What will you learn in Machine Learning with Scikit-learn, PyTorch & Hugging Face Professional Certificate course
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Build machine learning models using scikit-learn for classical ML tasks.
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Develop deep learning models using PyTorch.
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Work with transformer models and NLP pipelines using Hugging Face.
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Understand supervised and unsupervised learning workflows.
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Train, evaluate, and deploy ML models for real-world applications.
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Apply modern ML engineering practices in Python-based environments.
Program Overview
Foundations of Machine Learning with scikit-learn
⏳ 3–4 weeks
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Learn regression, classification, and clustering.
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Use scikit-learn for model building and evaluation.
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Understand feature engineering and preprocessing.
Deep Learning with PyTorch
⏳ 4–5 weeks
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Build neural networks from scratch.
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Train models using tensors and autograd.
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Implement CNNs and other deep learning architectures.
Natural Language Processing with Hugging Face
⏳ 4–5 weeks
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Work with pretrained transformer models.
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Fine-tune models for NLP tasks such as text classification and sentiment analysis.
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Understand tokenization and embeddings.
Model Deployment and ML Workflows
⏳ 3–4 weeks
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Evaluate and optimize model performance.
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Prepare models for production environments.
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Understand ethical considerations and responsible AI practices.
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Job Outlook
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Highly relevant for Data Scientists, ML Engineers, and AI Developers.
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Skills in scikit-learn, PyTorch, and Hugging Face are widely востребованы in AI roles.
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Valuable across industries including tech, finance, healthcare, and research.
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Strong foundation for advanced AI, NLP, and deep learning careers.
