- Python (primary for AI/ML): TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy
- R (for statistical modeling)
- Optional: Java, C , or Julia for performance-critical tasks
Machine Learning &
- AI :
- Supervised &
- Unsupervised Learning (classification, regression, clustering)
- Deep Learning (CNNs, RNNs, Transformers)
- Natural Language Processing (NLP): Tokenization, embeddings, LLM fine-tuning
- Generative AI: Prompt engineering, model deployment
Mathematics &
- Statistics :
- Linear algebra, calculus, probability, and optimization
- Statistical inference and hypothesis testing
Data Engineering &
- Preprocessing :
- Data cleaning, feature engineering, and handling large datasets
- Familiarity with PySpark or Spark MLlib for distributed ML