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J.C.(Junxing) Chen
and
Joseph Santarcangelo
Machine learning has changed the game for sports predictions. Popular Python libraries like LIME and SHAP are used to interpret and explain models. Even if you are not a soccer fan or working in the sports industry, machine learning skills are in demand in many industries. The skills needed to import and use data to create predictive models are both practical and valuable.
Read more
Machine learning has changed the game for sports predictions. Popular Python libraries like LIME and SHAP are used to interpret and explain models. Even if you are not a soccer fan or working in the sports industry, machine learning skills are in demand in many industries. The skills needed to import and use data to create predictive models are both practical and valuable.
Read more
Machine learning has changed the game for sports predictions. Popular Python libraries like LIME and SHAP are used to interpret and explain models. Even if you are not a soccer fan or working in the sports industry, machine learning skills are in demand in many industries. The skills needed to import and use data to create predictive models are both practical and valuable.
In this hands-on guided project, you’ll develop practical Python, pandas, numpy, sklearn, seaborn, matplotlib, seaborn, LIME, and SHAP skills to process data using the 2022 World Cup teams’ data. Then, you’ll train a model to predict the outcome of the group stages.
After completing this project, you will have practical experience working with Python machine-learning tools.
Get started fast. This hands-on guided project uses a browser-accessible development environment with the technologies and libraries you need, preinstalled—including the Python IDE—saving you setup time and complications. Also, note that this platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.
Register for this course and see more details by visiting:
OpenCourser.com/course/i237s2/guided
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What's inside
Learning objectives
- After completing this hands-on guided project, you’ll be able to:
- Choose and collect the data to import into the project
- Clean data for a machine learning project
- Understand objects needed for a machine learning project
- Use machine learning to predict sports games
- Analyze machine learning model using lime and shap
- After completing this hands-on guided project, you’ll be able to:
- Choose and collect the data to import into the project
- Clean data for a machine learning project
- Understand objects needed for a machine learning project
- Use machine learning to predict sports games
- Analyze machine learning model using lime and shap
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Could serve as a first project to introduce learners to basic machine learning
Students learn key machine learning skills such as data cleaning and handling using various Python libraries
Leverages the 2022 World Cup teams' data, which may be appealing to sports enthusiasts
Appropriate for learners interested in developing skills in data handling, model interpretation, and sports prediction using Python
Requires proficiency in Python and familiarity with machine learning concepts
Save this course
Save Guided Project: Predict World Cup Soccer Results with ML to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Guided Project: Predict World Cup Soccer Results with ML with these
activities:
Organize and Review Course Materials
Show steps
Enhance your understanding by organizing and reviewing notes, assignments, and other course materials.
Show steps
-
Create a system for organizing materials (e.g., digital or physical folders)
-
Review materials regularly and summarize key concepts
Review Machine Learning Basics
Show steps
Refresh your understanding of essential machine learning concepts, including supervised and unsupervised learning, model evaluation, and feature engineering.
Browse courses on
Machine Learning Basics
Show steps
-
Revisit key machine learning algorithms (e.g., linear regression, logistic regression, decision trees)
-
Review common machine learning metrics (e.g., accuracy, precision, recall)
Connect with Machine Learning Practitioners
Show steps
Reach out to professionals in the field to gain insights, ask questions, and explore potential career paths.
Show steps
-
Identify potential mentors through LinkedIn or professional organizations
-
Prepare a brief introduction and reach out via email or message
Six other activities
Expand to see all activities and additional details
Show all nine activities
Solve Python Coding Challenges
Show steps
Sharpen your Python programming skills by solving coding challenges related to data manipulation and model building.
Show steps
-
Work through online coding challenges (e.g., HackerRank, LeetCode)
-
Create custom coding exercises based on course materials
Predict Game Outcomes
Show steps
Use the skills learned in this course to predict outcomes of real-world soccer games.
Show steps
-
Gather data on soccer matches, such as team statistics, player performance, and match history.
-
Clean and prepare the data for analysis.
-
Train a machine learning model on the prepared data.
-
Predict the outcome of future soccer matches.
-
Evaluate the performance of the model by comparing the predictions to actual results.
Participate in Study Groups
Show steps
Collaborate with fellow students to reinforce concepts, discuss ideas, and work through problems together.
Show steps
-
Find or form a study group with other students
-
Set regular meeting times and discuss course concepts
Explore Machine Learning Libraries
Show steps
Enhance your understanding of the machine learning libraries used in this course by completing guided tutorials.
Browse courses on
LIME
Show steps
-
Follow official documentation or tutorials for the LIME and SHAP libraries.
-
Practice using the libraries' functions and methods in Python.
-
Apply the libraries to real-world datasets to gain practical experience.
Build a Simple Sports Prediction Model
Show steps
Apply the machine learning concepts learned in the course to develop a basic model for predicting sports outcomes.
Show steps
-
Gather and clean a relevant sports dataset
-
Train and evaluate a simple machine learning model
-
Interpret the model results and discuss limitations
Read Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Show steps
Delve into a comprehensive guide to machine learning libraries and techniques covered in the course.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
-
Study the fundamentals of supervised and unsupervised learning
-
Explore advanced topics such as deep learning and natural language processing
Organize and Review Course Materials
Show steps
Enhance your understanding by organizing and reviewing notes, assignments, and other course materials.
Show steps
Show steps
Show steps
- Create a system for organizing materials (e.g., digital or physical folders)
- Review materials regularly and summarize key concepts
Review Machine Learning Basics
Show steps
Refresh your understanding of essential machine learning concepts, including supervised and unsupervised learning, model evaluation, and feature engineering.
Browse courses on
Machine Learning Basics
Show steps
Show steps
Show steps
- Revisit key machine learning algorithms (e.g., linear regression, logistic regression, decision trees)
- Review common machine learning metrics (e.g., accuracy, precision, recall)
Connect with Machine Learning Practitioners
Show steps
Reach out to professionals in the field to gain insights, ask questions, and explore potential career paths.
Show steps
Show steps
Show steps
- Identify potential mentors through LinkedIn or professional organizations
- Prepare a brief introduction and reach out via email or message
Six other activities
Expand to see all activities and additional details
Show all nine activities
Solve Python Coding Challenges
Show steps
Sharpen your Python programming skills by solving coding challenges related to data manipulation and model building.
Show steps
Show steps
Show steps
- Work through online coding challenges (e.g., HackerRank, LeetCode)
- Create custom coding exercises based on course materials
Predict Game Outcomes
Show steps
Use the skills learned in this course to predict outcomes of real-world soccer games.
Show steps
Show steps
Show steps
- Gather data on soccer matches, such as team statistics, player performance, and match history.
- Clean and prepare the data for analysis.
- Train a machine learning model on the prepared data.
- Predict the outcome of future soccer matches.
- Evaluate the performance of the model by comparing the predictions to actual results.
Participate in Study Groups
Show steps
Collaborate with fellow students to reinforce concepts, discuss ideas, and work through problems together.
Show steps
Show steps
Show steps
- Find or form a study group with other students
- Set regular meeting times and discuss course concepts
Explore Machine Learning Libraries
Show steps
Enhance your understanding of the machine learning libraries used in this course by completing guided tutorials.
Browse courses on
LIME
Show steps
Show steps
Show steps
- Follow official documentation or tutorials for the LIME and SHAP libraries.
- Practice using the libraries' functions and methods in Python.
- Apply the libraries to real-world datasets to gain practical experience.
Build a Simple Sports Prediction Model
Show steps
Apply the machine learning concepts learned in the course to develop a basic model for predicting sports outcomes.
Show steps
Show steps
Show steps
- Gather and clean a relevant sports dataset
- Train and evaluate a simple machine learning model
- Interpret the model results and discuss limitations
Read Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Show steps
Delve into a comprehensive guide to machine learning libraries and techniques covered in the course.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
Show steps
Show steps
- Study the fundamentals of supervised and unsupervised learning
- Explore advanced topics such as deep learning and natural language processing
Career center
Learners who complete Guided Project: Predict World Cup Soccer Results with ML will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use their programming and analytical skills to build models that can predict outcomes and make recommendations based on data. This course in Guided Project: Predict World Cup Soccer Results with ML can provide you with foundational knowledge in machine learning, data analysis, and model building techniques, which are essential skills for Machine Learning Engineers.
Data Scientist
Data Scientist
Data Scientists use their expertise in statistics, mathematics, and computer science to analyze data and extract meaningful insights from it. This course in Guided Project: Predict World Cup Soccer Results with ML can help you develop your data analysis, data visualization, and machine learning skills, which are in high demand for Data Scientists.
Software Engineer
Software Engineer
Software Engineers design, develop, and test software applications. This course in Guided Project: Predict World Cup Soccer Results with ML can help you build a solid foundation in Python programming, data structures, and algorithms, which are essential for Software Engineers.
Data Analyst
Data Analyst
Data Analysts use their analytical skills to collect, clean, and analyze data to identify trends and patterns. This course in Guided Project: Predict World Cup Soccer Results with ML can help you develop your data analysis, data visualization, and statistical modeling skills, which are highly sought after by Data Analysts.
Business Analyst
Business Analyst
Business Analysts use their analytical and problem-solving skills to identify and solve business problems. This course in Guided Project: Predict World Cup Soccer Results with ML can help you develop your data analysis, problem-solving, and communication skills, which are essential for Business Analysts.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products and features. This course in Guided Project: Predict World Cup Soccer Results with ML can help you develop your analytical, problem-solving, and communication skills, which are essential for Product Managers.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their mathematical and statistical skills to analyze financial data and make investment recommendations. This course in Guided Project: Predict World Cup Soccer Results with ML can help you develop your data analysis, modeling, and forecasting skills, which are highly valued by Quantitative Analysts.
Market Researcher
Market Researcher
Market Researchers use their analytical and research skills to collect and analyze data about consumer behavior. This course in Guided Project: Predict World Cup Soccer Results with ML can help you develop your data analysis, research design, and communication skills, which are essential for Market Researchers.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their mathematical and analytical skills to improve the efficiency and effectiveness of business operations. This course in Guided Project: Predict World Cup Soccer Results with ML can help you develop your data analysis, optimization, and decision-making skills, which are highly sought after by Operations Research Analysts.
Financial Analyst
Financial Analyst
Financial Analysts use their analytical and financial modeling skills to evaluate investment opportunities and make recommendations to clients. This course in Guided Project: Predict World Cup Soccer Results with ML can help you develop your data analysis, financial modeling, and valuation skills, which are highly sought after by Financial Analysts.
Statistician
Statistician
Statisticians use their mathematical and statistical skills to collect, analyze, and interpret data. This course in Guided Project: Predict World Cup Soccer Results with ML can help you develop your data analysis, statistical modeling, and communication skills, which are essential for Statisticians.
Risk Analyst
Risk Analyst
Risk Analysts use their analytical and financial modeling skills to assess and manage risk. This course in Guided Project: Predict World Cup Soccer Results with ML can help you develop your data analysis, modeling, and forecasting skills, which are highly valued by Risk Analysts.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data infrastructure and systems. This course in Guided Project: Predict World Cup Soccer Results with ML can help you build a solid foundation in Python programming, data structures, and algorithms, which are essential for Data Engineers.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use their analytical and data visualization skills to extract meaningful insights from data and present them to stakeholders. This course in Guided Project: Predict World Cup Soccer Results with ML can help you develop your data analysis, data visualization, and communication skills, which are highly sought after by Business Intelligence Analysts.
Actuary
Actuary
Actuaries use their mathematical and statistical skills to assess and manage risk in the insurance and finance industries. This course in Guided Project: Predict World Cup Soccer Results with ML can help you develop your data analysis, modeling, and forecasting skills, which are highly valued by Actuaries.
For more career information including salaries, visit:
OpenCourser.com/course/i237s2/guided
Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Guided Project: Predict World Cup Soccer Results with ML.
Provides a comprehensive and mathematical treatment of statistical methods used in machine learning.
Classic reference on statistical learning, providing a comprehensive overview of machine learning algorithms and techniques.
Provides a theoretical foundation for machine learning, covering topics such as statistical learning theory, optimization, and generalization.
Presents machine learning from a probabilistic perspective, providing a deep understanding of the underlying theory and algorithms.
Save
Theoretical and practical guide to machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning.
Save
Comprehensive reference on deep learning, covering fundamental concepts, architectures, and applications.
Comprehensive guide to data science with Python, covering topics such as data exploration, data visualization, and machine learning.
Provides a practical guide to machine learning with Python, covering essential concepts and algorithms along with hands-on exercises.
Offers a practical and hands-on approach to machine learning, teaching how to build and deploy machine learning models quickly and effectively.
Provides a comprehensive and practical introduction to machine learning algorithms and techniques, including data preprocessing, model training, and evaluation.
Provides a practical guide to machine learning with R, covering essential concepts, algorithms, and applications.
Save
Teaches how to apply machine learning to business problems, covering topics such as data analysis, model building, and deployment.
Teaches the fundamentals of data science from scratch, covering topics such as data manipulation, machine learning, and data analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/i237s2/guided
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Effort
1 hours per week for 1 week
Level
Introductory
Via
edX
Institution
IBM
Instructors
J.C.(Junxing) Chen
Joseph Santarcangelo
Session
April 30, 2024
- April 30, 2025
Language
English
Transcript
English
Good to know
Could serve as a first project to introduce learners to basic machine learning
Students learn key machine learning skills such as data cleaning and handling using various Python libraries
Leverages the 2022 World Cup teams' data, which may be appealing to sports enthusiasts
Appropriate for learners interested in developing skills in data handling, model interpretation, and sports prediction using Python
Requires proficiency in Python and familiarity with machine learning concepts
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