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https://github.com/codewithdark-git/ai-ml-intern-tasks

Welcome to the AI/ML Internship repository. This repository outlines a structured 3-week program designed to enhance your skills in machine learning and deep learning, focusing on medical data analysis and disease prediction.
https://github.com/codewithdark-git/ai-ml-intern-tasks

cnn cnn-classification decision-trees image-processing logistic-regression machine machine-learning python random-forest sklearn svm-classifier torch torchvision

Last synced: 6 months ago
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Welcome to the AI/ML Internship repository. This repository outlines a structured 3-week program designed to enhance your skills in machine learning and deep learning, focusing on medical data analysis and disease prediction.

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README 

          
# AI/ML Internship Tasks



Welcome to the AI/ML Internship repository. This repository outlines a structured 3-week program designed to enhance your skills in machine learning and deep learning, focusing on medical data analysis and disease prediction.



## Table of Contents



- [AI/ML Internship Tasks](#aiml-internship-tasks)

  - [Table of Contents](#table-of-contents)

  - [Repository Structure](#repository-structure)

  - [Week 1: Disease Prediction Using Patient Data](#week-1-disease-prediction-using-patient-data)

  - [Week 2: Cancer Detection Using Histopathological Images](#week-2-cancer-detection-using-histopathological-images)

  - [Week 3: Medical Image Classification](#week-3-medical-image-classification)

    - [1. Skin Cancer Detection](#1-skin-cancer-detection)

    - [2. Pneumonia Detection from Chest X-Rays](#2-pneumonia-detection-from-chest-x-rays)

  - [Learning Goals](#learning-goals)

  - [Tools \& Libraries](#tools--libraries)

  - [Submission Requirements](#submission-requirements)

  - [Bonus Task](#bonus-task)

  - [Getting Started](#getting-started)

  - [Prerequisites](#prerequisites)

  - [Installation](#installation)

  - [Usage](#usage)

  - [Contributing](#contributing)

  - [License](#license)

  - [Contact](#contact)



## Repository Structure

```python

AI-ML-Intern-ta

├── Week_1/

│   ├── Diabetes_prediction/

│   │   ├── data/

│   │   │   └── diabetes.csv

│   │   ├── diabetes_prediction.ipynb

│   │   |── app.py # The Streamlit Ui for testing save models

│   │   ├── models/

│   │   │   └── # Trained models saved here

│   │   └── README.md  # Documentation about the model proformance and insights

│   └── Heart_disease_prediction/

│       ├── data/

│       |   ├── heart-disease-dataset.zip

│       │   └── heart.csv

│       |   ├── heart-disease-dataset.zip

│       |── app.py # The Streamlit Ui for testing save models

|       |── heart_disease.ipynb

│       ├── models/

│       │   └── # Trained models saved here

│       └── README.md  # Documentation about the model proformance and insights

├── Week_2/

│   ├── Cancer_detection/

│   │   ├── data/

│   │   │   └── histopathology_images/

│   │   ├── notebooks/

│   │   │   └── cancer_detection.ipynb

│   │   ├── models/

│   │   │   └── cnn_model.h5

│   │   ├── reports/

│   │   │   └── cancer_detection_report.pdf

│   │   └── README.md

├── Week_3/

│   ├── Skin_cancer_detection/

│   │   ├── melamoma_cancer_dataset

|   |   |   ├── test

|   │   │   |  ├── melanoma

|   │   │   |  └── benign

|   │   |   └── train

|   |   |      ├── melanoma

|   |   |      └── benign

│   │   ├── skin_cancer_detection.ipynb

│   │   ├── Models/

│   │   │   └──             

│   │   └── README.md       # full documentation for the Skin-Cancer-detection Pipeline 

│   └── Pneumonia_detection/

│       ├── data/

│       │   └── chest_xrays/

│       ├── notebooks/

│       │   └── pneumonia_detection.ipynb

│       ├── models/

│       │   └── mobilenet_model.h5

│       ├── reports/

│       │   └── pneumonia_detection_report.pdf

│       └── README.md

├── LICENSE

├── requirements.txt

└── README.md  # Main README file with an overview of the internship tasks

```



## Week 1: Disease Prediction Using Patient Data



**Objective:** Train and evaluate machine learning models to predict diseases such as diabetes or heart disease.



**Tasks:**



1. **Dataset Acquisition:**

   - Download the dataset from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php).



2. **Data Preprocessing:**

   - Handle missing values appropriately.

   - Perform normalization of numerical features.

   - Encode categorical features using suitable encoding techniques.



3. **Exploratory Data Analysis (EDA):**

   - Analyze feature distributions and correlations.

   - Visualize data using histograms, scatter plots, and heatmaps.



4. **Model Training:**

   - Train models using Logistic Regression, Random Forest, and Support Vector Machine (SVM).



5. **Model Evaluation:**

   - Evaluate models with metrics such as accuracy, precision, recall, and F1-score.

   - Compare model performances to identify the best-performing algorithm.



**Outcome:** A comprehensive report summarizing the analysis, model performances, and insights.



## Week 2: Cancer Detection Using Histopathological Images



**Objective:** Utilize deep learning to detect cancerous cells from histopathological images.



**Tasks:**



1. **Dataset Acquisition:**

   - Access the [Breast Cancer Histopathology Images Dataset](https://www.kaggle.com/paultimothymooney/breast-histopathology-images) from Kaggle.



2. **Data Augmentation:**

   - Apply techniques such as rotation, flipping, and scaling to balance the dataset and enhance generalization.



3. **Model Implementation:**

   - Implement a Convolutional Neural Network (CNN) using frameworks like TensorFlow or PyTorch.

   - Explore Transfer Learning with pre-trained models such as ResNet or VGG16.



4. **Visualization:**

   - Highlight cancerous regions in images using Image Segmentation or Grad-CAM visualization techniques.



**Outcome:** A deep learning model capable of accurately detecting and highlighting cancerous regions.



## Week 3: Medical Image Classification



### 1. Skin Cancer Detection



**Objective:** Classify skin lesions into categories (e.g., benign or malignant).



**Dataset:** [ISIC Skin Cancer Dataset](https://www.isic-archive.com/).



**Steps:**



1. **Data Preprocessing:**

   - Load the dataset and normalize pixel values to [0, 1].

   - Resize images to a fixed size (e.g., 224x224) for compatibility with CNNs.

   - Split the dataset into training, validation, and testing sets.



2. **Data Augmentation:**

   - Apply techniques like random rotation, flipping, zooming, and brightness adjustment.



3. **Model Development:**

   - Utilize a pre-trained CNN model (e.g., ResNet50 or EfficientNet) with transfer learning.

   - Fine-tune the model by replacing the last fully connected layer with a dense layer for binary classification.

   - Use Binary Crossentropy as the loss function and Adam optimizer.



4. **Evaluation:**

   - Assess the model using accuracy, precision, recall, and F1-score.



**Deliverables:**

   - Code file (`skin_cancer_detection.py` or notebook).

   - A short report (1-2 pages) detailing model performance and insights.



### 2. Pneumonia Detection from Chest X-Rays



**Objective:** Classify chest X-ray images as pneumonia-positive or negative.



**Dataset:** [Chest X-Ray Images Dataset](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia) from Kaggle.



**Steps:**



1. **Data Preprocessing:**

   - Load and preprocess images (resize to 224x224, normalize pixel values).

   - Divide the dataset into training, validation, and testing sets.



2. **Data Augmentation:**

   - Apply augmentation techniques (e.g., random cropping, rotation, and histogram equalization).



3. **Model Development:**

   - Implement a CNN architecture or fine-tune a pre-trained model (e.g., MobileNet or InceptionV3).

   - Optimize the model with Categorical Crossentropy loss and Adam optimizer.



4. **Evaluation:**

   - Use metrics like sensitivity, specificity, and ROC-AUC score.

   - Compare results on augmented vs. non-augmented datasets.



**Deliverables:**

   - Code file (`pneumonia_detection.py` or notebook).

   - A summary report (1-2 pages) on model performance and challenges.



## Learning Goals



- Understand and apply Transfer Learning for medical image classification.

- Gain hands-on experience with data preprocessing and augmentation techniques.

- Comprehend evaluation metrics such as sensitivity, specificity, and F1-score.

- Explore the impact of fine-tuning pre-trained models on performance.



## Tools & Libraries



- **Frameworks:** TensorFlow/Keras or PyTorch.

- **Libraries:** OpenCV, NumPy, Pandas, Matplotlib, Scikit-learn.

- **Hardware:** Google Colab or a local system with GPU support.



## Submission Requirements



1. Code files or notebooks for each task.

2. Reports summarizing findings, challenges, and insights.

3. Model files (optional) and visualizations (charts/graphs) demonstrating results.

4. **Deadline:** End of Week 3.



## Bonus Task



For enthusiastic internees:



- Experiment with ensemble models combining multiple pre-trained CNNs.

- Perform hyperparameter tuning (e.g., learning rate, batch size, epochs) to enhance performance.



## Getting Started



To begin working on these tasks, clone the repository to your local machine:



```bash

git clone https://github.com/codewithdark-git/AI-ML-Intern-tasks.git

```



## Prerequisites



Ensure you have the following software installed:



- Python 3.x

- Jupyter Notebook

- Necessary Python libraries (listed in `requirements.txt`)



## Installation



Navigate to the project directory and install the required dependencies:



```bash

pip install -r requirements.txt

```



## Usage



Each task is contained within its respective directory. Open the Jupyter Notebook files to explore the code and follow the instructions provided within each notebook.



bash

jupyter notebook



## Contributing



Contributions are welcome! Please fork the repository and create a pull request with your enhancements or bug fixes.



## License



This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.



## Contact



For any questions or suggestions, feel free to reach out:



- **GitHub:** [codewithdark-git](https://github.com/codewithdark-git)

- **LinkedIn:** [Ahsan Umar](https://www.linkedin.com/in/codewithdark)

- **Kaggle:** [codewithdark](https://www.kaggle.com/codewithdark)

- **Email:** [codewithdark](mailto:codewithdark90@gmail.com)