Sequences, Time Series and Prediction

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Sequences, Time Series and Prediction

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

Instructor: Laurence Moroney

151,613 already enrolled

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4 modules

Gain insight into a topic and learn the fundamentals.

5,164 reviews

Intermediate level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

97%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

5,164 reviews

Intermediate level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

97%

Most learners liked this course

What you'll 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'll gain

Tools you'll learn

Tensorflow

Details to know

Shareable certificate

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Assessments

4 assignments

Taught in English

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

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There are 4 modules in this course

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 Developer 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.

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!

What's included

10 videos7 readings1 assignment1 programming assignment2 ungraded labs

10 videosTotal 32 minutes

Introduction: A conversation with Andrew Ng3 minutes
Time series examples4 minutes
Machine learning applied to time series2 minutes
Common patterns in time series5 minutes
Introduction to time series4 minutes
Train, validation and test sets3 minutes
Metrics for evaluating performance2 minutes
Moving average and differencing3 minutes
Trailing versus centered windows1 minute
Forecasting4 minutes

7 readingsTotal 18 minutes

Welcome to the course!1 minute
About the notebooks in this course5 minutes
Week 1 Wrap up2 minutes
Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas!2 minutes
Lecture Notes Week 11 minute
Assignment Troubleshooting Tips5 minutes
(Optional) Downloading your Notebook and Refreshing your Workspace2 minutes

1 assignmentTotal 30 minutes

Week 1 Quiz30 minutes

1 programming assignmentTotal 180 minutes

Working with generated time series180 minutes

2 ungraded labsTotal 60 minutes

Introduction to time series notebook (Lab 1)30 minutes
Forecasting notebook (Lab 2)30 minutes

Having explored time series and some of the common attributes of time series such as trend and seasonality, and then having used statistical methods for projection, let's now begin to teach neural networks to recognize and predict on time series!

What's included

10 videos2 readings1 assignment1 programming assignment3 ungraded labs

10 videosTotal 25 minutes

A conversation with Andrew Ng1 minute
Preparing features and labels4 minutes
Preparing features and labels (screencast)3 minutes
Feeding windowed dataset into neural network2 minutes
Single layer neural network3 minutes
Machine learning on time windows1 minute
Prediction2 minutes
More on single layer neural network2 minutes
Deep neural network training, tuning and prediction4 minutes
Deep neural network3 minutes

2 readingsTotal 2 minutes

Week 2 Wrap up1 minute
Lecture Notes Week 21 minute

1 assignmentTotal 30 minutes

Week 2 Quiz30 minutes

1 programming assignmentTotal 180 minutes

Forecasting Using Neural Networks180 minutes

3 ungraded labsTotal 90 minutes

Preparing features and labels notebook (Lab 1)30 minutes
Single layer neural network notebook (Lab 2)30 minutes
Deep neural network notebook (Lab 3)30 minutes

Recurrent Neural networks and Long Short Term Memory networks are really useful to classify and predict on sequential data. This week we'll explore using them with time series...

What's included

8 videos4 readings1 assignment1 programming assignment2 ungraded labs

8 videosTotal 16 minutes

Week 3 - A conversation with Andrew Ng3 minutes
Conceptual overview3 minutes
Shape of the inputs to the RNN2 minutes
Outputting a sequence1 minute
Lambda layers1 minute
Adjusting the learning rate dynamically2 minutes
LSTM2 minutes
Coding LSTMs2 minutes

4 readingsTotal 22 minutes

More info on Huber loss10 minutes
Link to the LSTM lesson10 minutes
Week 3 Wrap up1 minute
Lecture Notes Week 31 minute

1 assignmentTotal 30 minutes

Week 3 Quiz30 minutes

1 programming assignmentTotal 180 minutes

Forecast using RNNs or LSTMs180 minutes

2 ungraded labsTotal 60 minutes

RNN notebook (Lab 1)30 minutes
LSTM notebook (Lab 2) 30 minutes

On top of DNNs and RNNs, let's also add convolutions, and then put it all together using a real-world data series -- one which measures sunspot activity over hundreds of years, and see if we can predict using it.

What's included

11 videos9 readings1 assignment1 programming assignment2 ungraded labs

11 videosTotal 22 minutes

Week 4 - A conversation with Andrew Ng1 minute
Convolutions1 minute
Bi-directional LSTMs3 minutes
Convolutions with LSTM1 minute
Real data - sunspots3 minutes
Train and tune the model3 minutes
Prediction2 minutes
Sunspots1 minute
Combining our tools for analysis4 minutes
Congratulations!1 minute
Specialization wrap up - A conversation with Andrew Ng2 minutes

9 readingsTotal 42 minutes

Convolutional neural networks course10 minutes
More on batch sizing10 minutes
Lecture Notes Week 41 minute
[IMPORTANT] Reminder about end of access to Lab Notebooks2 minutes
Wrap up1 minute
References10 minutes
Acknowledgments1 minute
What next?5 minutes
(Optional) Opportunity to Mentor Other Learners2 minutes

1 assignmentTotal 30 minutes

Week 4 Quiz30 minutes

1 programming assignmentTotal 180 minutes

Adding CNNs to improve forecasts180 minutes

2 ungraded labsTotal 75 minutes

Convolutions with LSTM notebook (Lab 1)30 minutes
Sunspots notebooks (Lab 2 & Lab 3)45 minutes

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Instructor

Instructor ratings

(787 ratings)

Laurence Moroney

DeepLearning.AI

22 Courses606,515 learners

Offered by

DeepLearning.AI

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Reviewed on Jun 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

Reviewed on Mar 26, 2020

Few hands on programming assignments could be better for experience as was the case with starting two courses. Overall good course and the structure was well laid. Thanks for building it up

Reviewed on May 20, 2020

Its very interesting, thank you Lawrence and Andrew. You both have brought me to a wonderful world. I hope I can continue to more explore, more learning, and more practice to this new world. :D

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