Data Skills for Artificial Intelligence

Overview

Syllabus

Courses

• 0 reviews

  6 weeks, 4-5 hours a week, 4-5 hours a week

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  Advances in Artificial Intelligence and Machine Learning have led to technological revolutions. Yet, AI systems at the forefront of such innovations have been the center of growing concerns. These involve reports of system failure when conditions are only slightly different from the training phase and they also trigger ethical and societal considerations that arise as a result of their use.

  Machine learning models have been criticized for lacking robustness, fairness and transparency. Such model-related problems can generally be attributed to a large extent to issues with data. In order to learn comprehensive, fine-grained and unbiased patterns, models have to be trained on a large number of high-quality data instances with distribution that accurately represents real application scenarios. Creating such data is not only a long, laborious and expensive process, but sometimes even impossible when the data is extremely imbalanced, or the distribution constantly evolves over time.

  This course will introduce an important method that can be used to gather data for training machine learning models and building AI systems. Crowdsourcing offers a viable means of leveraging human intelligence at scale for data creation, enrichment and interpretation with great potential to improve the performance of AI systems and increase the wider adoption of AI in general.

  By the end of this course you will be able to understand and apply crowdsourcing methods to elicit human input as a means of gathering high-quality data for machine learning. You will be able to identify biases in datasets as a result of how they are gathered or created and select from task design choices that can optimize data quality. These learnings will contribute to 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.

• 1 review

  6 weeks, 5-7 hours a week, 5-7 hours a week

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  Artificial Intelligence and Machine Learning have become central techniques for most services and products, ranging from web-based systems to medical procedures, self-driving cars – even intelligent coffee makers.

  Alongside algorithms, data is central to AI applications. Without solid data management, AI projects typically underperform or even fail. Unfortunately, the relevance and complexity of handling data is frequently underestimated.

  That’s why we developed this course which covers foundational questions like “Why is data important to AI?” and “What data does AI need?” and covers more application-oriented topics and skills like how to extract, load and query data using an SQL pipeline.

  In the second part of the course, 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.

  This introductory course is targeted at learners with little experience in data management or Python-based data management who want to develop Python-based AI applications in the future. The course covers a brief introduction into data management for AI, relational data management (e.g., SQL), and practical data handling skills in Python, pandas, and Jupyter.

  This allows you to build a foundation to prepare for future AI and Machine Learning development with Python.

Taught by

Christoph Lofi, Junzi Sun, Ujwal Gadiraju and Jie Yang

Tags

• netherlands