TensorFlow Developer Professional Certificate

4 Courses

Intermediate

4 months (5 hours/week)

Laurence Moroney

Enroll Now

What you will learn

Best practices for TensorFlow, a popular open-source machine learning framework to train a neural network for a computer vision applications.
Handle real-world image data and explore strategies to prevent overfitting, including augmentation and dropout.
Build natural language processing systems using TensorFlow.
Apply RNNs, GRUs, and LSTMs as you train them using text repositories.

Skills you will gain

Computer Vision
Convolutional Neural Network
Machine Learning
Natural Language Processing
Tensorflow
Inductive Transfer
Augmentation
Dropouts
Tokenization
RNNs
Forecasting
Time Series

TensorFlow is one of the most in-demand and popular open-source deep learning frameworks available today. The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you applied machine learning skills with TensorFlow so you can build and train powerful models.

In this hands-on, four-course Professional Certificate program, you’ll learn the necessary tools to build scalable AI-powered applications with TensorFlow. After finishing this program, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects. This program can help you prepare for the Google TensorFlow Certificate exam and bring you one step closer to achieving the Google TensorFlow Certificate.

4 Courses
4 months (5 hours/week)
Intermediate

Syllabus

This first course introduces you to TensorFlow, a popular machine learning framework. You will learn how to build a basic neural network for computer vision and use convolutions to improve your neural network.

Week 1: A New Programming Paradigm

Introduction: A conversation with Andrew Ng
A primer in machine learning
The “Hello World” of neural networks
Working through “Hello World” in TensorFlow and Python

Week 2: Introduction to Computer Vision

A conversation with Andrew Ng
An introduction to computer vision
Writing code to load training data
Coding a computer vision neural network
Walk through a notebook for computer vision
Using callbacks to control training
Walk through a notebook with callbacks

Week 3: Enhancing Vision with Convolutional Neural Networks

A conversation with Andrew Ng
What are convolutions and pooling?
Implementing convolutional layers
Implementing pooling layers
Improving the fashion classifier with convolutions
Walking through convolutions

Week 4: Using Real-World Images

A conversation with Andrew Ng
Understanding ImageGenerator
Defining a ConvNet to use complex images
Training the ConvNet with fit_generator
Walking through developing a ConvNet
Walking through training the ConvNet with fit_generator
Adding automatic validation to test accuracy
Exploring the impact of compressing images
Outro: Conversation with Andrew

Instructors

Laurence Moroney

Instructor

Lead AI Advocate, Google

Sign Up

Be notified of new courses

TensorFlow Developer Professional Certificate

What you will learn

Skills you will gain

Syllabus

Week 1: A New Programming Paradigm

Week 2: Introduction to Computer Vision

Week 3: Enhancing Vision with Convolutional Neural Networks

Week 4: Using Real-World Images

Week 1: Exploring a Larger Dataset

Week 2: Augmentation, a Technique to Avoid Overfitting

Week 3: Transfer Learning

Week 4: Multi-class Classifications

Week 1: Sentiment in Text

Week 2: Word Embeddings

Week 3: Sequence Models

Week 4: Sequence Models and Literature

Week 1: Sequences and Prediction

Week 2: Deep Neural Networks for Time Series

Week 3: Recurrent Neural Networks for Time Series

Week 4: Real-world Time Series Data

Course 1: Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Course 2: Convolutional Neural Networks in TensorFlow

Course 3: Natural Language Processing in TensorFlow

Course 4: Sequences, Time Series, and Prediction

Instructors

Laurence Moroney

Sign Up

Frequently Asked Questions