AI for Good Specialization

Learn AI's role in addressing complex challenges. Build skills combining human and machine intelligence for positive real-world impact using AI

Instructor: Robert Monarch

What you'll learn

  •   Master a step-by-step framework for the development of AI projects.
  •   Explore real-world case studies related to public health, climate change, and disaster management.
  •   Analyze data and build AI models for projects focused on air quality, wind energy, biodiversity monitoring, and disaster management.

    • Skills you'll gain

  •   Unstructured Data
  •   Jupyter
  •   Image Analysis
  •   Machine Learning
  •   Artificial Intelligence
  •   Statistical Analysis
  •   Predictive Modeling
  •   Disaster Recovery
  •   Applied Machine Learning
  •   Text Mining
  •   Forecasting
  •   Exploratory Data Analysis

    • Specialization - 3 course series

    Use natural language processing techniques to analyze trends in a corpus of text messages sent in the aftermath of the 2010 earthquake in Haiti.

    In this course, you will be introduced to the basics of artificial intelligence and machine learning and how they are applied in real-world scenarios in the AI for Good space. You will also be introduced to a framework for problem solving where AI is part of the solution. The course concludes with a case study featuring three Jupyter notebook labs where you’ll create an air quality monitoring application for the city of Bogotá, Colombia.

    In this course, you’ll start with a review of the mechanisms behind anthropogenic climate change and its impact on global temperatures and weather patterns. You will work through two case studies, one using time series analysis for wind power forecasting and another using computer vision for biodiversity monitoring. Both case studies are examples of where AI techniques can be part of the solution when it comes to the mitigation of and adaptation to climate change.

    In this course, you will be introduced to the four phases of the disaster management cycle; mitigation, preparation, response, and recovery. You’ll work through two case studies in this course. In the first, you will use computer vision to analyze satellite imagery from Hurricane Harvey in 2017 to identify damage in affected areas. In the second, you will use natural language processing techniques to explore trends in aid requests in the aftermath of the 2010 earthquake in Haiti.

    AI and Climate Change

    AI and Disaster Management

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