To view this video please enable JavaScript.
Course Description
In this course, you will explore the world of time series forecasting using TensorFlow, a powerful open-source library for deep learning. Discover the techniques and tools to preprocess and prepare time series data, build sequential models, and make accurate predictions. From understanding the importance of data preprocessing to implementing state-of-the-art models, this course equips you with the skills to excel in the field of time series forecasting.
The course begins with a strong foundation in data preprocessing and preparation. You will learn how to handle missing values, normalize data, and handle categorical variables specific to time series data. With hands-on exercises and real-world examples, you will gain expertise in cleaning and transforming time series datasets.
We also explore the power of sequential models in time series forecasting, and dive into the concepts of recurrent neural networks (RNNs), long short-term memory (LSTM), and convolutional neural networks (CNNs) for analyzing and predicting sequential patterns. We’ll discuss building and training these models using TensorFlow and its vast array of tools and functionalities.
Throughout the course, you will have the opportunity to apply your knowledge through hands-on coding exercises and projects. Working with real-world time series datasets helps you develop practical skills that can be directly applied to your own forecasting projects.
What You'll Learn
- Preprocess and prepare time series data, including handling missing values, normalizing data, and handling categorical variables
- Build sequential models for time series forecasting using TensorFlow
- Train and optimize sequential models for accurate time series predictions
- Apply advanced techniques such as hyperparameter tuning and ensembling
- Evaluate the performance of time series models using appropriate metrics
- Understand the fundamentals of time series forecasting and its applications
Key Takeaways
- TensorFlow is a powerful open-source library for deep learning used here to build and train models for time series forecasting.
- Data preprocessing and preparation form the foundation of the course, covering cleaning and transforming time series datasets.
- Recurrent neural networks (RNNs), long short-term memory (LSTM), and convolutional neural networks (CNNs) are used for analyzing and predicting sequential patterns.
- The course includes hands-on coding exercises and projects using real-world time series datasets to develop practical, directly applicable skills.
- Model performance is evaluated using appropriate metrics, alongside techniques such as hyperparameter tuning and ensembling.
Frequently Asked Questions
What will I learn in this course?
You will learn to preprocess and prepare time series data, build sequential models, train and optimize them for accurate predictions, apply techniques such as hyperparameter tuning and ensembling, and evaluate model performance using appropriate metrics, all using TensorFlow.
Which deep learning models does this course cover?
The course dives into recurrent neural networks (RNNs), long short-term memory (LSTM), and convolutional neural networks (CNNs) for analyzing and predicting sequential patterns, and covers building and training these models using TensorFlow.
Is this course hands-on?
Yes. Throughout the course you apply your knowledge through hands-on coding exercises and projects, working with real-world time series datasets to develop practical skills you can apply to your own forecasting projects.
What topics are covered in the lessons?
The lessons cover an overview of sequences and time series forecasting, an introduction to sequences, preprocessing and data preparation for time series data, building a sequential model for time series forecasting, and training and evaluating the model.
What skills will I gain from this course?
You will build skills in forecasting, data science, financial forecasting, TensorFlow, and time series, aligned with the SAS Certified Specialist: Forecasting and Optimization area.
