26 professional certifications across AI, machine learning, MLOps, data science, blockchain and software engineering — the technical foundation behind the JusticeTech work.
Vivek Singh Chandel holds 26 professional certifications spanning generative AI, machine learning, MLOps, data science, blockchain and digital transformation — issued by IBM, Google, Duke University, the University of Michigan, the University at Buffalo (SUNY), the University of Virginia with BCG, DeepLearning.AI and Oracle, and delivered through Coursera and Udemy. This training is the technical foundation behind his JusticeTech work: AI-assisted legal research, NLP over case law, and Digital Public Infrastructure such as India's GSTAT e-Filing Portal.
A working map of AI, machine learning and deep learning — and where they create real value.
Covers core AI concepts and terminology, real-world use cases across industries, generative AI and natural-language tools, and the ethics and adoption of applied AI.
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Hands-on grounding in generative-AI models and how to put them to work.
Explores how generative models create text, images, code and audio, surveys leading tools and large language models, and applies prompt-driven workflows to practical tasks.
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Reliable prompt-engineering technique for steering large language models.
Teaches prompt patterns, in-context learning, and techniques such as templates and chaining to guide LLMs toward accurate, useful and repeatable responses.
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Professional-level command of building and deploying generative-AI solutions on Oracle Cloud.
Covers large language models, retrieval-augmented generation, embeddings and vector search, and the OCI Generative AI services used to ship production GenAI applications and agents.
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Foundational fluency in AI, ML and generative-AI services on Oracle Cloud Infrastructure.
Introduces core AI, machine-learning and deep-learning concepts, generative AI and LLMs, and the Oracle Cloud Infrastructure AI service portfolio.
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End-to-end skills to build retrieval-augmented GenAI applications.
Builds LLM applications with LangChain — chains, agents, memory and document loaders — using Pinecone vector search for retrieval-augmented generation over private data.
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A business lens on where and how to apply AI and ML for measurable impact.
Frames AI and machine-learning use cases, model selection and adoption strategy for leaders turning data into decisions and business outcomes.
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The ability to build and evaluate machine-learning models in Python.
Covers supervised and unsupervised learning — regression, classification, clustering and recommender systems — with scikit-learn, plus model evaluation and tuning.
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The ability to build and train neural networks with Keras.
Introduces neural-network fundamentals, forward- and back-propagation, and building convolutional and recurrent models in Keras for classification and regression.
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A deep understanding of the attention and transformer models behind modern NLP.
Covers attention mechanisms, transformers, neural machine translation, summarization and question answering — the architecture powering today's large language models and legal-NLP.
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A practical, applied ML workflow from features to evaluation.
Applies scikit-learn to real datasets — supervised learning, feature engineering, model evaluation and avoiding over- and under-fitting — with an emphasis on practical judgement.
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End-to-end ML delivery on Azure, from training to production.
Builds, trains and deploys models with Azure Machine Learning, then operationalizes them with MLOps pipelines, CI/CD and monitoring.
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Deploy and operate machine learning on the two major cloud MLOps platforms.
Hands-on MLOps with Amazon SageMaker and Azure ML — training, deployment, endpoints and lifecycle management on managed cloud platforms.
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Track, package and ship models with industry-standard MLOps tooling.
Uses MLflow for experiment tracking, model registry and packaging, and Hugging Face for hosting and deploying open-source models.
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A unified operations mindset across software, data and machine learning.
Connects DevOps, DataOps and MLOps — CI/CD, automation, testing and reproducible pipelines — for shipping data and ML systems reliably.
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Production-grade Python foundations for machine-learning engineering.
Covers Python scripting, testing, data structures, command-line tooling and applied data handling that underpin reliable MLOps workflows.
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The core MLOps concepts and the ML lifecycle in practice.
Introduces the MLOps lifecycle — versioning, pipelines, deployment and monitoring — with practical examples on Microsoft Azure.
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A clear map of the data-science field and how it drives decisions.
Defines data science, the roles and skills involved, the data-science methodology, and how analytics informs decisions across organizations.
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Practical data manipulation and analysis in Python.
Uses pandas and NumPy to clean, manipulate and analyse tabular data, with descriptive statistics and hypothesis testing on real datasets.
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A structured method for turning questions into data-driven decisions.
Part of the Google Data Analytics program — framing problems, asking effective questions and structuring data to guide sound decisions.
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End-to-end command of blockchain — from protocol to deployed dApps.
The full University at Buffalo Blockchain specialization: cryptography and consensus, Solidity smart contracts, decentralized applications, and blockchain platforms and design.
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How a blockchain actually works, from hashing to consensus.
Covers cryptographic hashing, public-key cryptography, transactions, blocks, distributed consensus and the Ethereum model.
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The ability to design and write smart contracts in Solidity.
Programs smart contracts in Solidity on Ethereum — structure, functions, modifiers and secure design patterns — deploying them to a test network.
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Build full decentralized applications on Ethereum.
Designs and builds end-to-end dApps — connecting web front-ends to smart contracts with web3 tooling and a decentralized application stack.
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Grounding in the software-engineering lifecycle and modern practice.
Covers the SDLC, software architecture and design, agile methods, and the tools and roles that turn requirements into working software.
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A strategy framework for leading technology-driven transformation.
From UVA Darden and BCG — how digital technologies reshape strategy, operations and business models, and how to lead transformation at scale.
View on CourseraThese certifications sit behind the four practice fronts on the about page — from AI and data analytics for justice to the systems work on the GSTAT e-Filing Portal. Open any certificate to view it in full.