top of page

Advanced Machine Learning with TensorFlow on Google Cloud Platform (MLTF)



What is machine learning, and what kinds of problems can it solve? What are the five phases of converting a candidate use case to be driven by machine learning, and why is it important that the phases not be skipped? Why are neural networks so popular now? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent and a thoughtful way of creating datasets?

Who should attend

Data Engineers and programmers interested in learning how to apply machine learning in practice.
Anyone interested in learning how to build and operationalize TensorFlow models.


To get the most out of this course, participants should have:

Experience coding in Python
Knowledge of basic statistics
Knowledge of SQL and cloud computing (helpful)

Course Objectives

Learn how to write distributed machine learning models that scale in Tensorflow, scale out the training of those models, and offer high-performance predictions. Convert raw data to features in a way that allows ML to learn important characteristics from the data and bring human insight to bear on the problem. Finally, learn how to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems. You will experiment with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using Google Cloud Platform

This course teaches participants the following skills:

Frame a business use case as a machine learning problem
Create machine learning datasets that are capable of achieving generalization
Implement machine learning models using TensorFlow
Understand the impact of gradient descent parameters on accuracy, training speed, sparsity, and generalization
Build and operationalize distributed TensorFlow models
Represent and transform features

bottom of page