Machine Learning with TensorFlow on Google Cloud Platform


5 Days


Delivery Methods
Virtual Instructor Led
Private Group

Course Overview

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?

Course Objectives

  • 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
  • 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.
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    Course Prerequisites

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

    1 - How Google Does Machine Learning

    • Develop a data strategy around machine learning
    • Examine use cases that are then reimagined through an ML lens
    • Recognize biases that ML can amplify
    • Leverage Google Cloud Platform tools and environment to do ML
    • Learn from Google’s experience to avoid common pitfalls
    • Carry out data science tasks in online collaborative notebooks
    • Invoke pre-trained ML models from Cloud Datalab

    2 - Launching into Machine Learning

    • Identify why deep learning is currently popular
    • Optimize and evaluate models using loss functions and performance metrics
    • Mitigate common problems that arise in machine learning
    • Create repeatable and scalable training, evaluation, and test datasets

    3 - Intro to TensorFlow

    • Create machine learning models in TensorFlow
    • Use the TensorFlow libraries to solve numerical problems
    • Troubleshoot and debug common TensorFlow code pitfalls
    • Use tf_estimator to create, train, and evaluate an ML model
    • Train, deploy, and productionalize ML models at scale with Cloud ML Engine

    4 - Feature Engineering

    • Turn raw data into feature vectors
    • Preprocess and create new feature pipelines with Cloud Dataflow
    • Create and implement feature crosses and assess their impact
    • Write TensorFlow Transform code for feature engineering

    5 - The Art and Science of ML

    • Optimize model performance with hyperparameter tuning
    • Experiment with neural networks and fine-tune performance
    • Enhance ML model features with embedding layers
    • Create reusable custom model code with the Custom Estimator

    Upcoming Class Dates and Times

    Jul 24, 25, 26, 27, 28
    8:00 AM - 4:00 PM
    ENROLL $2,995.00

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