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Time-Series Forecasting with TensorFlow 2.0

Time-Series Forecasting with TensorFlow 2.0

Time-Series Forecasting with TensorFlow 2.0

Welcome to this course on time-series forecasting with TensorFlow 2.0!

In this tutorial, you will be learning how to build powerful time-series forecasting model of your own using various kinds of deep learning algorithms such as Dense Neural Networks (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Networks (RNN). Also, this course is an elaboration of the time-series forecasting tutorial by TensorFlow.

Course Objectives

A high-level overview of the learning objectives of this course is as follows:

  • Learn what is time-series forecasting and its importance
  • Learn how to clean time-series data and how to engineer new features
  • Learn how to create data windows
  • Learn how to build and evaluate single-step time-series forecasting models
  • Learn how to build and evaluate multiple-step time-series forecasting models
  • Learn advanced time-series forecasting techniques

You can expect to meet all these objectives by the time you reach the end of this course.


If this is your first time working on deep learning, it may be hard for you to effectively grasp all the concepts. Therefore, the following pre-requisites are necessary for you to get the best out of the course:

  • Familiar with Pandas, Matplotlib and Numpy
  • Familiar with Python and TensorFlow 2.0
  • Solid understanding of the theoretical concepts of Deep Learning (Dense Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks)

If you do not have the above pre-requisites, don’t worry! You can always come back later to this course once you are ready.

Ready to build accurate forecasting models? See you in the course!



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Kharpann was founded by a team of mathematicians, programmers and machine learning/artificial engineers with a vision to help businesses find their data science team faster and to help them grow with their own data.
Price Free
Instructor Kharpann
Duration 2 weeks
Lectures 8
Enrolled 0 students
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