Introduction to Machine Learning for Good
How can you use data for good? In this introductory course, students learn foundational theory and the coding skills necessary to translate data into actionable insights that improve communities and technical capacity around the world. Each of the hands-on, project-based modules use real-world data from Kiva, a non-profit that connects people through lending to alleviate poverty. Data is powerful, and we believe that anyone can harness it for change.
Machine learning is a highly interdisciplinary practice that demands critical thinking, an understanding of statistics, and technical coding ability. Irresponsible application of powerful algorithms or an inadequate exploration of underlying assumptions can lead to spurious results. In this course, we emphasize the fundamentals of proper data science and expose students to what is possible using sophisticated machine learning methods.
How can you use data for good? In this introductory course, students learn foundational theory and the coding skills necessary to translate data into actionable insights that improve communities and technical capacity around the world. Each of the hands-on, project-based modules use real-world data from Kiva, a non-profit that connects people through lending to alleviate poverty. Data is powerful, and we believe that anyone can harness it for change.
Machine learning is a highly interdisciplinary practice that demands critical thinking, an understanding of statistics, and technical coding ability. Irresponsible application of powerful algorithms or an inadequate exploration of underlying assumptions can lead to spurious results. In this course, we emphasize the fundamentals of proper data science and expose students to what is possible using sophisticated machine learning methods.
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0. Course Overview and Installation Instructions
a. Course Overview and Installation Instructions b. Introduction to Machine Learning c. Machine Learning Deep Dive d. Labs: Introduction to Python, Pandas, and Data Analysis |
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2. Decision Trees and Ensemble Algorithms
a. Theory: Decision Trees b. Theory: Ensemble Algorithms c. Labs: Decision Trees and Random Forests |
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5. Advanced Topics
a. Labs: Introduction to TensorFlow and Image Processing |