Machine Learning with Scikit-learn, PyTorch & Hugging Face Professional Certificate course Syllabus

A comprehensive professional certificate program designed to equip learners with practical skills in both classical machine learning and modern deep learning techniques. This course spans approximately 15–22 weeks of hands-on learning, blending foundational concepts with real-world applications using industry-standard tools including scikit-learn, PyTorch, and Hugging Face. Each module emphasizes practical implementation, model evaluation, and deployment workflows, culminating in a final project that integrates all learned skills. Ideal for aspiring data scientists and ML engineers seeking job-ready expertise.

Module 1: Foundations of Machine Learning with scikit-learn

Estimated time: 12 hours

• Introduction to supervised and unsupervised learning workflows
• Regression and classification with scikit-learn
• Clustering techniques and model evaluation
• Feature engineering and data preprocessing

Module 2: Deep Learning with PyTorch

Estimated time: 16 hours

• Building neural networks from scratch
• Working with tensors and autograd in PyTorch
• Training deep learning models using GPU acceleration
• Implementing CNNs for image classification tasks

Module 3: Natural Language Processing with Hugging Face

Estimated time: 16 hours

• Working with pretrained transformer models
• Fine-tuning models for text classification and sentiment analysis
• Understanding tokenization, embeddings, and attention mechanisms

Module 4: Model Evaluation and Optimization

Estimated time: 12 hours

• Evaluating model performance across ML tasks
• Hyperparameter tuning and cross-validation
• Optimizing models for accuracy and efficiency

Module 5: Model Deployment and ML Workflows

Estimated time: 12 hours

• Preparing models for production environments
• Applying modern ML engineering practices in Python
• Understanding ethical considerations and responsible AI practices

Module 6: Final Project

Estimated time: 20 hours

• Build an end-to-end machine learning application using scikit-learn, PyTorch, or Hugging Face
• Evaluate and fine-tune models for a real-world task
• Deploy the model and document the workflow with best practices

Prerequisites

• Familiarity with Python programming
• Basic understanding of statistics and data analysis
• Access to a computer with sufficient computational resources for deep learning tasks

What You'll Be Able to Do After

• Build and evaluate classical machine learning models using scikit-learn
• Develop deep learning models with PyTorch for various applications
• Work effectively with transformer models and NLP pipelines using Hugging Face
• Apply industry-aligned ML engineering practices in real-world projects
• Train, evaluate, and deploy machine learning models for production use