Python Programming
- Introduction to Python syntax, data types, and control structures.
- Functions, modules, and packages in Python.
- File I/O operations and exception handling in Python.
Machine Learning Fundamentals with Python
- Introduction to NumPy for numerical computing and handling arrays.
- Data manipulation and preprocessing with pandas for structured data.
- Implementing machine learning algorithms with scikit-learn.
- Model evaluation and validation using scikit-learn’s built-in functions.
Deep Learning with PyTorch
- Introduction to PyTorch for deep learning.
- Building neural networks with PyTorch’s torch.nn module.
- Training deep learning models for image classification and regression tasks.
- Implementing custom loss functions and optimizing models with PyTorch’s autograd functionality.
Natural Language Processing with Hugging Face Transformers
- Introduction to the Hugging Face Transformers library for NLP tasks.
- Using pre-trained transformer models for tasks like text classification, named entity recognition, and sentiment analysis.
- Fine-tuning transformer models on domain-specific datasets for custom NLP tasks
Applied Machine Learning
- Feature engineering and preprocessing techniques using pandas and NumPy.
- Hyperparameter tuning and model optimization with scikit-learn.
- Hands-on projects solving real-world problems with machine learning using scikit-learn and PyTorch
Model Deployment and Scalability with Python
- Deployment of scikit-learn, PyTorch, and Hugging Face Transformers models using Flask or FastAPI.
- Containerization with Docker and deployment in Kubernetes.
- Scaling machine learning systems with libraries like Celery for asynchronous task queues
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