Azure ML, React, Docker, Distributed Systems, Python, Kubernetes, software engineering best practices, DL fundamentals, Go, Gradio, vector databases, classical ML, MLOps practices, GCP Vertex AI, AWS SageMaker, Streamlit

Video Processing, Tensorflow, Pytorch, Opencv, Python, ML deep learning fundamentals, IMU wearable sensor-fusion, Media Pipe, Signal Processing, scikit-learn, human activity recognition, time-series modelling, pose estimation

data engineering , Technical Leadership, python, Pytorch, Deep Learning, Machine Learning, Numpy, MLops, Azure, retrieval-augmented generation, vector search, langchain, mentorship, model deployment, embeddings, langgraph, llmops, agentic ai, production integration, model serving, prompt engineering, pipeline architecture

Keras, Sql, Decision Trees, Tensorflow, Pytorch, Python, Arima, Hypothesis Testing, Statistical Analysis, Z-Test, CNTK, Great Expectations, T-Test, Regression, probabilistic graph models, MXNet, support vector machines, exponential smoothing, ARIMAX, Sci-Kit Learn, KubeFlow, BentoML, Evidently AI

cloud platform , Artificial Intelligence - BASIC, Generative AI - Basic, Machine Learning, AI-Generative AI, Technology, AI-Data science, AWS Data Analytics

Image Processing, Data Science, deep learning frameworks, chatbot development, production-quality AI pipelines, generative AI models, AI applications, neural network architectures, cloud AI services, on-premises infrastructure

Python Programming, Databricks, Agent Frameworks Libraries, Prompt engineering