Predict World Cup Results with Machine Learning
Learn to build a ML model to predict FIFA World Cup outcomes. Master Python libraries for data analysis and model interpretation in just one hour.
Learn to build a ML model to predict FIFA World Cup outcomes. Master Python libraries for data analysis and model interpretation in just one hour.
This hands-on guided project introduces beginners to practical data science and machine learning using the exciting context of World Cup soccer predictions. In less than an hour, you'll develop skills in Python, pandas, numpy, sklearn, and visualization libraries like matplotlib and seaborn. The course focuses on building a prediction model for the 2022 FIFA World Cup group stages, teaching you how to import and process real-world sports data, clean it for machine learning, and create predictive models. You'll also learn to use advanced interpretation tools like LIME and SHAP to analyze and explain your models. This project provides a perfect entry point for anyone interested in prediction models, offering practical experience with Python machine learning tools applicable across various industries. The course uses a browser-accessible development environment with pre-installed technologies, making it easy to start without setup complications.
Instructors:
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English
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What you'll learn
Select and import relevant data for a machine learning project
Clean and preprocess data specifically for sports prediction models
Understand and implement key components of a machine learning project
Build a predictive model for sports game outcomes using Python
Visualize data and results using matplotlib and seaborn
Apply LIME and SHAP techniques to interpret and explain machine learning models
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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Module Description
This guided project focuses on applying machine learning techniques to predict World Cup soccer results. Participants will learn the entire process of building a predictive model, from data collection and cleaning to model training and interpretation. The course covers essential Python libraries for data manipulation (pandas, numpy), machine learning (sklearn), and data visualization (matplotlib, seaborn). A unique aspect of this course is the introduction to model interpretation tools like LIME and SHAP, which are crucial for understanding and explaining machine learning models. By working with real 2022 World Cup team data, learners gain practical experience in sports analytics, a field with growing demand. The project is designed to be completed in under an hour, making it an efficient way to gain hands-on experience with machine learning concepts and tools applicable across various industries.
Fee Structure
Instructors
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1 Course
Passionate Data Scientist Democratizing Technical Education
J.C. (Junxing) Chen works as a Data Scientist at IBM Skills Network, where he has made significant contributions to technical education through his engaging and accessible teaching approach. His expertise spans across Machine Learning, Python, Data Science, Artificial Intelligence, and Data Analysis, demonstrated through his creation of 14 guided projects that have reached over 11,700 learners while maintaining an impressive 4.6 rating. His teaching philosophy centers on making data science approachable and practical for everyday applications, as evidenced by his contributions to advanced projects like "Unleashing the Power of Reinforcement Learning for Trading." Chen's unique approach combines technical expertise with a friendly, community-oriented teaching style, emphasizing the practical applications of data science while building meaningful connections with his students. His mission to spread data science knowledge and make it accessible to everyone reflects his belief that technical education should be both approachable and impactful in everyday life.
103 Courses
Pioneering Data Scientist Bridging AI Research and Education
Dr. Joseph Santarcangelo, a Data Scientist at IBM, brings a unique blend of academic excellence and practical expertise to the field of data science and artificial intelligence. With a Ph.D. in Electrical Engineering, his groundbreaking research focused on the intersection of machine learning, signal processing, and computer vision to understand how video content influences human cognitive processes. At IBM, he has established himself as a prominent educator and course developer, creating comprehensive learning materials that have reached hundreds of thousands of students worldwide. His teaching portfolio encompasses a wide range of technical subjects, from foundational Python programming to advanced topics in artificial intelligence, machine learning, and computer vision. Santarcangelo's ability to translate complex technical concepts into accessible learning experiences has made him an influential figure in data science education, maintaining consistently high ratings from learners while continuing to push the boundaries of applied machine learning and artificial intelligence research.
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