Microsoft Certified - Azure AI-102
AI Engineer Associate

By Microsoft

Azure AI Services Setup & Security: Learned to provision and secure Azure AI resources using API keys, managed identities, and RBAC through hands on portal and SDK practice.
Computer Vision & Image Analysis: Built solutions using Azure AI Vision for OCR, object detection, and image classification by working through real world image processing scenarios.
Natural Language Processing (NLP): Implemented text analytics, sentiment analysis, and entity recognition using Azure AI Language service through guided labs and API exploration.
Azure OpenAI & Generative AI: Explored prompt engineering, embeddings, and deploying GPT models via Azure OpenAI Studio to build intelligent, language driven applications.
Conversational AI with Bot Framework & CLU: Designed and deployed question answering bots and custom language understanding models using Azure AI Language and Bot Framework Composer.
Document Intelligence & Knowledge Mining: Extracted structured data from documents using Azure AI Document Intelligence and built enrichment pipelines with Azure AI Search and custom skillsets.

BCG X - GenAI
Job Simulation

By BCG X

Completed a job simulation involving AI-powered financial chatbot development for BCG's GenAI Consulting team.
Gained experience in Python programming, including the use of libraries such as pandas for data manipulation.
Integrated and interpreted complex financial data from 10-K and 10-Q reports, employing rule-based logic to create a chatbot that provides user-friendly financial insights and analysis.

By Google

Learned concepts of data life cycle and data analysis process.
Learned effective questioning techniques and structured thinking for developing solutions.
Learned how to ensure data credibility and different ways to access and use databases.
Applied SQL to clean and transform data.
Used SQL to complete calculations, formulae, and functions to aggregate data in spreadsheets.
Created dashboards using Tableau to form a compelling narrative through data stories.
Explored fundamentals and manipulated as well as visualized data using R programming.
Discovered real world job interviews scenarios and case studies.

By DeepLearning.AI

Learned deep understanding of how neural networks work and how to use AI powered solutions.
Worked with real world images of different shapes and sizes, and learned how a computer parses an image.
Plotted loss and accuracy and learned transfer learning as well as how to extract features from images.
Built natural language processing systems and learned how to process text, tokenizers and representing sentences as vectors. Applied RNNs, GRUs and LSTMs in TensorFlow.
Built a time series model in TensorFlow and used RNNs and 1D ConvNets for prediction.

By University of Michigan

Worked with Python to learn fundamentals such as Lambdas, reading and manipulating csv files as well as NumPy library. Learned how to use tabular data and clean it and manipulate it using pandas.
Used matplotlib in Python to plot charts and make proper visualizations.
Worked with Machine Learning libraries of Python like Scikit-Learn to create supervised and unsupervised techniques, engineer features and learned which model should be applied to what kind of data and problem statement.
Used NLTK framework of Python to understand how text is handled by Python. Learned common manipulation needs, including regular expressions, cleaning text, and preparing text for use by machine learning processes like topic modelling.
Learned how to use NetworkX library of Python and how important network analysis is in Machine Learning.