduction (PCA, t-SNE, UMAP)

Ensemble methods: Random Forest, Gradient Boosting, XGBoost, CatBoost, LightGBM

Time series modeling: ARIMA, Prophet, rolling stats, and seasonality decomposition

Evaluation metrics: ROC-AUC, F1 score, confusion matrices, precision/recall tradeoffs

Model selection: cross-validation, grid/randomized search, hyperparameter tuning

Feature engineering: transformation pipelines, interaction terms, encoding, scaling

Explainability: SHAP, permutation importance, partial dependence plots

Fully connected (dense) networks: feedforward architectures, dropout, batch normalization

Convolutional Neural Networks (CNNs): for image recognition, object detection, edge cases

Recurrent Neural Networks (RNNs): LSTM, GRU for sequential tasks like time series and text

Embeddings: learned representations via Word2Vec, Doc2Vec, and TensorFlow/Keras Embedding layers

Optimization: backpropagation, gradient descent variants (Adam, RMSProp, SGD)

Overfitting strategies: regularization, dropout, data augmentation

Frameworks: PyTorch, TensorFlow, Keras — including building from scratch for deeper understanding

Generative AI: prompt engineering, fine-tuning large language models (LLMs), text summarization

NLP: transformers (BERT, RoBERTa, T5), sentiment analysis, named entity recognition, embeddings

Search + retrieval: vector search with FAISS, embedding-based similarity, RAG (retrieval augmented generation)

Reinforcement learning: policy gradients, Q-learning, basic agent-environment frameworks

Ethics and alignment: fairness, bias detection, interpretability, responsible deployment

AI-driven systems: end-to-end ML/AI architectures that solve real business problems

Pipeline orchestration: training, validation, deployment, monitoring

Drift detection: custom solutions for feature and prediction distribution shift

Model versioning: MLflow, DVC, model registries, reproducibility

Automated retraining: scheduled jobs, model decay triggers, batch/online updates

Monitoring: prediction confidence tracking, input schema validation, latency thresholds

Integration with business systems: embedding models into APIs, web tools, or BI layers

AWS: S3, EC2, SageMaker, Lambda, Glue, Athena, Redshift, CloudWatch

Azure: Azure ML, Blob Storage, Synapse, Azure DevOps, Key Vault

GCP (familiarity): BigQuery, Vertex AI, GCS

Infrastructure-as-Code: Terraform for provisioning repeatable, secure cloud environments

Cost optimization: data lifecycle policies, autoscaling, spot instances

GitHub Actions: secure workflows for testing, packaging, deployment

DevSecOps: gated merges, environment secrets, PR labeling, unit tests

Package automation: Poetry-based versioning, wheel builds, wheelhouse packaging

Multi-environment deployment: staging → UAT → prod rollouts, rollback plans

Notebook → production transitions: converting notebooks to robust Python modules and services

ETL pipelines: ingesting from APIs, databases, flat files, streaming sources

Data wrangling: pandas, PySpark, SQL joins, window functions, cleaning pipelines

Storage design: normalized + denormalized schemas, Delta Lake, data lakes vs. warehouses

Job scheduling: Airflow, AWS Step Functions, cron-based tasks

Graph databases: Neo4j design for knowledge graphs and entity relationship mapping

Data quality checks: validation, profiling, anomaly detection models for pipeline health

Business Intelligence: Tableau, Power BI, AWS QuickSight

Python viz: Matplotlib, Seaborn, Plotly, Dash, Streamlit (custom tools + prototypes)

Storytelling: crafting narratives through data with audience-specific framing

Interactive dashboards: filters, drilldowns, parameterized views for stakeholder control

Model explainability UI: SHAP plots, feature importance summaries, prediction explanations

Real-time insights: dashboards connected to streaming/near-real-time data pipelines