Certificates

A showcase of my certificates from Microsoft, MIT, IBM, and more. Each card includes a description of what I learned and a link to the certificate or related project.

Duke University

MLOps Platforms: Amazon SageMaker and Azure ML

Orchestration of end-to-end machine learning operations on enterprise cloud platforms. Emphasis is placed on production-grade workflow automation, model monitoring, and lifecycle management, equipping participants with the expertise to architect scalable, secure, and maintainable MLOps solutions.

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Microsoft

Computer Vision in Microsoft Azure

Hands-on experience integrating Azure Computer Vision APIs, orchestrating image data pipelines, and automating document processing tasks such as receipt and invoice extraction. Leveraging cloud-native AI services for scalable, enterprise-grade visual perception systems. The architecture and operational principles of Azure pre-trained deep learning models for image analysis, classification, object detection, facial recognition, and optical character recognition (OCR).

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Google

Encoder-Decoder Architecture

Foundational architecture for sequence-to-sequence machine learning tasks such as neural machine translation, abstractive text summarization, and question answering. Hands-on implementation using TensorFlow and Keras, constructing and training a custom encoder-decoder model for generative tasks like poetry synthesis.

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DeepLearning.AI

Machine Learning in Production

Data and model pipelines, addressing concept drift and data evolution, Model baseline selection, performance auditing, Error analysis, handling skewed or imbalanced datasets, Data validation, augmentation, label consistency, Continuous monitoring and progressive deployment patterns

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IBM

Al Agents Using RAG and LangChain

RAG workflows with PyTorch and Hugging Face Transformers for content evaluation and dataset querying. Advanced prompt engineering techniques for optimizing in-context learning and response accuracy. LangChain modular architecture for building AI agents, including document loaders, retrievers, chains, and dynamic prompt templates. Deploying integrated LLM-RAG systems for applications like chatbots and interactive document Q&A.

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IBM

Enterprise Model Deployment

Using Apache Spark for scalable data manipulation, model training, hyperparameter tuning, and building recommender systems. Hands-on experience with Spark RDDs, dataframes, pipelines, and spark-submit scripting, alongside containerization with Docker and deployment workflows integrating IBM Watson Studio and Watson Machine Learning.

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Duke University

Large Language Model Operations (LLMOps)

Architecting and scaling LLM pipelines for production environments. Fine-tuning and serving LLMs using open-source and commercial frameworks. Integrating LLMs with cloud-native services (Azure AI, AWS SageMaker, Databricks). Implementing data engineering, ETL, and workflow management for LLM applications.Leveraging tools such as Semantic Kernel, Llamafile, and vector search technologies

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Google

ML Pipelines on Google Cloud

Orchestrating robust ML pipelines using TFX and Apache Beam. Containerizing training applications across TensorFlow, PyTorch, Scikit-learn, and XGBoost. Implementing continuous training and deployment using KubeFlow, AI Platform Pipelines, and Cloud Composer (Apache Airflow). Managing the complete ML lifecycle with MLflow, including experiment tracking, model registry, and reproducibility

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IBM

Generative Al Engineering and Fine-Tuning Transformers

Covering transformer architectures and generative model frameworks, with comparative analysis of Hugging Face and PyTorch platforms. Parameter-efficient fine-tuning (PEFT) techniques, including Low-Rank Adaptation (LoRA) and Quantized LoRA (QLoRA), for scalable and resource-efficient model adaptation. End-to-end workflows for loading, pre-training, and fine-tuning LLMs on domain-specific tasks using Hugging Face and PyTorch. Model quantization strategies, Extensive hands-on labs involving model loading, inference, and training, equipping learners with job-ready skills sought after in industry

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Duke University

GenAl and LLMs on AWS

Deploying LLMs with Amazon Bedrock, including model evaluation, customization, and integration via AWS SDKs. Serverless solutions using AWS Lambda and developing with the AWS SDK for Rust. CodeWhisperer for AI-assisted programming, prompt engineering, and CLI tool automation. MLOps, continuous monitoring (CloudWatch), secure coding, and cost-performance optimization

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Google

Attention Mechanism

Theoretical underpinnings of attention illustrating how it enables models to dynamically focus on relevant segments of input sequences, thereby enhancing performance in tasks such as machine translation, text summarization, and question answering. Implementation of attention from scratch within modern neural architectures and compare its advantages over traditional sequence modeling approaches.

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Columbia University

Visual Perception

Designing algorithms for detecting meaningful scene changes. Object tracking methods in video sequences. Segmenting images into semantically meaningful regions. Constructing end-to-end pipelines for object learning and recognition

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Duke University

Operationalizing LLMs on Azure

Implementing Retrieval-Augmented Generation (RAG) with Azure AI Search for context-aware LLM responses. Automating deployments via GitHub Actions and containerized workflows. Advanced query optimization using Semantic Kernel for LLM interaction. Risk mitigation strategies, quota management, and cost-performance optimization in Azure.

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Meta

Database Structures and Management with MySQL

Relational schema design, normalization, indexing, transactions, and database security. Implementing primary and foreign keys; constructing joins, unions, and subqueries; and managing views and stored procedures. Data modeling, integrity, and optimization, preparing participants to manage scalable and robust database systems in real-world applications

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IBM

AI Workflow: Machine Learning, Visual Recognition and NLP

Linear models, tree-based algorithms, Hyperparameter tuning via grid search, cross-validation, construction of neural network models in TensorFlow. Integration and deployment of IBM Watson Visual Recognition and Natural Language Understanding (NLU) services. Real-world case studies in image analysis and natural language processing, contextualized for enterprise-scale model pipelines.

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Duke University

Databricks to Local LLMs

Architecture of Databricks, cluster management, notebook-based analytics, and data transformation using Spark SQL and Delta Lake. End-to-end ETL pipelines, Delta Live Tables, and workflow orchestration in data pipeline automation and governance. Responsible AI deployment strategies. local LLMs such as Mixtral using tools like Mozilla llamafile and Hugging Face Candle.

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IBM

Generative Al Advance Fine-Tuning for LLMs

Instruction-Tuning & Reward Modeling. Implementation via Hugging Face, including dataset preprocessing, Low-Rank Adaptation (LoRA) configuration, and reward trainer workflows. Reinforcement Learning from Human Feedback (RLHF), Proximal Policy Optimization (PPO) for dynamic policy adaptation and scoring function design. Direct Preference Optimization (DPO) using partition functions and Hugging Face integrations.

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The Linux Foundation

Introduction to Quantum Circuits

Core quantum computing concepts, including superposition, qubits, entanglement, and quantum gates. uilding quantum circuits using IBM Q Experience Visual Composer, deploying them on real hardware/simulators, and exploring algorithms like Deutsch Algorithm.

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University of Michigan

Advanced Concepts in Time Value of Money (TVM)

Explored advanced concepts in the time value of money and financial decision making.

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Duke University

Virtualization, Docker, and Kubernetes for Data Engineering

Virtual machines, hardware utilization, and scaling applications for efficient resource management. Docker architecture, Dockerfile creation, multi-container orchestration with Docker Compose, and workflow automation using Apache Airflow. Kubernetes cluster architecture, service deployment, and cloud-native development using platforms like GitHub Codespaces. AI-driven coding tools (e.g., GitHub Copilot), container image management with Azure and AWS registries, and Site Reliability Engineering (SRE) methodologies

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Princeton University

Analysis of Algorithms

Recurrence relations and asymptotic approximations. Generating functions as a bridge between algorithmic structures and analytic combinatorics. Symbolic methods applied to permutations, trees, strings, and mappings. Designed by Robert Sedgewick, co-author of Algorithms, 4th Edition.

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IBM

Generative Al Language Modeling with Transformers

Positional encoding, attention mechanisms (including self-attention and multi-head attention), and transformer encoder/decoder structures. Construction of encoder-based BERT and decoder-based GPT models using PyTorch, emphasizing text classification, translation, and masked language modeling (MLM). Training bidirectional encoder models (BERT) via MLM and next sentence prediction (NSP). Building generative decoder models (GPT) for language translation and text generation

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Duke University

MLOps | Machine Learning Operations

Data preprocessing, feature extraction, and model training pipelines. Deploying ML models as cloud-native and containerized solutions (Docker, ONNX). Fine-tuning and operationalizing Large Language Models (LLMs) using Hugging Face. Implementing GPU-accelerated ML tasks with Rust. Full MLOps pipelines with MLflow for experiment tracking, model registry, and reproducibility. CLI tools for model interaction with frameworks such as Gradio and Click

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Meta

Coding Interview Preparation

Prepared for coding interviews with a focus on algorithms, data structures, and problem-solving.

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Microsoft

Microsoft Azure Machine Learning for Data Scientists

Building, training, and evaluating regression, classification, and clustering models using Azure Machine Learning Designer and automated machine learning (AutoML). Deploying and operationalizing predictive models as scalable endpoints, with a focus on no-code and low-code solutions. Azure Databricks, MLflow, and Scikit-learn for advanced experimentation and production deployment.

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Google

Regression Analysis: Simplify Complex Data Relationships

Assumptions and Construction of Regression Models. Statistical metrics such as R-squared, mean squared error, and hypothesis testing for model assessment. analysis of variance (ANOVA) for comparing group means, and logistic regression for modeling categorical outcomes, including binomial, multinomial, and Poisson log-linear models. Predictive analytics, correlation versus causation, and the use of regression to identify key drivers in business and scientific datasets

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University of Michigan

Advanced Understanding of Stocks and Bonds

Bond pricing, yield calculations, and the multiple sources of risk inherent in fixed-income securities, including interest rate risk, credit risk, and reinvestment risk. Equity valuation models, the impact of different growth scenarios (both beneficial and detrimental), and the mechanics of value creation within firms. How companies and governments make borrowing decisions, and how these affect firm value, investor returns, and market dynamics.

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Google

Google Advanced Data Analytics

Descriptive and inferential statistics, probability, sampling, confidence intervals, hypothesis testing, ANOVA, supervised, unsupervised machine learning algorithms, data examination and predictive modeling

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Microsoft

Create Machine Learning Models in Microsoft Azure

Regression, classification, clustering, and deep learning models using Scikit-Learn, TensorFlow, and PyTorch. Hyperparameter tuning, and automated ML (AutoML) pipelines. Scalable endpoints using Azure Kubernetes Service (AKS). CNNs, transfer learning, and GPU-accelerated training.

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Duke University

Open Source LLMOps Solutions

State-of-the-art open-source LLMs (e.g., LLaMA, Mistral) locally using tools like Llamafile and Whisper.cpp, with API integration and metrics monitoring. Customizing pre-trained models for domain-specific tasks, and leveraging LLMs to generate synthetic datasets. Deploying models in-browser using Transformers.js and ONNX Runtime, and developing portable AI solutions with Cosmopolitan. Retrieval-augmented generation (RAG), GPU-accelerated pipelines, and containerized deployment using modern open-source infrastructure

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Meta

Advanced MySQL Topics

User-Defined Functions and Stored Procedures. Triggers and Scheduled Events. Common table expressions (CTEs), prepared statements, and transactions. MySQL native JSON data type for semi-structured data. Database Administration.

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Duke University

MLOps Tools: MLflow and Hugging Face

Installation, experiment tracking, model and artifact registry, and project management. reproducible ML pipelines, interact with registered models via APIs, and integrate with Databricks for scalable tracking and deployment. Utilization of the Hugging Face Hub, Docker, FastAPI, and Azure ML Studio. Fine-tuning transformer models, exporting to ONNX, GPU-enabled workflows, and troubleshooting production deployments.

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DeepLearning.AI

Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Learned the basics of TensorFlow for AI, ML, and deep learning applications.

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University of Michigan

Foundational Finance for Strategic Decision Making

Learned the fundamentals of finance and how to apply them to strategic business decisions, including capital budgeting, risk analysis, and value creation.

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Duke University

Advanced Data Engineering

Developed expertise in building scalable data pipelines, ETL processes, and data engineering on cloud platforms.

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Duke University

DevOps, DataOps, MLOps

Modern MLOps tools, (CI/CD), Containerization, GitHub Copilot, Gradio, Hugging Face, Transitioning from Python to Rust for GPU-accelerated ML tasks, Click framework, Docker, Kubernetes, and serverless architectures.

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University of Michigan

Stocks and Bonds

Structure and pricing of various bond types, including zero-coupon and government bonds. Stock pricing and valuation, including growth and dividend stocks. How corporations and governments raise capital, and the similarities between individual and institutional borrowing.

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University of Michigan

Introduction to Time Value of Money (TVM)

Simple Future Value (FV), Simple Present Value (PV), Future Value of Annuity, Loans, Compounding, and Valuing Perpetuities. Systematic development of TVM formulas and applications. TVM for personal and corporate finance, including capital budgeting and cash flow analysis.

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Google

Google Advanced Data Analytics Capstone

complex data exploration, predictive modeling, and rigorous statistical inference using industry-standard tools. Analytical methodologies, effective data visualization, and the articulation of actionable business insights.

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RedHat

Fundamentals of Red Hat Enterprise Linux

Linux system administration within enterprise-grade environments, command-line interface operations, hierarchical file system architecture, software package provisioning, and network stack configuration.

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