Activities

A foundational course completed through the AWS machine learning scholarship pathway. Topics covered: machine learning workflow, core machine learning algorithms, Python programming practices, Amazon SageMaker, and AWS AI devices such as DeepLens, DeepRacer, and DeepComposer.

An introduction to how open source software projects are organized and sustained. Topics covered: open source history, governance models, collaboration practices, licensing, continuous integration, Git, GitHub, and diversity in open source communities.

A mathematics foundation course for data science learners. Topics covered: set theory, inequalities, interval notation, sigma notation, functions and graphs, exponents, logarithms, probability, and Bayes' theorem.

An enterprise-focused course on exploratory data analysis and statistical investigation. Topics covered: data visualization, missing-data strategies, imputation, probability distributions, hypothesis testing, ANOVA, multiple testing, dashboards, and case-study-based analysis.

A professional AWS certification validating end-to-end analytics knowledge on the AWS platform. Topics covered: data collection, storage and data management, processing, analysis and visualization, security, architecture patterns, service integrations, scaling, and cost-performance considerations.

A linear algebra course designed for machine learning applications. Topics covered: vectors, matrices, linear transformations, systems of equations, determinants, eigenvalues, eigenvectors, and applying these ideas in Python and Jupyter.

A course on large-scale data analysis using Hive and the Spark ecosystem. Topics covered: Hive queries, Spark SQL, DataFrames, GraphFrames, graph analytics, PageRank-style analysis, query execution, and Spark performance optimization.

A practical introduction to Python for data analysis. Topics covered: Python basics, CSV handling, NumPy, pandas Series and DataFrames, data cleaning, groupby, merge, pivot tables, sampling, distributions, and t-tests.

A foundational cloud computing course focused on the Google Cloud platform. Topics covered: Google Cloud concepts and terminology, projects and resource hierarchy, identity and access management, Compute Engine, VPC networking, load balancing, storage options, Kubernetes, and Cloud Run.

A computer vision course focused on deep learning with convolutional models. Topics covered: convolutional neural network architecture, residual networks, detection and recognition tasks, transfer learning, and neural style transfer for image applications.

An introductory course aimed at building practical understanding of deep learning systems. Topics covered: neural network fundamentals, practical model-building workflows, training considerations, and real-world deep learning applications.

A broad introduction to machine learning and statistical pattern recognition. Topics covered: supervised learning, unsupervised learning, neural networks, support vector machines, model evaluation, bias-variance trade-offs, anomaly detection, recommender systems, and practical machine learning applications.

A project-oriented deep learning course with practical coding exercises. Topics covered: artificial neural networks, convolutional neural networks, recurrent neural networks, self-organizing maps, Boltzmann machines, autoencoders, TensorFlow/Keras workflows, and applied case studies.

An introductory programming course for R-based data analysis. Topics covered: basic operations in R, data types, vectors, factors, lists, data frames, and using R with real datasets for analysis.

An industry internship focused on applied artificial intelligence development. Topics covered: software implementation, face recognition, detection pipelines, classification workflows, and practical AI application development.

5-day training on Machine Learning fundamentals and applications.

5-day workshop focused on deep learning models for computer vision tasks.

3-day workshop covering statistical methods for data analysis.

10-day internship focusing on IoT architecture and development.

Participation in the national-level hackathon for developing innovative solutions.

Workshop on functional programming paradigms using Clojure.