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.
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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.
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.
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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
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Review Python programming concepts
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Python Basics
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Review Python data types, control flow, and functions.
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Complete a few practice exercises to test your understanding.
Revisit statistics and probability concepts
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Strengthen your foundation in statistics and probability, which are essential concepts underlying machine learning algorithms.
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Statistics
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Review basic concepts such as mean, median, and standard deviation.
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Brush up on probability distributions, Bayes' theorem, and hypothesis testing.
Read 'Machine Learning for Dummies'
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Gain a solid foundation in the fundamentals of machine learning, including the concepts and techniques used in this course.
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Read chapters 1-3 to understand the basics of machine learning.
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Read chapters 4-6 to learn about Python libraries like NumPy and Pandas.
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Establish a centralized repository of course-related materials, including notes, assignments, and additional resources, for easy access and effective review.
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Create a folder or online platform to store course materials.
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Organize materials into folders based on topics or modules.
Follow the scikit-learn tutorials
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Develop practical skills in using scikit-learn, a popular Python library for machine learning, to implement various algorithms.
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scikit-learn
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Complete the 'Machine Learning with scikit-learn' tutorial.
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Explore additional tutorials on specific algorithms, such as 'Support Vector Machines' or 'Decision Trees'.
Join a study group to discuss course concepts
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Engage in peer-to-peer learning by joining a study group, where you can discuss course materials, share insights, and clarify concepts.
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Collaborative Learning
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Find or start a study group with other students in the course.
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Meet regularly to discuss course topics, work on assignments together, and quiz each other.
Solve coding exercises on LeetCode
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Enhance your programming skills and apply machine learning concepts by solving coding exercises on LeetCode.
Browse courses on
Machine Learning Algorithms
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Solve easy-level problems related to data structures and algorithms.
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Attempt medium-level problems involving machine learning techniques.
Write a blog post on a machine learning topic
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Deepen your understanding of machine learning by researching and writing about a topic of interest, reinforcing your knowledge through the process.
Browse courses on
Machine Learning Applications
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Identify a specific topic within machine learning that you want to explore.
-
Research and gather information from credible sources.
-
Write a blog post that clearly explains the topic and shares your insights.
Review Python programming concepts
Show steps
Refresh your understanding of Python programming fundamentals, ensuring a strong foundation for the course's technical aspects.
Browse courses on
Python Basics
Show steps
- Review Python data types, control flow, and functions.
- Complete a few practice exercises to test your understanding.
Revisit statistics and probability concepts
Show steps
Strengthen your foundation in statistics and probability, which are essential concepts underlying machine learning algorithms.
Browse courses on
Statistics
Show steps
- Review basic concepts such as mean, median, and standard deviation.
- Brush up on probability distributions, Bayes' theorem, and hypothesis testing.
Read 'Machine Learning for Dummies'
Show steps
Gain a solid foundation in the fundamentals of machine learning, including the concepts and techniques used in this course.
View
Data Analytics & Visualization All-in-One For...
on Amazon
Show steps
- Read chapters 1-3 to understand the basics of machine learning.
- Read chapters 4-6 to learn about Python libraries like NumPy and Pandas.
Create a course resource repository
Show steps
Establish a centralized repository of course-related materials, including notes, assignments, and additional resources, for easy access and effective review.
Show steps
- Create a folder or online platform to store course materials.
- Organize materials into folders based on topics or modules.
Follow the scikit-learn tutorials
Show steps
Develop practical skills in using scikit-learn, a popular Python library for machine learning, to implement various algorithms.
Browse courses on
scikit-learn
Show steps
- Complete the 'Machine Learning with scikit-learn' tutorial.
- Explore additional tutorials on specific algorithms, such as 'Support Vector Machines' or 'Decision Trees'.
Join a study group to discuss course concepts
Show steps
Engage in peer-to-peer learning by joining a study group, where you can discuss course materials, share insights, and clarify concepts.
Browse courses on
Collaborative Learning
Show steps
- Find or start a study group with other students in the course.
- Meet regularly to discuss course topics, work on assignments together, and quiz each other.
Solve coding exercises on LeetCode
Show steps
Enhance your programming skills and apply machine learning concepts by solving coding exercises on LeetCode.
Browse courses on
Machine Learning Algorithms
Show steps
- Solve easy-level problems related to data structures and algorithms.
- Attempt medium-level problems involving machine learning techniques.
Write a blog post on a machine learning topic
Show steps
Deepen your understanding of machine learning by researching and writing about a topic of interest, reinforcing your knowledge through the process.
Browse courses on
Machine Learning Applications
Show steps
- Identify a specific topic within machine learning that you want to explore.
- Research and gather information from credible sources.
- Write a blog post that clearly explains the topic and shares your insights.
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