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.

Master the art of LLM finetuning with LoRA, QLoRA, and Hugging Face. Learn how to prepare, train and optimize models for specific tasks efficiently.

Learn how to detect fraud using Python.

Learn about MLOps architecture, CI/CD/CM/CT techniques, and automation patterns to deploy ML systems that can deliver value over time.

Extend your regression toolbox with the logistic and Poisson models and learn to train, understand, and validate them, as well as to make predictions.

MATLAB is a widely used programming language for statistical computing. This course will teach you the fundamentals of getting started with machine learning and data science within MATLAB.