06
The Nuts and Bolts of Machine Learning
06
The Nuts and Bolts of Machine Learning
Learn how machine learning uses algorithms and statistics to find patterns in data, helping professionals solve complex problems and make accurate predictions. Learn about supervised and unsupervised machine learning, and apply models like Naive Bayes, decision tree, and random forest. This is the sixth course in the Google Advanced Data Analytics Certificate, a series designed to prepare you for an advanced data analytics role.
Informações sobre o curso
Objetivos
By the end of this course, you will:
- Apply feature engineering techniques using Python
- Construct a Naive Bayes model
- Describe how unsupervised learning differs from supervised learning
- Code a K-means algorithm in Python
- Evaluate and optimize the results of K-means model
- Explore decision tree models, how they work, and their advantages over other types of supervised machine learning
- Characterize bagging in machine learning, specifically for random forest models
- Distinguish boosting in machine learning, specifically for XGBoost models
- Explain tuning model parameters and how they affect performance and evaluation metrics
Pré-requisitos
Prior knowledge of foundational analytical principles, skills, and tools.
Público-alvo
Advanced
Idiomas disponíveis
English