Author: Abdul Rafay
Degree: BS Software Engineering
I am currently doing a Machine Learning Virtual Internship at Intern Intelligence, focusing on advanced machine learning techniques, deep learning, model optimization, and real-world applications using tools like Scikit-Learn, TensorFlow, and PyTorch.
Objective: Optimize the hyperparameters of a machine learning model to enhance performance.
- Model Selection: Random Forest, XGBoost, etc.
- Techniques Used: Grid Search, Random Search, Optuna
- Evaluation Metrics: Accuracy, Precision, Recall, F1 Score
- Tools: Scikit-Learn, Optuna, Google Colab
Objective: Build and train a deep learning model for complex tasks like image classification or NLP.
- Data Preparation: Images / Text preprocessing
- Model Architecture: CNNs, RNNs, LSTMs
- Frameworks: TensorFlow, Keras, PyTorch
- Evaluation Metrics: Accuracy, Precision, Recall, Loss
- Deployment: Integration into applications
- Python, Scikit-Learn, Optuna, XGBoost, RandomForestClassifier
- TensorFlow, Keras, PyTorch, NumPy, Pandas, Matplotlib, Seaborn
- Google Colab, Jupyter Notebook, Hugging Face, TensorFlow Hub, PyTorch Hub
- Streamlit, Flask (for deployment)
“Every step forward is a step closer to excellence—learning never stops.”