Welcome to this course on Time-Series Forecasting with TensorFlow 2.0! In this course, you will be learning how to build a powerful time series forecasting model using TensorFlow 2.0 and also, learn how to perform time series analysis.
This course on time series forecasting will teach you how to use various kinds of deep learning algorithms such as Dense Neural Networks (DNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) for building such models. Also, this course is an elaboration of the time-series forecasting tutorial by TensorFlow.
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.
Best way to work through the course
The course is not long but requires a good amount of attention from your end.
Before moving to the next lecture, we suggest you set up your coding environment and open up your Jupyter Notebook. If you are a more advanced user of Python and have your own preferences, please feel free to choose an IDE that you prefer. However, all of the coding examples will be written for execution on Jupyter Notebook cells. Also, we have used TensorFlow 2.3.0 for this course and it is suggested to update your TensorFlow version to >=2.3.0 in order to follow along with this course smoothly.
If you come across any problem, please check to see if your code matches exactly with the course or not. If you still are facing errors or have some doubts, please provide your question through the comment section of the specific chapter you are stuck on.
We also recommend you join our community and get connected to our vibrant network of data science aspirants. Once you are in the community, you can share your learnings, form a study group, or even get help building a project around Deep Learning using Tensorflow 2.0.
Ready to add a fundamental skill to your Data Science toolbelt? Let’s get started. Head over to the first lesson of this course on ‘Getting Started with Time Series Data‘.
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