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Google Cloud Training
This is a self-paced lab that takes place in the Google Cloud console. In this lab you use Machine Learning (ML) to analyze the public NCAA dataset and predict NCAA tournament brackets.
Register for this course and see more details by visiting:
OpenCourser.com/course/mpralq/bracketology
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What's inside
Syllabus
Bracketology with Google Machine Learning
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops beginner-friendly skills in machine learning
Focuses on predicting NCAA tournament brackets, which is a popular application of machine learning
Taught by Google Cloud Training, an entity recognized for its expertise in machine learning
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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 Bracketology with Google Machine Learning with these
activities:
Revisit Pre-Algebra
Show steps
Brush up on your Pre-Algebra skills to strengthen your foundation for Machine Learning, especially in areas like linear equations and graphing.
Show steps
-
Review notes and textbooks from previous Pre-Algebra courses
-
Solve practice problems and equations
-
Take online quizzes or tests to assess your understanding
Explore Google Cloud Machine Learning Engine
Show steps
Familiarize yourself with Google Cloud Machine Learning Engine, the platform you'll use throughout the course, through guided tutorials and documentation.
Browse courses on
Machine Learning
Show steps
-
Follow the 'Getting Started' guide on the Machine Learning Engine website
-
Complete the 'Machine Learning Crash Course' tutorial series on Coursera
-
Explore additional tutorials and documentation on specific Machine Learning Engine features
Interactive Coding Exercises
Show steps
Strengthen your coding skills by practicing with interactive coding exercises on platforms like LeetCode or HackerRank, focusing on Python and Machine Learning algorithms.
Browse courses on
Python
Show steps
-
Choose a platform and select exercises related to Machine Learning
-
Solve the exercises, debugging and optimizing your code
-
Review solutions and explanations to enhance your understanding
Six other activities
Expand to see all activities and additional details
Show all nine activities
NCAA Basketball Dataset Analysis
Show steps
Practice applying machine learning techniques to analyze the NCAA basketball dataset and make predictions.
Show steps
-
Load and explore the dataset
-
Train a machine learning model
-
Make predictions on the tournament bracket
Create a Comprehensive Course Reference
Show steps
Organize and synthesize course materials, including notes, assignments, and resources, into a comprehensive reference to aid your learning and retention.
Show steps
-
Gather all relevant materials from the course
-
Organize the materials into a logical structure
-
Create a searchable index or table of contents
-
Review and update the reference regularly
Contribute to Open Source Projects
Show steps
Deepen your understanding by contributing to open source Machine Learning projects on platforms like GitHub, collaborating with developers and applying your skills in a real-world setting.
Browse courses on
Machine Learning
Show steps
-
Identify open source projects related to your interests
-
Find issues or areas where you can contribute
-
Fork the project, make your changes, and submit a pull request
-
Collaborate with other contributors and maintain your contributions
Summarize Machine Learning Concepts
Show steps
Reinforce your understanding by summarizing key concepts of supervised and unsupervised machine learning, focusing on the algorithms and techniques covered in the course.
Browse courses on
Machine Learning
Show steps
-
Read through your notes and identify the main concepts
-
Create a mind map or diagram to visually organize the concepts
-
Write a concise summary, focusing on definitions, algorithms, and applications
Participate in a Kaggle Competition
Show steps
Test your skills and gain practical experience by participating in a Kaggle competition related to Machine Learning, applying the concepts and techniques learned in the course.
Browse courses on
Machine Learning
Show steps
-
Identify a suitable Kaggle competition
-
Explore the competition data and familiarize yourself with the problem statement
-
Develop a Machine Learning model and train it using the provided data
-
Evaluate and optimize your model
Lead a Machine Learning Study Group
Show steps
Solidify your knowledge by leading a study group for other learners interested in Machine Learning, sharing your insights and facilitating discussions.
Browse courses on
Machine Learning
Show steps
-
Gather a group of interested learners
-
Plan discussion topics and activities
-
Lead the study sessions, facilitating discussions and answering questions
-
Provide guidance and support to the group members
Revisit Pre-Algebra
Show steps
Brush up on your Pre-Algebra skills to strengthen your foundation for Machine Learning, especially in areas like linear equations and graphing.
Show steps
Show steps
Show steps
- Review notes and textbooks from previous Pre-Algebra courses
- Solve practice problems and equations
- Take online quizzes or tests to assess your understanding
Explore Google Cloud Machine Learning Engine
Show steps
Familiarize yourself with Google Cloud Machine Learning Engine, the platform you'll use throughout the course, through guided tutorials and documentation.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Follow the 'Getting Started' guide on the Machine Learning Engine website
- Complete the 'Machine Learning Crash Course' tutorial series on Coursera
- Explore additional tutorials and documentation on specific Machine Learning Engine features
Interactive Coding Exercises
Show steps
Strengthen your coding skills by practicing with interactive coding exercises on platforms like LeetCode or HackerRank, focusing on Python and Machine Learning algorithms.
Browse courses on
Python
Show steps
Show steps
Show steps
- Choose a platform and select exercises related to Machine Learning
- Solve the exercises, debugging and optimizing your code
- Review solutions and explanations to enhance your understanding
Six other activities
Expand to see all activities and additional details
Show all nine activities
NCAA Basketball Dataset Analysis
Show steps
Practice applying machine learning techniques to analyze the NCAA basketball dataset and make predictions.
Show steps
Show steps
Show steps
- Load and explore the dataset
- Train a machine learning model
- Make predictions on the tournament bracket
Create a Comprehensive Course Reference
Show steps
Organize and synthesize course materials, including notes, assignments, and resources, into a comprehensive reference to aid your learning and retention.
Show steps
Show steps
Show steps
- Gather all relevant materials from the course
- Organize the materials into a logical structure
- Create a searchable index or table of contents
- Review and update the reference regularly
Contribute to Open Source Projects
Show steps
Deepen your understanding by contributing to open source Machine Learning projects on platforms like GitHub, collaborating with developers and applying your skills in a real-world setting.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify open source projects related to your interests
- Find issues or areas where you can contribute
- Fork the project, make your changes, and submit a pull request
- Collaborate with other contributors and maintain your contributions
Summarize Machine Learning Concepts
Show steps
Reinforce your understanding by summarizing key concepts of supervised and unsupervised machine learning, focusing on the algorithms and techniques covered in the course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Read through your notes and identify the main concepts
- Create a mind map or diagram to visually organize the concepts
- Write a concise summary, focusing on definitions, algorithms, and applications
Participate in a Kaggle Competition
Show steps
Test your skills and gain practical experience by participating in a Kaggle competition related to Machine Learning, applying the concepts and techniques learned in the course.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify a suitable Kaggle competition
- Explore the competition data and familiarize yourself with the problem statement
- Develop a Machine Learning model and train it using the provided data
- Evaluate and optimize your model
Lead a Machine Learning Study Group
Show steps
Solidify your knowledge by leading a study group for other learners interested in Machine Learning, sharing your insights and facilitating discussions.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Gather a group of interested learners
- Plan discussion topics and activities
- Lead the study sessions, facilitating discussions and answering questions
- Provide guidance and support to the group members
Career center
Learners who complete Bracketology with Google Machine Learning will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts study how data can be used to create value for businesses. They provide insights that can help businesses make better decisions, improve operations, and increase profitability. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in machine learning and how it can be used to analyze data.
Data Scientist
Data Scientist
Data Scientists use machine learning and other techniques to extract insights from data. They work on a variety of projects, such as developing predictive models, identifying trends, and creating visualizations. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in machine learning and how it can be used to analyze data.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models. They work on a variety of projects, such as developing new algorithms, improving existing models, and deploying models to production. This course can help you develop the skills needed to succeed in this role by providing you with a foundation in machine learning and how it can be used to analyze data.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work on a variety of projects, such as developing new features, fixing bugs, and improving performance. This course may be useful for Software Engineers who want to learn more about machine learning and how it can be used to analyze data.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. They use statistical methods to solve problems and make predictions. This course may be useful for Statisticians who want to learn more about machine learning and how it can be used to analyze data.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. They work on a variety of projects, such as developing new data sources, cleaning and transforming data, and creating data visualizations. This course may be useful for Data Engineers who want to learn more about machine learning and how it can be used to analyze data.
Business Analyst
Business Analyst
Business Analysts identify and solve business problems. They use data to understand customer needs, improve operations, and increase profitability. This course may be useful for Business Analysts who want to learn more about machine learning and how it can be used to analyze data.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data to understand customer behavior and develop marketing campaigns. They work on a variety of projects, such as developing new marketing strategies, tracking the results of marketing campaigns, and identifying new customer segments. This course may be useful for Marketing Analysts who want to learn more about machine learning and how it can be used to analyze data.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. They work on a variety of projects, such as evaluating companies, analyzing financial statements, and developing investment strategies. This course may be useful for Financial Analysts who want to learn more about machine learning and how it can be used to analyze data.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to solve business problems. They work on a variety of projects, such as optimizing supply chains, scheduling production, and designing new products. This course may be useful for Operations Research Analysts who want to learn more about machine learning and how it can be used to analyze data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to make investment decisions. They work on a variety of projects, such as developing trading strategies, managing risk, and pricing financial instruments. This course may be useful for Quantitative Analysts who want to learn more about machine learning and how it can be used to analyze data.
Product Analyst
Product Analyst
Product Analysts use data to improve products. They work on a variety of projects, such as developing new product features, tracking the usage of products, and identifying customer pain points. This course may be useful for Product Analysts who want to learn more about machine learning and how it can be used to analyze data.
User Experience Researcher
User Experience Researcher
User Experience Researchers study how people interact with products. They work on a variety of projects, such as developing new product features, testing product usability, and identifying customer pain points. This course may be useful for User Experience Researchers who want to learn more about machine learning and how it can be used to analyze data.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to make business decisions. They work on a variety of projects, such as developing new business strategies, improving operations, and increasing profitability. This course may be useful for Business Intelligence Analysts who want to learn more about machine learning and how it can be used to analyze data.
Risk Analyst
Risk Analyst
Risk Analysts use data to identify and manage risk. They work on a variety of projects, such as developing risk management strategies, assessing the impact of risk, and making recommendations to reduce risk. This course may be useful for Risk Analysts who want to learn more about machine learning and how it can be used to analyze data.
For more career information including salaries, visit:
OpenCourser.com/course/mpralq/bracketology
Reading list
We've selected 14 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
Bracketology with Google Machine Learning.
This comprehensive textbook covers a wide range of machine learning topics, from basic concepts to advanced techniques, with a focus on Bayesian approaches.
This advanced textbook provides a rigorous and in-depth treatment of statistical learning methods, with a focus on theoretical concepts and mathematical derivations.
Combines theoretical foundations with practical applications, covering a wide range of statistical learning methods and providing a comprehensive understanding of the field.
Provides a practical, step-by-step approach for exploring and implementing various machine learning pipelines using popular open source frameworks.
Provides a foundation in the mathematics and theoretical concepts underlying machine learning, with a focus on probability, linear algebra, and optimization.
Approaches machine learning from a probabilistic perspective, providing a solid foundation in the underlying mathematical concepts and algorithms.
This hands-on guide provides a practical introduction to machine learning, focusing on real-world applications and providing numerous code examples.
From the creator of Keras aims to make deep learning more accessible and approachable, providing a comprehensive overview of theory and hands-on examples.
Save
This practical guide focuses on applying predictive modeling techniques to real-world problems, covering various methods and providing hands-on exercises.
Save
This practical guide provides a comprehensive overview of data mining techniques, with a focus on real-world applications and providing hands-on examples.
Provides a comprehensive introduction to Python for data analysis, covering data cleaning, manipulation, visualization, and statistical modeling using popular libraries such as NumPy, Pandas, and Matplotlib.
Save
Provides a comprehensive introduction to R for data science, covering data cleaning, manipulation, visualization, and statistical modeling using the tidyverse suite of packages.
This approachable book provides a hands-on introduction to machine learning for those with no prior experience, covering basic concepts and practical applications.
This beginner-friendly book provides a practical introduction to Python programming, focusing on automating tasks and solving common problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/mpralq/bracketology
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Effort
1 hour
Level
Introductory
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Good to know
Develops beginner-friendly skills in machine learning
Focuses on predicting NCAA tournament brackets, which is a popular application of machine learning
Taught by Google Cloud Training, an entity recognized for its expertise in machine learning
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