Machine Learning with TensorFlow on Google Cloud

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.

More Information
Course feature Lifetime Access, 24x7 Support, Real-time code analysis and feedback, 100% Money Back Guarantee, Certified Trainer
  • Lifetime Access

  • 24x7 Support

  • Real-time code analysis and feedback

  • 100% Money Back Guarantee

  • Certified Trainer

 Learn how to write distributed machine learning models that scale in Tensorflow 2.x, 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

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?

Topics covered

Data
Machine Learning

Think strategically and analytically about ML as a business process and consider the fairness implications with respect to ML

• How ML optimization works and how various hyperparameters affect models during optimization

• How to write models in TensorFlow using both pre-made estimators as well as custom ones and train them locally or in Cloud AI Platform

•Why feature engineering is critical to success and how you can use various technologies including Cloud Dataflow and Cloud Dataprep

•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 engineer

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

Course Outline

Day 1

Module 0 - Welcome to Machine Learning on

Module 1 - Getting Started with Machine

Module 2 - Launching into ML 130

Module 3 - Optimization 90

Module 4 - Generalization and Sampling 35

 

Day 2

Module 5 - Core TensorFlow 230

Module 6 - Estimator API 210

 

DAY 3

Module 7 - Wide and Deep Models 160

Module 8 - Training on Large Datasets 120

Module 9 - Scaling TensorFlow with Cloud AI

 

DAY 4

Module 10 - Raw Data and Features 80

Module 11 - Feature engineering 100

Module 12 - Regularization 120

Module 13 - Hyperparameter tuning 130

 

DAY 5

Module 14 - Custom Estimators 200

Module 15 - Preprocessing 220