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Predict credit defaults with random forest using Python | View in the Skills Network Catalog

Build a predictive model using Python, pandas, and scikit-learn's random forest algorithm for financial risk management. This hands-on project covers data preprocessing, model fitting, and performance evaluation. Learn hyperparameter tuning to enhance model robustness. Perfect for data science enthusiasts and financial analysts, this 30-minute project transforms your data into actionable insights for predicting credit defaults, showcasing the real-world power of machine learning in banking.

Fine-tune a transformer-based neural network with PyTorch | View in the Skills Network Catalog

Master the art of fine-tuning a transformer-based neural network using PyTorch. Discover the power of transfer learning as you meticulously fine-tune the entire neural network, comparing it to the more focused approach of fine-tuning just the final layer. Unlock this essential skill by immersing yourself in this end-to-end hands-on project today!

Efficient fine-tuning of neural nets using LoRA and PyTorch | View in the Skills Network Catalog

Fine-tune neural networks using Low-Rank Adaptation (LoRA) in Python and PyTorch. Start by pretraining a model on the AG News data set, which allows it to develop extensive news categorization skills. Then apply LoRA to further refine this model on the IMDB data set, with a focus on sentiment analysis. Discover how LoRA delivers outstanding results while training a smaller number of parameters compared to traditional fine-tuning approaches.