Master the K Nearest Neighbors (KNN) Algorithm and Uncover the Mathematical Foundations of Machine Learning

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.


In this comprehensive Udemy course, you will dive into the fascinating world of machine learning and master the K Nearest Neighbors (KNN) classifier algorithm.

Machine learning has revolutionized numerous industries, from healthcare to finance, by enabling computers to learn patterns and make intelligent predictions. KNN, one of the fundamental algorithms in the field, is widely used for classification tasks.

This course is designed to provide you with a solid foundation in both the practical implementation of KNN using Python and the underlying mathematical concepts behind it. Whether you’re a beginner or an experienced programmer looking to expand your machine learning skills, this course will equip you with the knowledge and tools needed to excel.

Throughout the course, you will:

1. Understand the principles and theory behind the KNN algorithm, including its assumptions and limitations.

2. Learn how to preprocess and explore datasets, preparing them for KNN classification.

3. Master the implementation of KNN using Python’s scikit-learn library, leveraging its powerful tools for data manipulation, model training, and evaluation.

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4. Discover the importance of hyperparameter tuning and how to optimize KNN models using GridSearchCV and cross-validation techniques.

5. Gain hands-on experience by working on a real-world project: classifying the famous Iris flower dataset.

6. Visualize and interpret the results of your KNN models using classification reports and other insightful graphical representations.

7. Explore the math behind KNN, including distance metrics, decision boundaries, and the concept of k-nearest neighbors.

8. Grasp the intuition behind feature importance and why it is crucial for certain machine learning algorithms (excluding KNN).

By the end of this course, you will have a deep understanding of the K Nearest Neighbors algorithm, its application in classification tasks, and the mathematical principles that underpin its computations. Armed with this knowledge, you will be ready to tackle real-world machine learning problems and make informed decisions about when and how to use KNN effectively.

Enroll now and embark on your journey into the world of machine learning with KNeighborsClassifier and the math behind it. Let’s unlock the potential of data and make accurate predictions together!




Installing Jupyter
How to download Python files

Course Contents

1 Importing Libraries
2 load the Iris dataset
3 split data into training and testing sets
4 define the hyperparameter grid
5 n_neighbors explained
6 weights explained
7 Manhattan distance and Euclidean distance explained
8 perform a grid search with cross-validation
9 make predictions on the test set
10 classification_report
11 Understand DataFrame and generate a heatmap