Welcome to my living repository of notes and resources on Data Science and Software Engineering. This repository is continuously updated as I grow and learn more in my career. It serves as my personal knowledge base, and I hope it can be useful to others as well.
This repository is a dynamic and ever-growing collection of notes, code snippets, and resources that I have gathered throughout my journey in Data Science and Software Engineering. As I continue to learn and evolve in my career, this repository will be frequently updated with new content, directories, and insights.
Notes and resources on building and consuming APIs. Includes various examples and best practices.
A project clone of the AirBnB web application. Contains detailed notes on the development process, architecture, and key learnings.
A collection of personal projects and experiments in data science and software engineering. This directory showcases some of my hands-on work and innovative ideas.
Scripts and notes on Bash scripting for automation, system management, and enhancing productivity. Includes practical examples and tips.
Notes and examples on programming in C. Covers fundamental concepts, data structures, and algorithms.
Notes and resources on deep learning, including theory, practical implementations, and case studies. Covers neural networks, CNNs, RNNs, and more.
Resources and notes on DevOps practices, tools, and methodologies. Covers CI/CD pipelines, containerization, orchestration, and monitoring.
Notes and examples on JavaScript programming and web development. Includes ES6+ features, frameworks, and best practices.
Resources and notes on Natural Language Processing (NLP) techniques and applications. Covers text preprocessing, sentiment analysis, topic modeling, and more.
Notes on product management principles, tools, and best practices. Includes strategies for product development, roadmaps, and user research.
A collection of notes on Python programming, covering various libraries and frameworks. Includes data manipulation, web development, automation, and more.
Notes on regression analysis and related statistical methods. Covers linear regression, logistic regression, regularization techniques, and applications.
Resources and notes on SQL for database management and data analysis. Includes queries, optimization techniques, and database design principles.
Notes on statistical methods and their implementation in Python. Covers descriptive statistics, inferential statistics, hypothesis testing, and more.
Resources and notes on unsupervised learning algorithms and applications. Includes clustering, dimensionality reduction, anomaly detection, and more.
This is a personal repository, but I welcome suggestions and improvements. If you have any ideas or corrections, feel free to open an issue or submit a pull request.
This repository is licensed under the MIT License.
If you have any questions or would like to connect, feel free to reach out. I'm always open to discussions and collaborations.
Stay tuned for more updates as I continue to expand this repository with new learnings and insights!