The Intermediate Google Cloud for Data Scientists training course is designed to further the skills of participants who are already familiar with the data science capabilities of Google Cloud and wish to deepen their understanding of applied machine learning, relevant mathematical foundations, and practical approaches for creating and launching TensorFlow-based systems, for example for recommendation engines.
This course starts with a deep dive review of the key mathematical concepts used by the hands-on data science and machine learning exercises in this course. Next, students will work with sample Jupyter notebooks in Google Colab to implement low-level TensorFlow code for commonly used tensor operations such as stacking, slicing, reshaping, and squeezing. After reviewing how to deploy, debug, and serve TensorFlow code at scale, students will learn about implementing a practical data science use case around recommendation systems. This course will conclude with case studies and best practices for how to build a mature data science and machine learning practice for your project or at your organization.
Before attending this course, students should take the Google Cloud for Data Scientists course or be familiar with all of the topics listed here: Google Cloud for Data Scientists
Purpose
|
Learn how to implement statistical and machine learning models using TensorFlow, for example for recommendation engines, and how to improve their performance based on the students' understanding of underlying mathematical principles. |
Audience
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Developers needing to leverage Jupyter notebooks and Tensorflow on the GCP in a Data Science context. |
Role
| Data Engineer - Data Scientist - Software Developer - Technical Manager |
Skill Level
| Intermediate |
Style
| Fast Track - Targeted Topic - Workshops |
Duration
| 2 Days |
Related Technologies
| Google Cloud | Tensorflow |
Productivity Objectives
- Develop TensorFlow code for data science and machine learning models.
- Optimize machine learning model training accuracy and performance.
- Use batch and real-time streaming data processing pipelines.
- Design recommendation engines and systems.
