Added Code for Multiple Concepts

[iSiddharth20]

Hello Tinny-Robot,

Added Working Code with Comments, requirements.txt, README.md for each of the following Concepts:

• Data Cleaning

  • Import Dataset (CSV) from URLs using Pandas
  • Identify and Eliminate Null Values, Outliers using NumPy and Pandas
  • Visualize every step using Matplotlib and Seaborn
  • Train a Basic Classification Model using Scikit-Learn

• Computer Vision

  • Extracting Largest Object from any PNG/JPEG Image using OpenCV

• Deep Learning/Image Classification

  • OpenCV, TensorFlow, NumPy, Pandas, Pickle
  • Detailed Pre-Processing in a Notebook with Comments and Visual Verification of Each Step
  • Detailed Model Creation and Training in a Notebook of ResNet50 CNN Architecture.
  • Detailed Verification of Model, Creation of Confusion Matrix in a Notebook.

Added '.gitignore' file to keep unnecessary items out of the repo.

Updated 'LEARN.md' to fix broken URLs and added relative paths to the newly added code.

Thank You!

[iSiddharth20]

Hello @Tinny-Robot I have added codebase for multiple concepts. Entire codebase is in Jupyter Notebooks with proper explanation, comments, visual verification techniques. Each concept has its own requirements.txt and Readme.md file for cleaner and one stop learning experience. I hope you like it!

[Tinny-Robot]

Hello @iSiddharth20,

First of all, thank you for your contribution, and I appreciate the additions. However, there are a few improvements that need to be made to your pull request to align with our project's standards:

1. Data Organization: Please move all data files to the /data folder within the repository and update your code to reference these files directly from that location. This helps maintain a well-organized project structure.

2. Markdown Description: Instead of a separate README or MD file, we prefer to have all descriptions and explanations within the Jupyter notebook itself as Markdown cells. Please add an elaborate and self-explanatory Markdown section within your notebook that provides the necessary context and explanations for your code.

3. Data Description: It's important to include a data description within your notebook. This should detail the source, format, and any preprocessing steps for the data you are working with. Providing this information helps others understand the data's significance to your analysis.

Once you've made these changes, please let us know, and we'll be happy to review your pull request again.

Additionally, if you find our project valuable, we'd greatly appreciate it if you could star the repository to show your support.

If you have any questions or need assistance with these changes, please feel free to ask. We're here to help you make your contribution even better!

Thank you for your understanding, and we look forward to seeing the improvements in your pull request.

Best regards, Tinny-Robot

[iSiddharth20]

Hello @Tinny-Robot, Thank You for reviewing and providing me with the list of changes. I have made the changes as requested. Kindly review and let me know if anything else is needed.