Machine Learning: KNeighborsClassifier and Math Behind It
Master the K Nearest Neighbors (KNN) Algorithm and Uncover the Mathematical Foundations of Machine Learning
4.39 (9 reviews)
936
students
1.5 hours
content
Feb 2024
last update
FREE
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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.
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5797928
udemy ID
01/02/2024
course created date
16/02/2024
course indexed date
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