DeepLearning.AI TensorFlow Developer

The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you to build and train powerful scalable AI-powered models with tensorflow python.

Improve your network’s performance using convolutions as you train it to identify real-world images. Teach machines to understand, analyze and respond to human speech with natural language processing systems. Process text, represent sentences as vectors and train a model to create original poetry.

This hands-on, deeplearning specialization can also help you prepare for the Google TensorFlow Certificate exam.

TensorFlow

Learn best practices for TensorFlow, a popular open-source machine learning framework to train a neutral network for computer vision applications

Natural Language Processing

Build natural language processing systems using TensorFlow

Real-World Image Data

Handle real-world image data and explore strategies to prevent overfitting, including augmentation and dropout

Real-World Application

Apply RNNs, GRUs, and LSTMs as you train them using text repositories

Professional Certificate Programs enable you to become empowered and successful in every phase of your job!

Dana Baker

Dana Baker, Executive Director of Regional Campuses

"We are committed to developing current and relevant coursework to help transform our next generation of leaders."

Deeplearning.AI TensorFlow Developer

100% Online

Learn on your own schedule

Flexible Schedule

Set and maintain flexible deadlines

Entry Level

No previous experience required

4-Months to Complete

Suggested pace of 10 hours/week; 4 Courses

Deeplearning.AI TensorFlow Developer Professional Certificate Courses

Introduction to TensorFlow for Artificial Intelligence, Machine Learning and Deep Learning

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course will teach you best- practices for using TensorFlow, a popular open-source framework for machine learning. You will start building and applying scalable models to real-world problems.

By the end of this course, you will be able to:

  • Recognize best practices for using TensorFlow.
  • Build a basic neural network in TensorFlow.
  • Train a neural network for a computer vision application.
  • Use convolutions to improve your neural network.

Convolutional Neural Networks in TensorFlow

In this course, you will learn advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer sees information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout.

By the end of this course, you will be able to:

  • Handle real-world image data.
  • Plot loss and accuracy.
  • Explore strategies to prevent overfitting, including augmentation and dropout.
  • Learn transfer learning and how learned features can be extracted from models.

Natural Language Processing in TensorFlow

In this course, you will build natural language processing systems using TensorFlow. You will learn to process text, including tokenizing and representing sentences as vectors, to input to a neural network. You’ll also learn to apply RNNs, GRUs, and LSTMs in TensorFlowand train LSTMs on existing text.

By the end of this course, you will be able to:

  • Build natural language processing systems using TensorFlow.
  • Process text, including tokenization and representing sentences as vectors.
  • Apply RNNs, GRUs, and LSTMs in TensorFlow.
  • Train LSTMs on existing text to create original poetry and more.

Sequences, Time Series and Prediction

In this final course, you will build time series models in TensorFlow. You will implement best practices to prepare time series data and explore how RNNs and 1D ConvNets can be used for prediction. Finally, you’ll apply everything you’ve learned throughout the program to build a sunspot prediction model using real-world data.

By the end of this course, you will be able to:

  • Solve time series and forecasting problems in TensorFlow.
  • Prepare data for time series learning using best practices.
  • Explore how RNNs and ConvNets can be used for predictions.
  • Build a sunspot prediction model using real-world data.

Skills you will gain: