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Simple Nearest Neighbors Regression and Classification
Charles Ivan Niswander II
Charles Ivan Niswander II
In this 2-hour long project-based course, we will explore the basic principles behind the K-Nearest Neighbors algorithm, as well as learn how to implement KNN for decision making in Python.
Read more
In this 2-hour long project-based course, we will explore the basic principles behind the K-Nearest Neighbors algorithm, as well as learn how to implement KNN for decision making in Python.
Read more
In this 2-hour long project-based course, we will explore the basic principles behind the K-Nearest Neighbors algorithm, as well as learn how to implement KNN for decision making in Python.
A simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems is the k-nearest neighbors (KNN) algorithm. The fundamental principle is that you enter a known data set, add an unknown data point, and the algorithm will tell you which class corresponds to that unknown data point. The unknown is characterized by a straightforward neighborly vote, where the "winner" class is the class of near neighbors. It is most commonly used for predictive decision-making. For instance,:
Is a consumer going to default on a loan or not?
Will the company make a profit?
Should we extend into a certain sector of the market?
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/syuivp/simple
What's inside
Syllabus
Simple Nearest Neighbors Regression and Classification
In this 2-hour long project-based course, we will explore the basic principles behind the K-Nearest Neighbors algorithm, as well as learn how to implement KNN for decision making. A simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems, the k-nearest neighbors (KNN) algorithm can be used for a variety of prediction problems. The fundamental principle is that you enter a known data set, add an unknown data point, and the algorithm will tell you which class corresponds to that unknown data point. The unknown is characterized by a straightforward neighborly vote, where the "winner" class is the class of near neighbors. It is most commonly used for predictive decision-making.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Offers an in-depth look at K-Nearest Neighbors, its principles, and its application in decision-making
Taught by Charles Ivan Niswander II, an expert in the field
Relevant to both classification and regression problems, making it widely applicable
Focuses on implementation in Python, a popular and industry-standard language
Suitable for beginners with a basic understanding of machine learning concepts
May require additional resources or knowledge for those with limited programming experience
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Reviews summary
Mostly negative reviews
According to students, this course can be disorganized. Students especially dislike that the instructor copies and pastes code instead of demonstrating it in a Jupyter notebook.
The instructor does not provide demonstrations and simply copies and pastes examples.
"The instuctor did not use a Jupyter notebook and simply copied and pasted codes"
The course material is presented in a disorganized way.
"The instruction was disorganized"
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 Simple Nearest Neighbors Regression and Classification with these
activities:
Review pre-requisite knowledge
Show steps
Prepare for this course by reviewing the basic principles of supervised machine learning algorithms, including regression and classification.
Browse courses on
K-Nearest Neighbors
Show steps
-
Review your notes or textbooks on supervised machine learning.
-
Complete practice problems and exercises on regression and classification algorithms.
Identify a mentor who can guide you in K-NN and machine learning
Show steps
Seek guidance and support from an experienced professional in the field of K-NN and machine learning to enhance your learning journey.
Browse courses on
Mentoring
Show steps
-
Attend industry events or online forums to connect with potential mentors.
-
Reach out to individuals who have expertise in K-NN or machine learning and express your interest in mentorship.
-
Prepare a list of questions and topics you would like to discuss with your mentor.
Explore additional resources on K-NN
Show steps
Enhance your understanding of K-NN by exploring additional resources, such as online tutorials, articles, or books.
Browse courses on
K-Nearest Neighbors
Show steps
-
Search for online tutorials or articles on K-NN.
-
Identify books or research papers that provide in-depth coverage of K-NN.
-
Review the selected resources to gain additional insights and perspectives on K-NN.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Implement k-NN in your favorite programming language
Show steps
Solidify your understanding of the K-Nearest Neighbors algorithm by implementing it in a programming language of your choice.
Browse courses on
K-Nearest Neighbors
Show steps
-
Choose a programming language and familiarize yourself with its libraries for data analysis and machine learning.
-
Implement the k-NN algorithm from scratch, including distance calculations, neighbor selection, and class prediction.
-
Test your implementation on a simple dataset and evaluate its performance.
Attend a data science or machine learning meetup
Show steps
Connect with professionals in the field of data science or machine learning to exchange knowledge and learn about real-world applications of K-NN.
Browse courses on
Networking
Show steps
-
Find a data science or machine learning meetup in your area.
-
Attend the meetup and introduce yourself to other attendees.
-
Participate in discussions and ask questions about K-NN and its applications.
-
Follow up with any interesting contacts you meet.
Gather resources on K-NN applications
Show steps
Expand your knowledge of K-NN by compiling a collection of resources that showcase its applications in various domains.
Browse courses on
K-Nearest Neighbors
Show steps
-
Search for articles, case studies, or blog posts that demonstrate the use of K-NN in different industries or applications.
-
Organize the resources into categories, such as healthcare, finance, or marketing.
-
Create a document or presentation that summarizes the key insights and findings from the resources.
Create a decision tree using K-NN
Show steps
Extend your understanding of K-NN by applying it to create a decision tree for a classification or regression problem.
Browse courses on
Decision Trees
Show steps
-
Gather data for your decision tree.
-
Use k-NN to determine the best split at each node of the tree.
-
Visualize your decision tree using a data visualization library.
-
Evaluate the performance of your decision tree on unseen data.
-
Write a report summarizing your findings and insights.
Contribute to an open-source K-NN library or project
Show steps
Deepen your understanding of K-NN and contribute to the community by making contributions to an open-source K-NN library or project.
Browse courses on
Open Source
Show steps
-
Identify an open-source K-NN library or project that interests you.
-
Read the project documentation and familiarize yourself with its codebase.
-
Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
-
Make your contributions to the project and submit a pull request.
-
Work with the project maintainers to get your contributions merged.
Review pre-requisite knowledge
Show steps
Prepare for this course by reviewing the basic principles of supervised machine learning algorithms, including regression and classification.
Browse courses on
K-Nearest Neighbors
Show steps
Show steps
Show steps
- Review your notes or textbooks on supervised machine learning.
- Complete practice problems and exercises on regression and classification algorithms.
Identify a mentor who can guide you in K-NN and machine learning
Show steps
Seek guidance and support from an experienced professional in the field of K-NN and machine learning to enhance your learning journey.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Attend industry events or online forums to connect with potential mentors.
- Reach out to individuals who have expertise in K-NN or machine learning and express your interest in mentorship.
- Prepare a list of questions and topics you would like to discuss with your mentor.
Explore additional resources on K-NN
Show steps
Enhance your understanding of K-NN by exploring additional resources, such as online tutorials, articles, or books.
Browse courses on
K-Nearest Neighbors
Show steps
Show steps
Show steps
- Search for online tutorials or articles on K-NN.
- Identify books or research papers that provide in-depth coverage of K-NN.
- Review the selected resources to gain additional insights and perspectives on K-NN.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Implement k-NN in your favorite programming language
Show steps
Solidify your understanding of the K-Nearest Neighbors algorithm by implementing it in a programming language of your choice.
Browse courses on
K-Nearest Neighbors
Show steps
Show steps
Show steps
- Choose a programming language and familiarize yourself with its libraries for data analysis and machine learning.
- Implement the k-NN algorithm from scratch, including distance calculations, neighbor selection, and class prediction.
- Test your implementation on a simple dataset and evaluate its performance.
Attend a data science or machine learning meetup
Show steps
Connect with professionals in the field of data science or machine learning to exchange knowledge and learn about real-world applications of K-NN.
Browse courses on
Networking
Show steps
Show steps
Show steps
- Find a data science or machine learning meetup in your area.
- Attend the meetup and introduce yourself to other attendees.
- Participate in discussions and ask questions about K-NN and its applications.
- Follow up with any interesting contacts you meet.
Gather resources on K-NN applications
Show steps
Expand your knowledge of K-NN by compiling a collection of resources that showcase its applications in various domains.
Browse courses on
K-Nearest Neighbors
Show steps
Show steps
Show steps
- Search for articles, case studies, or blog posts that demonstrate the use of K-NN in different industries or applications.
- Organize the resources into categories, such as healthcare, finance, or marketing.
- Create a document or presentation that summarizes the key insights and findings from the resources.
Create a decision tree using K-NN
Show steps
Extend your understanding of K-NN by applying it to create a decision tree for a classification or regression problem.
Browse courses on
Decision Trees
Show steps
Show steps
Show steps
- Gather data for your decision tree.
- Use k-NN to determine the best split at each node of the tree.
- Visualize your decision tree using a data visualization library.
- Evaluate the performance of your decision tree on unseen data.
- Write a report summarizing your findings and insights.
Contribute to an open-source K-NN library or project
Show steps
Deepen your understanding of K-NN and contribute to the community by making contributions to an open-source K-NN library or project.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify an open-source K-NN library or project that interests you.
- Read the project documentation and familiarize yourself with its codebase.
- Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
- Make your contributions to the project and submit a pull request.
- Work with the project maintainers to get your contributions merged.
Career center
Learners who complete Simple Nearest Neighbors Regression and Classification will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for developing and implementing machine learning models to solve business problems. This course can help Machine Learning Engineers build a foundation in the K-Nearest Neighbors algorithm, which is a simple but effective machine learning algorithm that can be used for both classification and regression problems. The course will also teach Machine Learning Engineers how to implement KNN in Python, which is a popular programming language for machine learning.
Data Analyst
Data Analyst
Data Analysts use data to solve business problems. This course can help Data Analysts learn about the K-Nearest Neighbors algorithm, which is a simple but effective machine learning algorithm that can be used for both classification and regression problems. The course will also teach Data Analysts how to implement KNN in Python, which is a popular programming language for data analysis.
Data Scientist
Data Scientist
Data Scientists use data to solve complex business problems. This course can help Data Scientists learn about the K-Nearest Neighbors algorithm, which is a simple but effective machine learning algorithm that can be used for both classification and regression problems. The course will also teach Data Scientists how to implement KNN in Python, which is a popular programming language for data science.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematics and statistics to solve financial problems. This course can help Quantitative Analysts learn about the K-Nearest Neighbors algorithm, which is a simple but effective machine learning algorithm that can be used for both classification and regression problems. The course will also teach Quantitative Analysts how to implement KNN in Python, which is a popular programming language for quantitative finance.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make better decisions. This course can help Business Analysts learn about the K-Nearest Neighbors algorithm, which is a simple but effective machine learning algorithm that can be used for both classification and regression problems. The course will also teach Business Analysts how to implement KNN in Python, which is a popular programming language for business analysis.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course can help Software Engineers learn about the K-Nearest Neighbors algorithm, which is a simple but effective machine learning algorithm that can be used for both classification and regression problems. The course will also teach Software Engineers how to implement KNN in Python, which is a popular programming language for software engineering.
Statistician
Statistician
Statisticians use data to solve problems in a variety of fields, including science, engineering, and business. This course can help Statisticians learn about the K-Nearest Neighbors algorithm, which is a simple but effective machine learning algorithm that can be used for both classification and regression problems. The course will also teach Statisticians how to implement KNN in Python, which is a popular programming language for statistics.
Market Researcher
Market Researcher
Market Researchers use data to understand consumer behavior. This course can help Market Researchers learn about the K-Nearest Neighbors algorithm, which is a simple but effective machine learning algorithm that can be used for both classification and regression problems. The course will also teach Market Researchers how to implement KNN in Python, which is a popular programming language for market research.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematics and statistics to solve problems in a variety of fields, including business, engineering, and healthcare. This course can help Operations Research Analysts learn about the K-Nearest Neighbors algorithm, which is a simple but effective machine learning algorithm that can be used for both classification and regression problems. The course will also teach Operations Research Analysts how to implement KNN in Python, which is a popular programming language for operations research.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. This course can help Financial Analysts learn about the K-Nearest Neighbors algorithm, which is a simple but effective machine learning algorithm that can be used for both classification and regression problems. The course will also teach Financial Analysts how to implement KNN in Python, which is a popular programming language for financial analysis.
Risk Analyst
Risk Analyst
Risk Analysts use data to assess risk and uncertainty. This course can help Risk Analysts learn about the K-Nearest Neighbors algorithm, which is a simple but effective machine learning algorithm that can be used for both classification and regression problems. The course will also teach Risk Analysts how to implement KNN in Python, which is a popular programming language for risk analysis.
Actuary
Actuary
Actuaries use mathematics and statistics to assess risk and uncertainty. This course can help Actuaries learn about the K-Nearest Neighbors algorithm, which is a simple but effective machine learning algorithm that can be used for both classification and regression problems. The course will also teach Actuaries how to implement KNN in Python, which is a popular programming language for actuarial science.
Computer Scientist
Computer Scientist
Computer Scientists design, develop, and analyze computer systems. This course can help Computer Scientists learn about the K-Nearest Neighbors algorithm, which is a simple but effective machine learning algorithm that can be used for both classification and regression problems. The course will also teach Computer Scientists how to implement KNN in Python, which is a popular programming language for computer science.
Software Developer
Software Developer
Software Developers design, develop, and maintain software applications. This course can help Software Developers learn about the K-Nearest Neighbors algorithm, which is a simple but effective machine learning algorithm that can be used for both classification and regression problems. The course will also teach Software Developers how to implement KNN in Python, which is a popular programming language for software development.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data systems. This course can help Data Engineers learn about the K-Nearest Neighbors algorithm, which is a simple but effective machine learning algorithm that can be used for both classification and regression problems. The course will also teach Data Engineers how to implement KNN in Python, which is a popular programming language for data engineering.
For more career information including salaries, visit:
OpenCourser.com/course/syuivp/simple
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
Simple Nearest Neighbors Regression and Classification .
Practical guide to machine learning using Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics, including data preprocessing, model selection, and hyperparameter tuning.
Provides a comprehensive overview of machine learning using Python. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, model evaluation, and deep learning.
Provides a comprehensive overview of machine learning. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation.
Provides a rigorous and mathematical treatment of machine learning. It covers a wide range of topics, including probability theory, Bayesian inference, and graphical models.
Save
Provides a comprehensive overview of deep learning. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks.
Provides a comprehensive overview of pattern recognition and machine learning. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation.
Save
Provides a comprehensive overview of machine learning algorithms. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation.
Provides a practical guide to machine learning using R. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation.
Provides a practical guide to machine learning using R. It covers a wide range of topics, including data preprocessing, model selection, and hyperparameter tuning.
Provides a practical guide to machine learning using Python. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation.
Provides a practical guide to machine learning using Python. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation.
Provides a gentle introduction to machine learning using Python. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation.
Provides a gentle introduction to machine learning. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation.
Provides a very gentle introduction to machine learning. It covers a wide range of topics, including supervised and unsupervised learning, feature engineering, and model evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/syuivp/simple
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Charles Ivan Niswander II
Language
English
Good to know
Offers an in-depth look at K-Nearest Neighbors, its principles, and its application in decision-making
Taught by Charles Ivan Niswander II, an expert in the field
Relevant to both classification and regression problems, making it widely applicable
Focuses on implementation in Python, a popular and industry-standard language
Suitable for beginners with a basic understanding of machine learning concepts
May require additional resources or knowledge for those with limited programming experience
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