This course is set up as a workshop where you will do End-to-End Machine Learning with TensorFlow on Google Cloud Platform. It involves building an end-to-end model from data exploration all the way to deploying an ML model and getting predictions from it.

Explore bagging, boosting, stacking, and more in this introduction to ensemble methods in machine learning.

Learn how to build advanced and effective machine learning models in Python using ensemble techniques such as bagging, boosting, and stacking.

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.

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.

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