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Jaekwang KIM
In this course, you will:
a) understand the basic concepts of machine learning.
b) understand a typical memory-based method, the K nearest neighbor method.
c) understand linear regression.
d) understand model analysis.
Read more
In this course, you will:
a) understand the basic concepts of machine learning.
b) understand a typical memory-based method, the K nearest neighbor method.
c) understand linear regression.
d) understand model analysis.
Read more
In this course, you will:
a) understand the basic concepts of machine learning.
b) understand a typical memory-based method, the K nearest neighbor method.
c) understand linear regression.
d) understand model analysis.
Please make sure that you’re comfortable programming in Python and have a basic knowledge of mathematics including matrix multiplications, and conditional probability.
Register for this course and see more details by visiting:
OpenCourser.com/course/2zccep/machine
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What's inside
Syllabus
The basic concepts of machine learning
The k-Nearest Neighbors
Linear Regression
Read more
Syllabus
The basic concepts of machine learning
The k-Nearest Neighbors
Linear Regression
Read more
Logistic Regression
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for those just beginning to learn the fundamentals of machine learning
Provides a strong foundation for those interested in pursuing a career in machine learning
Taught by experienced instructors in the field, providing students with valuable insights
Requires basic knowledge in mathematics and programming in Python, making it accessible to learners with a technical foundation
Save this course
Save Machine Learning Basics to your list so you can find it easily later:
Save
Save
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 Machine Learning Basics with these
activities:
Brush up on Python programming basics
Show steps
Ensure a solid foundation in Python programming to support your learning throughout the course.
Browse courses on
Python Programming
Show steps
-
Review basic syntax, data types, and control flow
-
Practice working with lists, dictionaries, and other data structures
-
Complete coding exercises to test your understanding
Review Mathematics for Machine Learning
Show steps
Review this book for an introduction to the mathematical skills and concepts used in this machine learning course.
View
Mathematics for Machine Learning
on Amazon
Show steps
-
Read the book's first three chapters
-
Complete the exercise problems at the end of each chapter
Connect with experts in machine learning
Show steps
Enhance your learning by connecting with experienced professionals in the field of machine learning.
Show steps
-
Attend online or in-person meetups and conferences
-
Reach out to professors, researchers, or industry practitioners
-
Join online communities and forums related to machine learning
Five other activities
Expand to see all activities and additional details
Show all eight activities
K-Nearest Neighbors practice problems
Show steps
Reinforce your understanding of K-Nearest Neighbors by solving various practice problems, focusing on distance metrics and parameter optimization.
Browse courses on
K-Nearest Neighbors
Show steps
-
Implement the K-NN algorithm from scratch using a programming language of your choice
-
Experiment with different distance metrics, such as Euclidean distance and cosine similarity
-
Tune the value of K to optimize the accuracy of your model
Explain linear regression to a non-technical audience
Show steps
Deepen your understanding of linear regression by explaining its concepts and applications to a non-technical audience.
Browse courses on
Linear Regression
Show steps
-
Write a clear and concise explanation of the linear regression model
-
Provide real-world examples of how linear regression is used in practice
-
Illustrate the process of training and evaluating a linear regression model
Build a resource library for machine learning
Show steps
Organize your learning resources and stay up-to-date by compiling a comprehensive library of machine learning materials.
Show steps
-
Collect relevant articles, tutorials, and online courses
-
Use tools like Mendeley or Zotero to organize your references
-
Create a website or online repository to share your resource library
Apply your machine learning skills to a social impact project
Show steps
Contribute to the community while enhancing your learning by applying your machine learning skills to a meaningful project.
Show steps
-
Identify a social cause or organization that aligns with your interests
-
Develop a machine learning solution that addresses a specific problem
-
Collaborate with others to implement and evaluate your solution
Contribute to an open-source machine learning project
Show steps
Gain practical experience and contribute to the machine learning community by participating in open-source projects.
Show steps
-
Find a project that aligns with your interests and skills
-
Review the project's documentation and codebase
-
Identify an area where you can make a meaningful contribution
-
Submit a pull request with your proposed changes
Brush up on Python programming basics
Show steps
Ensure a solid foundation in Python programming to support your learning throughout the course.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Review basic syntax, data types, and control flow
- Practice working with lists, dictionaries, and other data structures
- Complete coding exercises to test your understanding
Review Mathematics for Machine Learning
Show steps
Review this book for an introduction to the mathematical skills and concepts used in this machine learning course.
View
Mathematics for Machine Learning
on Amazon
Show steps
Show steps
Show steps
- Read the book's first three chapters
- Complete the exercise problems at the end of each chapter
Connect with experts in machine learning
Show steps
Enhance your learning by connecting with experienced professionals in the field of machine learning.
Show steps
Show steps
Show steps
- Attend online or in-person meetups and conferences
- Reach out to professors, researchers, or industry practitioners
- Join online communities and forums related to machine learning
Five other activities
Expand to see all activities and additional details
Show all eight activities
K-Nearest Neighbors practice problems
Show steps
Reinforce your understanding of K-Nearest Neighbors by solving various practice problems, focusing on distance metrics and parameter optimization.
Browse courses on
K-Nearest Neighbors
Show steps
Show steps
Show steps
- Implement the K-NN algorithm from scratch using a programming language of your choice
- Experiment with different distance metrics, such as Euclidean distance and cosine similarity
- Tune the value of K to optimize the accuracy of your model
Explain linear regression to a non-technical audience
Show steps
Deepen your understanding of linear regression by explaining its concepts and applications to a non-technical audience.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Write a clear and concise explanation of the linear regression model
- Provide real-world examples of how linear regression is used in practice
- Illustrate the process of training and evaluating a linear regression model
Build a resource library for machine learning
Show steps
Organize your learning resources and stay up-to-date by compiling a comprehensive library of machine learning materials.
Show steps
Show steps
Show steps
- Collect relevant articles, tutorials, and online courses
- Use tools like Mendeley or Zotero to organize your references
- Create a website or online repository to share your resource library
Apply your machine learning skills to a social impact project
Show steps
Contribute to the community while enhancing your learning by applying your machine learning skills to a meaningful project.
Show steps
Show steps
Show steps
- Identify a social cause or organization that aligns with your interests
- Develop a machine learning solution that addresses a specific problem
- Collaborate with others to implement and evaluate your solution
Contribute to an open-source machine learning project
Show steps
Gain practical experience and contribute to the machine learning community by participating in open-source projects.
Show steps
Show steps
Show steps
- Find a project that aligns with your interests and skills
- Review the project's documentation and codebase
- Identify an area where you can make a meaningful contribution
- Submit a pull request with your proposed changes
Career center
Learners who complete Machine Learning Basics will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. This course can help you learn the basics of machine learning, which is essential for this role. You will learn about different machine learning algorithms, how to train and evaluate models, and how to deploy models to production.
Data Scientist
Data Scientist
Data Scientists use machine learning and other statistical methods to extract insights from data. This course can help you build a foundation in machine learning, which is a critical skill for Data Scientists. You will learn about different machine learning algorithms, how to train and evaluate models, and how to use machine learning to solve real-world problems.
Data Analyst
Data Analyst
A Data Analyst collects, cleans, and analyzes data to help businesses make informed decisions. This course can help you build a foundation in machine learning, which is a valuable skill for Data Analysts. Machine learning can be used to automate data analysis tasks, such as identifying patterns and trends. This can help Data Analysts save time and improve the accuracy of their work.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical methods to analyze financial data and make investment decisions. This course can help you build a foundation in machine learning, which is increasingly being used in quantitative finance. You will learn about different machine learning algorithms, how to train and evaluate models, and how to use machine learning to make investment decisions.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. This course can help you build a foundation in machine learning, which is increasingly being used in statistics. You will learn about different machine learning algorithms, how to train and evaluate models, and how to use machine learning to solve statistical problems.
Underwriter
Underwriter
Underwriters assess risk and determine the terms of insurance policies. This course can help you build a foundation in machine learning, which is increasingly being used in underwriting. You will learn about different machine learning algorithms, how to train and evaluate models, and how to use machine learning to improve underwriting decisions.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical methods to improve the efficiency of business operations. This course can help you build a foundation in machine learning, which is increasingly being used in operations research. You will learn about different machine learning algorithms, how to train and evaluate models, and how to use machine learning to solve operations research problems.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course can help you build a foundation in machine learning, which is increasingly being used in software development. You will learn about different machine learning algorithms, how to train and evaluate models, and how to deploy models to production.
Financial Analyst
Financial Analyst
Financial Analysts use data to help businesses make better financial decisions. This course can help you build a foundation in machine learning, which is increasingly being used in finance. You will learn about different machine learning algorithms, how to train and evaluate models, and how to use machine learning to solve financial problems.
Risk Analyst
Risk Analyst
Risk Analysts use data to help businesses identify and mitigate risks. This course can help you build a foundation in machine learning, which is increasingly being used in risk management. You will learn about different machine learning algorithms, how to train and evaluate models, and how to use machine learning to solve risk management problems.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. This course can help you build a foundation in machine learning, which is increasingly being used in data engineering. You will learn about different machine learning algorithms, how to train and evaluate models, and how to deploy models to production.
Actuary
Actuary
Actuaries use mathematical and statistical methods to assess risk and uncertainty. This course can help you build a foundation in machine learning, which is increasingly being used in actuarial science. You will learn about different machine learning algorithms, how to train and evaluate models, and how to use machine learning to solve actuarial problems.
Marketing Analyst
Marketing Analyst
Marketing Analysts use data to help businesses make better marketing decisions. This course can help you build a foundation in machine learning, which is increasingly being used in marketing. You will learn about different machine learning algorithms, how to train and evaluate models, and how to use machine learning to solve marketing problems.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course can help you build a foundation in machine learning, which is increasingly being used in product management. You will learn about different machine learning algorithms, how to train and evaluate models, and how to use machine learning to improve product development.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make better decisions. This course can help you build a foundation in machine learning, which is increasingly being used in business analysis. You will learn about different machine learning algorithms, how to train and evaluate models, and how to use machine learning to solve business problems.
For more career information including salaries, visit:
OpenCourser.com/course/2zccep/machine
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
Machine Learning Basics.
Provides a comprehensive and mathematically rigorous treatment of machine learning from a probabilistic perspective. It valuable resource for learners who want to gain a deeper understanding of the theoretical foundations of machine learning.
Provides a comprehensive overview of statistical learning methods, which are closely related to machine learning methods. It valuable resource for learners who want to gain a deeper understanding of the statistical foundations of machine learning.
Save
Provides a comprehensive overview of deep learning, a subfield of machine learning that has gained significant popularity in recent years. It valuable resource for learners who want to gain a deep understanding of deep learning algorithms and their applications.
Provides a comprehensive overview of machine learning algorithms and their applications. It valuable resource for learners who want to gain a deeper understanding of the theoretical foundations and practical applications of machine learning.
Provides a practical introduction to machine learning using Python and popular libraries such as Scikit-Learn, Keras, and TensorFlow. It valuable resource for learners who want to gain hands-on experience with machine learning algorithms.
Provides a practical introduction to machine learning for healthcare professionals. It valuable resource for learners who want to gain a deeper understanding of how machine learning can be used to improve patient outcomes.
Provides a comprehensive overview of machine learning algorithms for natural language processing. It valuable resource for learners who want to gain a deeper understanding of how machine learning can be used to process and understand natural language.
Provides a practical introduction to machine learning for business professionals. It valuable resource for learners who want to gain a deeper understanding of how machine learning can be used to improve business outcomes.
Provides a collection of recipes for solving common machine learning problems using Python. It valuable resource for learners who want to quickly and easily apply machine learning algorithms to real-world problems.
Provides a practical introduction to machine learning for finance professionals. It valuable resource for learners who want to gain a deeper understanding of how machine learning can be used to improve financial outcomes.
Provides a comprehensive introduction to machine learning using the R programming language. It valuable resource for learners who want to gain a practical understanding of machine learning algorithms using R.
Provides a practical introduction to machine learning using Python and popular libraries such as Scikit-Learn. It valuable resource for learners who want to gain hands-on experience with machine learning algorithms using Python.
Provides a practical introduction to machine learning for non-technical learners. It valuable resource for learners who want to gain a basic understanding of machine learning concepts and algorithms without getting bogged down in technical details.
Provides a non-technical introduction to machine learning for complete beginners. It valuable resource for learners who want to gain a basic understanding of machine learning concepts and algorithms without getting bogged down in technical details.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/2zccep/machine
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Via
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Institution
Sungkyunkwan University
Instructor
Jaekwang KIM
Language
English
Transcripts
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Good to know
Suitable for those just beginning to learn the fundamentals of machine learning
Provides a strong foundation for those interested in pursuing a career in machine learning
Taught by experienced instructors in the field, providing students with valuable insights
Requires basic knowledge in mathematics and programming in Python, making it accessible to learners with a technical foundation
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