Machine Learning: KNeighborsClassifier and Math Behind It

Machine Learning: KNeighborsClassifier and Math Behind It, available at Free, has an average rating of 4.6, with 13 lectures, based on 5 reviews, and has 587 subscribers.

You will learn about Understand the fundamentals of machine learning and its applications. Gain an in-depth understanding of the K Nearest Neighbors (KNN) algorithm. Learn the mathematical concepts behind KNN, including distance metrics and the k-nearest neighbors approach. Explore the Iris flower dataset and understand its structure and features. Implement the KNN algorithm using scikit-learn's KNeighborsClassifier. Split a dataset into training and testing sets for model evaluation. Perform hyperparameter tuning using GridSearchCV to find the best combination of hyperparameters for the KNN model. Evaluate the performance of the KNN model using accuracy metrics such as accuracy score and classification report. Visualize the classification report to gain insights into the model's performance for each class. Understand the concept of feature importance and its relevance in machine learning models. This course is ideal for individuals who are Beginner data scientists or machine learning enthusiasts who want to learn the fundamentals of machine learning algorithms. or Students or professionals who want to understand the mathematical concepts behind the K Nearest Neighbors (KNN) algorithm. or Individuals familiar with basic machine learning concepts and want to expand their knowledge by exploring one of the popular classification algorithms, KNN. or Programmers or developers who want to incorporate KNN into their machine learning projects or applications. It is particularly useful for Beginner data scientists or machine learning enthusiasts who want to learn the fundamentals of machine learning algorithms. or Students or professionals who want to understand the mathematical concepts behind the K Nearest Neighbors (KNN) algorithm. or Individuals familiar with basic machine learning concepts and want to expand their knowledge by exploring one of the popular classification algorithms, KNN. or Programmers or developers who want to incorporate KNN into their machine learning projects or applications.

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Summary

Title: Machine Learning: KNeighborsClassifier and Math Behind It

Price: Free

Average Rating: 4.6

Number of Lectures: 13

Number of Published Lectures: 13

Number of Curriculum Items: 13

Number of Published Curriculum Objects: 13

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

• Understand the fundamentals of machine learning and its applications.
• Gain an in-depth understanding of the K Nearest Neighbors (KNN) algorithm.
• Learn the mathematical concepts behind KNN, including distance metrics and the k-nearest neighbors approach.
• Explore the Iris flower dataset and understand its structure and features.
• Implement the KNN algorithm using scikit-learn's KNeighborsClassifier.
• Split a dataset into training and testing sets for model evaluation.
• Perform hyperparameter tuning using GridSearchCV to find the best combination of hyperparameters for the KNN model.
• Evaluate the performance of the KNN model using accuracy metrics such as accuracy score and classification report.
• Visualize the classification report to gain insights into the model's performance for each class.
• Understand the concept of feature importance and its relevance in machine learning models.

Who Should Attend

• Beginner data scientists or machine learning enthusiasts who want to learn the fundamentals of machine learning algorithms.
• Students or professionals who want to understand the mathematical concepts behind the K Nearest Neighbors (KNN) algorithm.
• Individuals familiar with basic machine learning concepts and want to expand their knowledge by exploring one of the popular classification algorithms, KNN.
• Programmers or developers who want to incorporate KNN into their machine learning projects or applications.

Target Audiences

• Beginner data scientists or machine learning enthusiasts who want to learn the fundamentals of machine learning algorithms.
• Students or professionals who want to understand the mathematical concepts behind the K Nearest Neighbors (KNN) algorithm.
• Individuals familiar with basic machine learning concepts and want to expand their knowledge by exploring one of the popular classification algorithms, KNN.
• Programmers or developers who want to incorporate KNN into their machine learning projects or applications.