What you will learn
- Why data management is central to AI applications.
- What kind of data these applications need and how to obtain or extract data for AI applications using SQL.
- How to set up your Python notebooks, use the Pandas library to work with tabular data, and visualize data using the Seaborn library.
- Why crowdsourcing is a great method for gathering data and how cognitive biases and other human factors influence data quality.
- The value of active learning in the creation of crowdsourced training data and design of crowdsourcing tasks with quality control mechanisms.
Artificial Intelligence and Machine Learning have become integral techniques for most services and products and data is central to robust and successful AI/ML applications. Without solid data management, AI projects frequently underperform or even fail.
This series of MOOCs provides unique insights and key considerations about data for AI. In this program you will discover why is data important to AI and what data AI requires. You will learn basic data engineering skills, including how to setup your Python notebook environment, explore data with advanced Pandas functions, and create simple and clear data visualizations. You will also understand how crowdsourcing offers a viable means of leveraging human intelligence at scale for data creation, enrichment and interpretation and discover its potential to both improve the performance and trustworthiness of AI systems and stimulate the increased adoption of AI in general.
These learnings will provide you with an important set of skills that are essential for career trajectories in the field of Data Science, Machine Learning, and the broader realms of Artificial Intelligence.
Courses in this program
DelftX's Data Skills for Artificial Intelligence Professional Certificate
- 5–7 hours per week, for 6 weeks
Good data is central to effective AI applications. This course teaches the basics of data for AI, covering what data is needed, how to extract data from existing databases and basic data skills including setup of a Python notebook environment, basic data exploration and simple data visualizations.
- 4–5 hours per week, for 6 weeks
A one-stop shop to get started on the key considerations about data for AI! Learn how crowdsourcing offers a viable means to leverage human intelligence at scale for data creation, enrichment and interpretation, demonstrating a great potential to improve both the performance of AI systems and their trustworthiness and increase the adoption of AI in general.
- This course allows you to build a foundation and skills in data management and engineering as an ideal preparation for future AI and ML developments.
- A thorough understanding of the basics of AI and ML will help enhance career prospects in almost any sector as these applications become central to business practice.
- As a data engineer you will be able to improve levels of customer service, optimize existing business practices and automate many operations with a resulting reduction in costs and increase in revenue.
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