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.
Free access everywhere.
This course is intended to be accessible to anyone, all over the world. Our curriculum structure of presenting theory alongside a real-life long-form data science project will open doors to novices and professionals alike to harness the power of data for good. In order to do that, we need to make sure the course material is accessible, useful, and error-free.
Here’s where we need your help.
Help us beta test!
Our curriculum is now open for beta testing! We encourage both people with no machine learning experience as well as data professionals to test our slides and provide constructive feedback about the course's level of difficulty, pace, and areas that could be improved.
Get right to it!
Module 0: Course Overview and Installation Instructions
Module 1: Introduction to Machine Learning
Module 2: Machine Learning Deep Dive
Module 3: Linear Regression
Module 4: Model Selection and Evaluation
Module 5: Decision Trees
Module 6: Ensemble Algorithms
Module 7: Unsupervised Learning
Module 8: NLP Part 1
Module 9: NLP Part 2
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.
Free access everywhere.
This course is intended to be accessible to anyone, all over the world. Our curriculum structure of presenting theory alongside a real-life long-form data science project will open doors to novices and professionals alike to harness the power of data for good. In order to do that, we need to make sure the course material is accessible, useful, and error-free.
Here’s where we need your help.
Help us beta test!
Our curriculum is now open for beta testing! We encourage both people with no machine learning experience as well as data professionals to test our slides and provide constructive feedback about the course's level of difficulty, pace, and areas that could be improved.
Get right to it!
Module 0: Course Overview and Installation Instructions
Module 1: Introduction to Machine Learning
Module 2: Machine Learning Deep Dive
Module 3: Linear Regression
Module 4: Model Selection and Evaluation
Module 5: Decision Trees
Module 6: Ensemble Algorithms
Module 7: Unsupervised Learning
Module 8: NLP Part 1
Module 9: NLP Part 2