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Overview

This course is delivered in six modules. The modules cover designing and building a TensorFlow 2.x input data pipeline, building ML models with TensorFlow 2.x and Keras, improving the accuracy of ML models, writing ML models for scaled use and writing specialized ML models.
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What You'll Learn

  • Create TensorFlow 2.x and Keras machine learning models
  • Describe Tensorflow 2.x key components
  • Use the tf.data library to manipulate data and large datasets
  • Use the Keras Sequential and Functional APIs for simple and advanced model creation
  • Train, deploy, and productionalize ML models at scale with Cloud AI Platform

Who Should Attend

This class is primarily intended for the following participants:

  • 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
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Prerequisites

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

  • Some familiarity with basic machine learning concepts
  • Basic proficiency with a scripting language - Python preferred
  • It is strongly recommended that participants new to machine learning take the Launching into Machine Learning Coursera course.

Learning Journey

Coming Soon...

Module 1: Introduction to Course

  • Summarize course scope
  • Get Started with Google Cloud and Qwiklabs

Module 2: Introduction to TensorFlow

  • Describe the TensorFlow 2.x API hierarchy
  • List the key components of TensorFlow 2.x
  • Create Tensors and Variables
  • Write low-level TensorFlow programs

Module 3: Design and Build a TensorFlow 2.x Input Data Pipeline

  • Load csv data using Pandas
  • Load numpy arrays with tf.data.Dataset
  • Load data using tf.Data.Dataset
  • Create multiple types of feature columns
  • Create an input pipeline using tf.data

Module 4: Training neural networks with Tensorflow 2.x and the Keras Sequential API

  • Describe how activation functions, loss and optimizers work
  • Build a DNN model using the Keras Sequential API
  • Use feature columns in a Keras model
  • Train a model with Keras
  • Describe how to save/load, and deploy a Keras model on Google Cloud AI Platform
  • Describe how to deploy and make predictions with at Keras model

Module 5: Training neural networks with Tensorflow 2.x and the Keras Functional API

  • Describe embeddings and how to create them with the feature column API
  • Define Deep and Wide models and when to use them
  • Describe the Keras functional API and how to build a deep and wide model
  • Explain how regularization can help improve the performance of a model

Module 6: Course Summary

  • Summarize Course
  • List Next Steps

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Frequently Asked Questions (FAQs)

  • Why get Google Cloud Platform (GCP) certified?

    Google Cloud certifications validate your expertise in cloud technologies and your proficiency in using Google Cloud Platform's vast array of services.

    These certifications are recognized globally and highly sought after by employers, as they demonstrate your ability to design, develop, and manage scalable and secure cloud solutions on GCP.

    Google Cloud-certified professionals are in high demand, opening doors to new career opportunities and higher earning potential.

  • What to expect for the examination?

    Google Cloud offers a variety of certification exams across different levels (Foundational, Associate, and Professional) covering various job roles and specializations.

    The exams typically consist of multiple-choice and multiple-select questions, as well as scenario-based questions that assess your ability to apply your knowledge in real-world situations.

    Note: Certification requirements and policies may be updated by Google Cloud from time to time. We apologize for any discrepancies; do get in touch with us if you have any questions.

  • How long is Google Cloud Platform (GCP) certification valid for?

    Most Google Cloud certifications, including Professional-level certifications, are valid for two years from the date of passing the exam.

    The Cloud Digital Leader and Associate Cloud Engineer certifications are valid for three years from the date of passing the exam.

    To maintain your certification, you will need to recertify by passing the latest version of the same exam or an equivalent higher-level certification exam before your current certification expires.

    You will receive a notification from Google Cloud prior to your certification's expiration date.

    Note: Certification requirements and policies may be updated by Google Cloud from time to time. We apologize for any discrepancies; do get in touch with us if you have any questions.

  • Why take this course with Trainocate?

    Here’s what sets us apart:

    - Global Reach, Localized Accessibility: Benefit from our geographically diverse training hubs in 24 countries (and counting!).

    - Top-Rated Instructors: Our team of subject matter experts (with high average CSAT and MTM scores) are passionate to help you accelerate your digital transformation.

    - Customized Training Solutions: Choose from on-site, virtual classrooms, or self-paced learning to fit your organization and individual needs.

    - Experiential Learning: Dive into interactive training with our curated lesson plans. Participate in hands-on labs, solve real-world challenges, and take on comprehensive assessments.

    - Learn From The Best: With 30+ authorized training partnerships and countless awards from Microsoft, AWS, Google – you're guaranteed learning from the industry's elite.

    - Your Bridge To Success: We provide up-to-date course materials, helpful exam guides, and dedicated support to validate your expertise and elevate your career.

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Course Curriculum

Course Curriculum

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Training Schedule

Exam & Certification

Exam & Certification

FAQs

Frequently Asked Questions

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