Intro to Machine Learning with TensorFlow

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This is now a virtual classroom course. You can find more information about our virtual classroom here

This is a 16-hour course on Tensorflow. We will explore Tensorflow architecture and application programming interfaces to build machine learning algorithms and train then on a real world dataset.

A discount is available when:
  • a single customer proceeds with more than three registrations for a particular session;
  • a learner is not currently employed.
Please contact us to receive your discount coupon if you meet any of the above-mentioned criteria.

Please also note that the upcoming sessions are guaranteed to run once they have a minimum number of learners registered.
Total price:
540.00660.00 final price

Available Sessions

DateTimezoneTraining TypeWeekend-WeekdayDescriptionPrice 
Coming soon540.00660.00 final price

This is a 16-hour course on Tensorflow. We will explore Tensorflow architecture and application programming interfaces to build machine learning algorithms and train then on a real world dataset.

Who should attend

IT Professionals with some experience in programming , data analysis and statistics interested in expanding and building skills on a state of the art framework used to build any kind of machine learning algorithms and high performance computations over big amounts of data

Prerequisites

  • Python programming fluency
  • Fundamentals of Statistics and Machine learning

What will you learn

During the course you will cover the following topics:

  • Introduction to Tensorflow
    • Present a small model end to end using Jupyter Notebook
    • Present a small model end to end using a python development environment
    • Introduction to Tensors and Operations 
    • Data Loading and Saving Models and results using Tensorflow
    • Introduction to the Computational Graph
    • Visualize the Computational Graph
    • Debugging Tensorflow Graphs
    • Monitoring Training metrics 
  • Developing Models from Using Tensorflow
    • Build a Neural Network 
    • Build a Regression model
    • Build a Tree model
    • Model Evaluation
    • Deploying models for production use

All course material will be taught using a ready made development environment using Jupyter notebooks and Python Development Environment fully integrated with Tensorflow libraries and execution environment. Real world size and complexity datasets will be provided as well.

 

Timezone

Training Type

Weekend-Weekday

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