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Machine Learning with TensorFlow on Google Cloud Platform

////Machine Learning with TensorFlow on Google Cloud Platform

Machine Learning with TensorFlow on Google Cloud Platform

Course ID: GCP-MLTFGCP 1 Week
   

Machine Learning with TensorFlow on Google Cloud Platform

Overview

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.

Description

AUDIENCE

  • 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.

CERTIFICATION

EXAM

INVESTMENT

Instructor-led / Virtual Instructor-led

Singapore: Upon Request

PREREQUISITES

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

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

OBJECTIVES

This series of courses 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

COURSE CONTENT

Module 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

Module 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

Module 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

Module 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

Module 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

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