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AI / Data Scientist (Senior Track)
Build robust machine learning models and handle big data architecture.
01
Mathematics for ML
Linear algebra, calculus, probability, and statistics.
02
Data Engineering
SQL/NoSQL, Spark, Hadoop, Kafka, and ETL pipelines.
03
Machine Learning Algorithms
SVM, Random Forests, Gradient Boosting, PCA, and ensemble methods.
04
Deep Learning
Neural Networks, CNNs, RNNs, PyTorch, and TensorFlow.
05
NLP & Computer Vision
Embeddings, attention mechanisms, object detection, and transformers.
06
Large Language Models
Fine-tuning, RAG, prompt engineering, LoRA, and vector databases.
07
MLOps & Deployment
Model monitoring, Kubernetes, MLflow, and scalable inference APIs.
KNOWCGPA
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