top of page

Machine Learning on Google Cloud (MLGC)



What is machine learning, and what kinds of problems can it solve? Why are neural networks so popular right now? How can you improve data quality and perform exploratory data analysis? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent? In this course, you'll learn how to write distributed machine learning models that scale in Tensorflow 2.x, perform feature engineering in BQML and Keras, evaluate loss curves and perform hyperparameter tuning, and train models at scale with Cloud AI Platform.

Who should attend

Aspiring machine learning data scientists and engineers.
Machine learning scientists, data scientists, and data analysts who want exposure to machine learning in the cloud using TensorFlow 2.x and Keras.
Data engineers.


Some familiarity with basic machine learning concepts.
Basic proficiency with a scripting language - Python preferred.

Course Objectives

Frame a business use case as a machine learning problem.
Describe how to improve data quality.
Perform exploratory data analysis.
Build and train supervised learning models.
Optimize and evaluate models using loss functions and performance metrics.
Create repeatable and scalable training, evaluation, and test datasets.
Implement machine learning models using Keras and TensorFlow 2.x.
Understand the impact of gradient descent parameters on accuracy, training speed, sparsity, and generalization.
Represent and transform features.
Train models at scale with AI Platform.

bottom of page