Certifications
Courses and credentials I've completed are listed here in reverse-chronological order.
My Learning Profiles: Credly · Accredible · Udemy · DeepLearning.AI
Multi AI Agent Systems with crewAI
Micro-course · DeepLearning.AI · Issued March 2026
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Credential ID: 854d558e-71ca-49aa-bef8-57d969e44548
Skills: crewAI · AI Agents · Tools · Multi agent collaboration · Gen AI
This short course features hands-on learning with crewAI to build multi-agent AI systems. It covers agent roles, memory, tools, task decomposition, guardrails, and agent cooperation, with practical applications across resume tailoring, technical research and writing, customer support, outreach campaigns, event planning, and financial analysis.
Deploy ML Model in Production with FastAPI and Docker
Micro-course · Udemy · Issued Sept 2022
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Credential ID: UC-2e02bdb7-c9c6-49b4-9b58-910b06235d4a
Skills: Model Deployment · FastAPI · Streamlit · Model monitoring · MLOps
This course focuses on production-grade machine learning deployment, covering FastAPI-based model serving, AWS EC2 and S3, Docker containerization, and automated ML operations with Boto3. It also explores real-time inference APIs, end-to-end ML pipelines, Streamlit applications, and secure, scalable cloud infrastructure. Learners gain practical experience deploying NLP and computer vision Transformers, while implementing monitoring, A/B testing, and bias detection to maintain reliable ML systems in production.
Mastering Data Visualization: Theory and Foundations
Micro-course · Udemy · Issued Sept 2022
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Credential ID: UC-d7364fa8-c275-4d86-af9d-ad87584f589f
Skills: Data Visualization · Design Theory · Storytelling · Plotting
This course covers the technical principles of effective data visualization, including graphical perception, visual encoding, data integrity, dimensionality, density, scale, and proportion. It explores statistical pitfalls such as correlation, selection bias, context, normalization, and Simpson’s paradox. The course also examines plot selection for distributions, relationships, rankings, comparisons, and spatial data, alongside common visualization errors involving axes, shading, color, chart design, and data representation.
The Web Developer Bootcamp 2022 by Colt Steele
Bootcamp · Udemy · Issued March 2022
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Credential ID: UC-dd5b975f-6c77-4f66-b050-57440d23b881
Skills: HTML · CSS · JavaScript · Bootstrap · Node.js · MongoDB · Full stack dev
A 60+ hour full-stack bootcamp covering HTML, CSS, JavaScript, Node.js, Express, MongoDB, and deployment — built several full projects including a restaurant app and a campground review platform along the way.
Node.js Course
6 week course · Internshala · Issued Sept 2021
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Credential ID: 1B65E568-F08A-D1C7-3589-5DF34E827053
Skills: JavaScript · Node.js · MongoDB · Backend dev
Successfully completed a 6 weeks online certified training on Node.js. The training consisted of Introduction to Node.js and Installation Guide, JavaScript Fundamentals, JavaScript Asynchronous programming, Node Modules, Node Web Application with Express, and Final project - Connecting a webapp to MongoDB using AJAX and node modules. I scored 97% in the final assessment and was a top performer in the training.
Deep Learning Specialization by DeepLearning.AI
Specialization · Coursera · Issued May 2020
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Credential ID: N2YG4SK7RU8Y
Skills: Deep Learning · Neural Networks · Tensorflow · MLPs · CNNs · RNNs · LSTMs · GRUs
The Deep Learning Specialization, taught by Andrew Ng, provides a comprehensive technical foundation in deep learning and artificial neural networks. Structured into five courses, it progresses from the fundamentals of neural networks and backpropagation to the design, and optimization of modern deep learning systems. The curriculum covers neural network architectures, activation functions, forward and backward propagation, hyperparameter tuning, regularization, optimization algorithms, and machine learning project structuring. It then moves into specialized architectures, including Convolutional Neural Networks (CNNs) for computer vision and Recurrent Neural Networks (RNNs), LSTMs, and GRUs for sequence modeling, with applications in image recognition, face verification, speech recognition, natural language processing, and time-series analysis. The specialization also emphasizes practical model development through Python-based programming assignments, covering implementation and experimentation with deep learning models using frameworks such as TensorFlow.
Stanford Machine Learning by Andrew Ng
11 week course · Coursera · Issued April 2020
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Credential ID: TGJHCALADT3K
Skills: Machine Learning · Supervised Learning · Unsupervised Learning · Neural Networks · Linear Algebra · MATLAB
A ~60-hour, 11-week introductory ML course covering linear algebra foundations, linear/logistic regression, regularization, and neural networks (representation, learning, multiclass classification, digit recognition), SVMs, unsupervised learning (clustering, PCA, anomaly detection), recommender systems, different flavours of gradient descent, and online learning. Includes MATLAB/Octave-based programming.