📂 YoungDevInterns_Machine-Learning_Tasks

👨‍💻 Abdul Rafay

Bachelor of Science in Software Engineering

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🏢 YoungDev Intern - Machine Learning Internship

This repository documents my progress as a Machine Learning Intern at YoungDev Intern. It includes hands-on tasks across three levels: Basic, Intermediate, and Expert, designed to deepen my understanding and practical knowledge of AI and ML.

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📘 Basic Tasks

These tasks are designed to build foundational understanding of ML concepts and tools.

✅ Task 1: Implement a Simple Linear Regression

• Load a dataset (e.g., house prices or student scores)
• Apply simple linear regression
• Visualize the regression line
• Evaluate with metrics like MSE or R²

✅ Task 2: Classify Data with a Decision Tree

• Use a labeled dataset (e.g., Iris or Titanic)
• Train a decision tree classifier
• Visualize the decision tree
• Interpret decision boundaries

✅ Task 3: Visualize Data with a Scatter Plot

• Choose two variables from a dataset
• Plot them using matplotlib or seaborn
• Add colors or labels for categories if applicable
• Use the visualization to observe correlations or clusters

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📗 Intermediate Tasks

These tasks help in understanding the intricacies of data processing and model evaluation.

🚀 Task 1: Build a Model with Cross-Validation

• Implement k-fold cross-validation
• Evaluate model consistency across folds
• Use sklearn's cross_val_score

🚀 Task 2: Preprocess Data for Machine Learning

• Handle missing values
• Normalize or scale features
• Encode categorical variables
• Split into training and testing sets

🚀 Task 3: Create a Classification Report

• Train a classification model
• Predict test labels
• Generate a report with precision, recall, f1-score using classification_report

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📙 Expert Tasks

These tasks push deeper into complex modeling, optimization, and deployment.

🌟 Task 1: Develop a Neural Network for Classification

• Use frameworks like TensorFlow or PyTorch
• Build a feedforward neural network
• Train and validate on a dataset (e.g., MNIST or CIFAR-10)
• Track accuracy and loss

🌟 Task 2: Implement Hyperparameter Tuning

• Use Grid Search or Random Search
• Optimize parameters like learning rate, depth, or batch size
• Compare and select the best performing model

🌟 Task 3: Deploy a Machine Learning Model

• Save the trained model (e.g., using joblib or pickle)
• Create a Flask or FastAPI backend
• Build a simple UI or API endpoint for inference
• Test deployment locally or on a cloud platform

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🌱 Final Notes

This journey is a blend of consistency, curiosity, and continuous learning. I'm excited to keep growing, exploring, and contributing as a Machine Learning enthusiast. 🚀

“Every new experience shapes a better version of ourselves.”

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🔗 Connect with me

LinkedIn: linkedin.com/in/abdul-rafay19