Learn the fundamentals of gradient boosting and build state-of-the-art machine learning models using XGBoost to solve classification and regression problems.

Machine learning is only as good as its training data. Learn how to process data properly before training your models.

This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.

Create new features to improve the performance of your Machine Learning models.

Learn techniques to extract useful information from text and process them into a format suitable for machine learning.

Learn the principles of feature engineering for machine learning models and how to implement them using the R tidymodels framework.

Learn the gritty details that data scientists are spending 70-80% of their time on; data wrangling and feature engineering.

Use AI to find Timothée Chalamet look-alikes with Llama. Learn how facial recognition and AI models can be used to identify similar features and match images with precision.

Discover how to recommend the best bottle of wine using Natural Language Processing (NLP). Learn to analyze customer reviews, wine descriptions, and preferences to build a personalized recommendation engine with Python.

Learn how to fine-tune transformer-based neural networks with PyTorch. Discover best practices and techniques for adapting pre-trained models to your specific tasks and improving performance.

Master the art of fine-tuning large language models (LLMs) with Hugging Face using LoRA and QLoRA. Learn how to improve model performance with lightweight, efficient training techniques.

Master the fine-tuning of BERT for text reconstruction tasks using Hugging Face. Learn to enhance BERT's performance on NLP tasks like text generation and sentence reconstruction with transfer learning techniques.