Machine Learning for Sequential Data
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Learn how to apply machine learning to sequential data, such as time series or text. Master algorithms like RNNs and LSTMs to model dependencies and make predictions based on sequential patterns in data, from stock prices to natural language.
At a Glance
In this project, we will analyze various sequential data types like text streams, audio clips, time-series data, and genetic data, and understand pre-processing techniques associated with each.
Sequential modelling is the process of forecasting a sequence of values from a set of input values. Input values can contain elements that are ordered into sequences like time-series, text streams, or DNA sequences. Lot of tasks can be modelled from these types of data. For example:
- text classification, e.g. spam email or not
- language translation, e.g. French to English
- time-series forecasting, e.g. stock prices prediction
A Look at the Project Ahead
After completing this Guided Project, you will be able to:
- Describe various forms of sequential data, and common tasks that can be modelled using sequential data
- Decompose a time-series and perform time-series imputation
- Pre-process and vectorize a text stream and genetic dataset
- Pre-process and visualize an audio dataset, and create spectrograms
This course mainly uses Python and JupyterLabs. Although these skills are recommended prerequisites, no prior experience is required as this Guided Project is designed for complete beginners.
Frequently Asked Questions
Your Instructor
Kopal Garg
I am a Data Scientist Intern at IBM, and a Masters student in computer science at the University of Toronto. I am passionate about building AI-based solutions that improve various aspects of human life.
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