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.
Sequences, Time Series and Prediction
This course is part of DeepLearning.AI TensorFlow Developer Professional Certificate
Instructor: Laurence Moroney
Sponsored by Coursera for Reliance Family
143,250 already enrolled
(5,086 reviews)
Recommended experience
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
Details to know
Add to your LinkedIn profile
4 assignments
See how employees at top companies are mastering in-demand skills
Build your Machine Learning expertise
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate from DeepLearning.AI
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
There are 4 modules in this course
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
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
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
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
Instructor
Offered by
Why people choose Coursera for their career
Learner reviews
5,086 reviews
- 5 stars
77.83%
- 4 stars
15.87%
- 3 stars
3.94%
- 2 stars
1.13%
- 1 star
1.19%
Showing 3 of 5086
Reviewed on Mar 21, 2020
Really like the focus on practical application and demonstrating the latest capability of TensorFlow. As mentioned in the course, it is a great compliment to Andrew Ng's Deep Learning Specialization.
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
Reviewed on Jun 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.
Recommended if you're interested in Data Science
University of Colorado Boulder
Coursera Project Network
Open new doors with Coursera Plus
Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Advance your career with an online degree
Earn a degree from world-class universities - 100% online
Join over 3,400 global companies that choose Coursera for Business
Upskill your employees to excel in the digital economy