DRCFS Pokhara Bootcamp Official Repo.

This repository contains the code and resources for the 7 Days of Python Bootcamp conducted by drcfs and Mercantile Cloud. The bootcamp covers various topics in machine learning and data analysis using Python. Each topic has its own folder with relevant notebooks and additional resources.

File Structure

├── Clustering
│   ├── k means.ipynb
│   └── kmeans.pdf
├── Linear Regression
│   ├── Addl Resources
│   │   ├── linear regression on dummy dataset 1.py
│   │   ├── linear regression on dummy dataset 2.py
│   │   └── linear regression on real dataset 1.py
│   └── Linear_Regression_ML_day_1.ipynb
├── Logistic Regression
│   ├── Addl Resources
│   │   ├── Logistic Regression 1.py
│   │   ├── Logistic Regression 2.py
│   │   ├── Logistic Regression 3.py
│   │   └── Logistic Regression 4.py
│   └── logistic_regression_day_2.ipynb
├── Machine Learning Bootcamp.pdf
├── Model Validation
│   └── model_validation.ipynb
├── Neural Networks and CNN
│   ├── CNN_Architecture.ipynb
│   ├── Neural_Network,_CNN_and_comparision_with_Logistic_and_SVC_Classifiers.ipynb
│   └── Neural Networks.pptx
└── readme.md

Folder Structure

Clustering: This folder contains notebooks and resources related to clustering algorithms.

k means.ipynb: Jupyter Notebook explaining the k-means clustering algorithm. kmeans.pdf: Additional resource on k-means clustering. Linear Regression: This folder contains notebooks and additional resources for linear regression.

Addl Resources: This subfolder contains additional resources for linear regression. linear regression on dummy dataset 1.py: Python script demonstrating linear regression on a dummy dataset.

linear regression on dummy dataset 2.py: Python script demonstrating linear regression on another dummy dataset.

linear regression on real dataset 1.py: Python script demonstrating linear regression on a real-world dataset.

Linear_Regression_ML_day_1.ipynb: Jupyter Notebook covering linear regression concepts. Logistic Regression: This folder contains notebooks and additional resources for logistic regression.

Addl Resources: This subfolder contains additional resources for logistic regression.

Logistic Regression 1.py: Python script demonstrating logistic regression.

Logistic Regression 2.py: Python script demonstrating logistic regression with different parameters.

Logistic Regression 3.py: Python script demonstrating logistic regression on a different dataset.

Logistic Regression 4.py: Python script demonstrating logistic regression with cross-validation.

logistic_regression_day_2.ipynb: Jupyter Notebook covering logistic regression concepts. Machine Learning Bootcamp.pdf: PDF document containing the bootcamp material and syllabus.

Model Validation: This folder contains a notebook related to model validation.

model_validation.ipynb: Jupyter Notebook explaining model validation techniques.

Neural Networks and CNN: This folder contains notebooks and a presentation on neural networks and convolutional neural networks (CNN).

CNN_Architecture.ipynb: Jupyter Notebook explaining CNN architecture.

Neural_Network,_CNN_and_comparision_with_Logistic_and_SVC_Classifiers.ipynb: Jupyter Notebook comparing neural networks, CNN, logistic regression