K-Nearest Neighbors (KNN) Algorithm in Python and R
IntermediateLevel
2537+Students Enrolled
30 MinsDuration
4.8Average Rating
About this Course
- Master the K-Nearest Neighbors algorithm by building real-world projects in Python and R. Understand distance metrics, model evaluation, and parameter tuning.
- Learn how KNN classifies data points based on proximity, explore hyperparameter tuning techniques, and optimize models for better accuracy.
- Gain hands-on experience with KNN for classification and regression tasks. Visualize decision boundaries, handle high-dimensional data, and evaluate model performance.
Learning Outcomes
Understanding KNN
Learn how KNN works, distance metrics, and decision rules.
Implementing KNN
Build and implement KNN models from scratch in Python and R.
Model Optimization
Explore hyperparameter tuning and cross-validation.
Real-World Applications
Solve practical problems with hands-on KNN projects.
Who Should Enroll
- Aspiring Data Scientists: Learn KNN fundamentals and build models in Python and R with hands-on practice.
- Machine Learning Enthusiasts: Explore practical KNN applications and optimize models for real-world data.
- Analysts & Researchers: Gain skills to implement, evaluate, and improve KNN models for data-driven insights.
Course Curriculum
Master KNN with hands-on examples in Python and R. Learn algorithm basics, distance metrics, choosing the right K value, handling imbalanced data, performance evaluation, and real-world applications. Build, train, and optimize models step by step!
1. What is KNN?
2. Applications of KNN
1. Steps to build a KNN model
2. Determining right value of k
3. How to Calculate distance?
1. Implementation of KNN in Pyhton
2. Implementation of KNN in R
Get this Course Now
With this course you’ll get
- 30 Mins
Duration
- Kunal Jain
Instructor
- Intermediate
Level
Certificate of completion
Earn a professional certificate upon course completion
- Globally recognized certificate
- Verifiable online credential
- Enhances professional credibility
Frequently Asked Questions
Looking for answers to other questions?
This course is designed for anyone who wants to understand what the KNN algorithm is and how it works in machine learning. This is an important concept in machine learning that’s widely used in the industry. All the more reason to start learning today!
You should ideally have a basic grasp on machine learning algorithms and know the difference between regression and classification. We suggest enrolling in the Introduction to Data Science course for starters.
This course is free of cost!
Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost.
You can complete the “K-Nearest Neighbor (KNN) Algorithm in Python and R” course in a few hours.
The next step in your journey is to build on what you've learned so far. We recommend taking the popular “Applied Machine Learning” course.
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