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It includes hands-on tasks across three levels: **Basic**, **Intermediate**, and **Expert**, designed to deepen my understanding and practical knowledge of AI and ML.\n\n---\n\n## 📘 Basic Tasks\n\nThese tasks are designed to build foundational understanding of ML concepts and tools.\n\n### ✅ Task 1: Implement a Simple Linear Regression  \n- Load a dataset (e.g., house prices or student scores)  \n- Apply simple linear regression  \n- Visualize the regression line  \n- Evaluate with metrics like MSE or R²  \n\n### ✅ Task 2: Classify Data with a Decision Tree  \n- Use a labeled dataset (e.g., Iris or Titanic)  \n- Train a decision tree classifier  \n- Visualize the decision tree  \n- Interpret decision boundaries  \n\n### ✅ Task 3: Visualize Data with a Scatter Plot  \n- Choose two variables from a dataset  \n- Plot them using matplotlib or seaborn  \n- Add colors or labels for categories if applicable  \n- Use the visualization to observe correlations or clusters  \n\n---\n\n## 📗 Intermediate Tasks\n\nThese tasks help in understanding the intricacies of data processing and model evaluation.\n\n### 🚀 Task 1: Build a Model with Cross-Validation  \n- Implement k-fold cross-validation  \n- Evaluate model consistency across folds  \n- Use sklearn's `cross_val_score`  \n\n### 🚀 Task 2: Preprocess Data for Machine Learning  \n- Handle missing values  \n- Normalize or scale features  \n- Encode categorical variables  \n- Split into training and testing sets  \n\n### 🚀 Task 3: Create a Classification Report  \n- Train a classification model  \n- Predict test labels  \n- Generate a report with precision, recall, f1-score using `classification_report`  \n\n---\n\n## 📙 Expert Tasks\n\nThese tasks push deeper into complex modeling, optimization, and deployment.\n\n### 🌟 Task 1: Develop a Neural Network for Classification  \n- Use frameworks like TensorFlow or PyTorch  \n- Build a feedforward neural network  \n- Train and validate on a dataset (e.g., MNIST or CIFAR-10)  \n- Track accuracy and loss  \n\n### 🌟 Task 2: Implement Hyperparameter Tuning  \n- Use Grid Search or Random Search  \n- Optimize parameters like learning rate, depth, or batch size  \n- Compare and select the best performing model  \n\n### 🌟 Task 3: Deploy a Machine Learning Model  \n- Save the trained model (e.g., using joblib or pickle)  \n- Create a Flask or FastAPI backend  \n- Build a simple UI or API endpoint for inference  \n- Test deployment locally or on a cloud platform  \n\n---\n\n## 🌱 Final Notes\n\nThis journey is a blend of **consistency**, **curiosity**, and **continuous learning**. I'm excited to keep growing, exploring, and contributing as a Machine Learning enthusiast. 🚀\n\n\u003e **“Every new experience shapes a better version of ourselves.”**\n\n---\n\n### 🔗 Connect with me  \n**LinkedIn:** [linkedin.com/in/abdul-rafay19](https://www.linkedin.com/in/abdul-rafay19)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabdul-rafay19%2Fyoungdevinterns_machine-learning_tasks","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fabdul-rafay19%2Fyoungdevinterns_machine-learning_tasks","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabdul-rafay19%2Fyoungdevinterns_machine-learning_tasks/lists"}