This course is part of the DeepLearning.AI TensorFlow Developer Professional Certificate

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Laurence Moroney

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DeepLearning.AI TensorFlow Developer Professional CertificateDeepLearning.AI

About this Course

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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 Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
In this fourth course, you will learn how to build time series models in TensorFlow. You’ll first implement best practices to prepare time series data. You’ll also explore how RNNs and 1D ConvNets can be used for prediction. Finally, you’ll apply everything you’ve learned throughout the Specialization to build a sunspot prediction model using real-world data!

The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

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Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 4 of 4 in the

DeepLearning.AI TensorFlow Developer Professional Certificate

Intermediate Level

You should take the first 3 courses of the TensorFlow Specialization and be comfortable coding in Python and understanding high school-level math.

Approx. 22 hours to complete

English

What you will learn

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

Forecasting
Machine Learning
Tensorflow
Time Series
prediction

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 4 of 4 in the

DeepLearning.AI TensorFlow Developer Professional Certificate

Intermediate Level

You should take the first 3 courses of the TensorFlow Specialization and be comfortable coding in Python and understanding high school-level math.

Approx. 22 hours to complete

English

Instructor

Instructor rating

4.73/5 (687 Ratings)

Laurence Moroney

Instructor

Lead AI Advocate, Google

417,453 Learners

15 Courses

Offered by

DeepLearning.AI

Syllabus - What you will learn from this course

Content Rating97%(10,608 ratings)

Week1

Week 1

6 hours to complete

Sequences and Prediction

Hi Learners and welcome to this course on sequences and prediction! In this course we'll take a look at some of the unique considerations involved when handling sequential time series data -- where values change over time, like the temperature on a particular day, or the number of visitors to your web site. We'll discuss various methodologies for predicting future values in these time series, building on what you've learned in previous courses!

6 hours to complete

10 videos (Total 32 min), 6 readings, 4 quizzes

10 videos

Introduction, A conversation with Andrew Ng3mTime series examples4mMachine learning applied to time series1mCommon patterns in time series5mIntroduction to time series4mTrain, validation and test sets3mMetrics for evaluating performance2mMoving average and differencing2mTrailing versus centered windows50sForecasting3m

6 readings

Where to find the notebooks for this course5mIntroduction to time series notebook (Lab 1)30mForecasting notebook (Lab 2)30mWeek 1 Wrap up2mAssignment Troubleshooting Tips5m(Optional) Downloading your Notebook and Refreshing your Workspace2m

1 practice exercise

Week 1 Quiz30m

Week2

Week 2

6 hours to complete

Deep Neural Networks for Time Series

6 hours to complete

10 videos (Total 25 min), 4 readings, 3 quizzes

10 videos

A conversation with Andrew Ng1mPreparing features and labels4mPreparing features and labels (screencast)2mFeeding windowed dataset into neural network2mSingle layer neural network2mMachine learning on time windows37sPrediction2mMore on single layer neural network1mDeep neural network training, tuning and prediction4mDeep neural network2m

4 readings

Preparing features and labels notebook (Lab 1)30mSingle layer neural network notebook (Lab 2)30mDeep neural network notebook (Lab 3)30mWeek 2 Wrap up1m

1 practice exercise

Week 2 Quiz30m

Week3

Week 3

5 hours to complete

Recurrent Neural Networks for Time Series

5 hours to complete

8 videos (Total 17 min), 5 readings, 3 quizzes

Week4

Week 4

7 hours to complete

Real-world time series data

7 hours to complete

11 videos (Total 23 min), 9 readings, 3 quizzes

11 videos

Week 4 - A conversation with Andrew Ng1mConvolutions58sBi-directional LSTMs3mConvolutions with LSTM1mReal data - sunspots3mTrain and tune the model2mPrediction1mSunspots1mCombining our tools for analysis3mCongratulations!38sSpecialization wrap up - A conversation with Andrew Ng2m

9 readings

Convolutional neural networks course10mMore on batch sizing10mConvolutions with LSTM notebook (Lab 1)45mSunspots notebooks (Lab 2 & Lab 3)1hWrap up1mReferences10mAcknowledgments2mWhat next?5m(Optional) Opportunity to Mentor Other Learners10m

1 practice exercise

Week 4 Quiz30m

Reviews

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TOP REVIEWS FROM SEQUENCES, TIME SERIES AND PREDICTION

by WEJul 16, 2020

The course is fantastic. It was a bit short and with some hyperparameters tuning focus, it could have been great. Also, it seems that it is biased to show that LSTM is always superior to RNN networks.

by VHJun 18, 2020

Coming from a background of knowing Deep Learning and theory of Time Series, this course was extremely helpful in understanding the practical aspects. I would recommend you take a course as well

by SWSep 15, 2020

Great course for review and hands-on practice of RNN/LSTM/DNN after AI specialization (Sequence Models). However, without background of DL (sequence models) it's a bit hard to catch up.

by AKJun 4, 2020

Laurence Moroney is the best. Before taking up the course, i didnt know anything about the AI or ML or Tensorflow. The concepts were explained in such a manner that anyone can learn Tensorflow.

View all reviews

About the DeepLearning.AI TensorFlow Developer Professional Certificate

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. Ready to deploy your models to the world? Learn how to go live with your models with the TensorFlow: Data and Deployment Specialization. Looking to customize and build powerful real-world models for complex scenarios? Check out the TensorFlow: Advanced Techniques Specialization.

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Sequences, Time Series and Prediction

About this Course

What you will learn

Skills you will gain