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K Nearest Neighbor V1.0 Supervised Machine Learning Algorithm

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© DataMites™. All Rights Reserved | www.datamites.com K-Nearest Neighbors Supervised Machine Learning Accredited with IABAC™ ( International Association of Business Analytics Certifications)` STATISTICS FOR DATA SCIENCE
© DataMites™. All Rights Reserved | www.datamites.com Course Topics  What is K-Nearest Neighbors ?  How K-Nearest Neighbors works?  K-Nearest Neighbors Assumptions  Pros and Cons of K-Nearest Neighbors .  Applications of K-Nearest Neighbors .  Modeling K-Nearest Neighbors with Python STATISTICS FOR DATA SCIENCE
© DataMites™. All Rights Reserved | www.datamites.com What is K-Nearest Neighbors algorithm? K-Nearest Neighbors (KNN) is one of the fundamental algorithms for classification in Machine Learning. KNN need labeled data to learn and, so belongs to the supervised learning category. KNN is widely disposable in real-life scenarios since it is non- parametric, meaning, it does not make any underlying assumptions about the distribution of data. STATISTICS FOR DATA SCIENCE
© DataMites™. All Rights Reserved | www.datamites.com How K-Nearest Neighbors Works? • KNN works based on minimum distance from the query instance to the training samples to determine the K-nearest neighbors. STATISTICS FOR DATA SCIENCE
© DataMites™. All Rights Reserved | www.datamites.com 1-Nearest Neighbor x STATISTICS FOR DATA SCIENCE
© DataMites™. All Rights Reserved | www.datamites.com 2-Nearest Neighbor ? STATISTICS FOR DATA SCIENCE
© DataMites™. All Rights Reserved | www.datamites.com 3-Nearest Neighbor X STATISTICS FOR DATA SCIENCE
© DataMites™. All Rights Reserved | www.datamites.com 8-Nearest Neighbor X STATISTICS FOR DATA SCIENCE
© DataMites™. All Rights Reserved | www.datamites.com Pros of K-Nearest Neighbors • Simple to implement • Flexible to feature / distance choices • Naturally handles multi-class cases • Can do well in practice with enough representative data STATISTICS FOR DATA SCIENCE
© DataMites™. All Rights Reserved | www.datamites.com Cons of K-Nearest Neighbor • Need to determine value of parameter K (number of nearest neighbors) • Computation cost is quite high because we need to compute distance of each query instance to all training samples. • Storage of data • Must know we have a meaningful distance function STATISTICS FOR DATA SCIENCE
© DataMites™. All Rights Reserved | www.datamites.com Popular Applications of KNN  Concept Search : Searching for semantically similar documents (i.e., documents containing similar topics)  Recommender Systems : If a customer likes a particular item, then you can recommend similar items for them. Also in Ads display etc.,  Facial recognition, Finger print detection STATISTICS FOR DATA SCIENCE
© DataMites™. All Rights Reserved | www.datamites.com Controlling COMPLEXITY in k-NN STATISTICS FOR DATA SCIENCE
© DataMites™. All Rights Reserved | www.datamites.com DataMites™ is a global institute of Data Science, Machine Learning, IoT and Artificial Intelligence Training and Consulting for individuals and Corporate. For courses enquires Call : +4420 8089 9220 (UK) (USA) | 1800 313 3434 (India Toll Free) Email : enquiry@datamites.com | Corporate Clients: corp@datamites.com If you are looking for Data Science Training Course in Bangalore please visit: https://datamites.com/data-science-course-training-bangalore/ DataMites

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K Nearest Neighbor V1.0 Supervised Machine Learning Algorithm

  • 1. © DataMites™. All Rights Reserved | www.datamites.com K-Nearest Neighbors Supervised Machine Learning Accredited with IABAC™ ( International Association of Business Analytics Certifications)` STATISTICS FOR DATA SCIENCE
  • 2. © DataMites™. All Rights Reserved | www.datamites.com Course Topics  What is K-Nearest Neighbors ?  How K-Nearest Neighbors works?  K-Nearest Neighbors Assumptions  Pros and Cons of K-Nearest Neighbors .  Applications of K-Nearest Neighbors .  Modeling K-Nearest Neighbors with Python STATISTICS FOR DATA SCIENCE
  • 3. © DataMites™. All Rights Reserved | www.datamites.com What is K-Nearest Neighbors algorithm? K-Nearest Neighbors (KNN) is one of the fundamental algorithms for classification in Machine Learning. KNN need labeled data to learn and, so belongs to the supervised learning category. KNN is widely disposable in real-life scenarios since it is non- parametric, meaning, it does not make any underlying assumptions about the distribution of data. STATISTICS FOR DATA SCIENCE
  • 4. © DataMites™. All Rights Reserved | www.datamites.com How K-Nearest Neighbors Works? • KNN works based on minimum distance from the query instance to the training samples to determine the K-nearest neighbors. STATISTICS FOR DATA SCIENCE
  • 5. © DataMites™. All Rights Reserved | www.datamites.com 1-Nearest Neighbor x STATISTICS FOR DATA SCIENCE
  • 6. © DataMites™. All Rights Reserved | www.datamites.com 2-Nearest Neighbor ? STATISTICS FOR DATA SCIENCE
  • 7. © DataMites™. All Rights Reserved | www.datamites.com 3-Nearest Neighbor X STATISTICS FOR DATA SCIENCE
  • 8. © DataMites™. All Rights Reserved | www.datamites.com 8-Nearest Neighbor X STATISTICS FOR DATA SCIENCE
  • 9. © DataMites™. All Rights Reserved | www.datamites.com Pros of K-Nearest Neighbors • Simple to implement • Flexible to feature / distance choices • Naturally handles multi-class cases • Can do well in practice with enough representative data STATISTICS FOR DATA SCIENCE
  • 10. © DataMites™. All Rights Reserved | www.datamites.com Cons of K-Nearest Neighbor • Need to determine value of parameter K (number of nearest neighbors) • Computation cost is quite high because we need to compute distance of each query instance to all training samples. • Storage of data • Must know we have a meaningful distance function STATISTICS FOR DATA SCIENCE
  • 11. © DataMites™. All Rights Reserved | www.datamites.com Popular Applications of KNN  Concept Search : Searching for semantically similar documents (i.e., documents containing similar topics)  Recommender Systems : If a customer likes a particular item, then you can recommend similar items for them. Also in Ads display etc.,  Facial recognition, Finger print detection STATISTICS FOR DATA SCIENCE
  • 12. © DataMites™. All Rights Reserved | www.datamites.com Controlling COMPLEXITY in k-NN STATISTICS FOR DATA SCIENCE
  • 13. © DataMites™. All Rights Reserved | www.datamites.com DataMites™ is a global institute of Data Science, Machine Learning, IoT and Artificial Intelligence Training and Consulting for individuals and Corporate. For courses enquires Call : +4420 8089 9220 (UK) (USA) | 1800 313 3434 (India Toll Free) Email : enquiry@datamites.com | Corporate Clients: corp@datamites.com If you are looking for Data Science Training Course in Bangalore please visit: https://datamites.com/data-science-course-training-bangalore/ DataMites