Machine Learning with K-Nearest Neighbors

Name: Machine Learning with K-Nearest Neighbors Course
Price: 36 USD

with Hristina Hristova

4.8/5

(626)

Master K-Nearest Neighbors using Python’s scikit-learn library: from theoretical foundations to practical applications

1 hour of content 3814 students

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What you get:

1 hour of content
3 Interactive exercises
8 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

Machine Learning with K-Nearest Neighbors

A course by Hristina Hristova

Start for Free

What you get:

1 hour of content
3 Interactive exercises
8 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

$99.00

Lifetime access

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Start for Free

What you get:

1 hour of content
3 Interactive exercises
8 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

What You Learn

Fully grasp the inner workings of the KNN algorithm as well as its practical application
Get acquainted with different distance metrics
Learn how to generate random datasets for practice
Understand the pros and cons of the KNN algorithm to make informed decisions in model selection
Develop the skills to independently plan, execute, and deliver a complete ML project from start to finish

Top Choice of Leading Companies Worldwide

Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

Course Description

Practical knowledge of various machine learning algorithms is essential for machine learning enthusiasts and experts alike. In this course, we focus extensively on one of the most intuitive and easy-to-implement ML algorithms out there – K-nearest neighbors, or KNN for short. Step by step, we will first lay the foundations and expand your mathematical toolbox. Then, you will progress to coding and using Python’s scikit-learn library to solve a randomly generated classification problem. Finally, you will apply KNN to a couple of regression tasks. In other words – you will learn all the subtleties that should be considered when applying the KNN algorithm in your future practice.

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1.1 What does the course cover?

7 min

1.2 Motivation

2 min

1.4 Math Prerequisites: Distance Metrics

4 min

2.1 Setting up the Environment

1 min

2.2 Installing the Relevant Packages

4 min

3.1 Random Dataset: Generating the Dataset

3 min

Curriculum

1. KNN Classifier – Theory

3 Lessons 13 Min

In this introductory section, we motivate the usage of a K-Nearest Neighbor classifier and give 2 intuitive examples. Following that is a short math refresher where we talk about the various ways in which the distance between 2 points in space can be defined. This would later serve us well when building our KNN model in Python.

What does the course cover?
7 min
Motivation
2 min
Math Prerequisites: Distance Metrics
4 min
2. Setting up the Environment

2 Lessons 5 Min

In advance of the hands-on part of the course, this section guides you through the installation process of relevant Python packages.

Setting up the Environment Read now
1 min
Installing the Relevant Packages
4 min
3. KNN Classifier – Practical Example

8 Lessons 37 Min

To apply your skills in practice, you will first learn how to generate a random set of points, distribute them into 3 classes, and place them on the coordinate system. We will then use this dataset to train and test a KNN classification algorithm with the help of Python’s scikit-learn library. We will look at some edge cases that can arise during the classification process and discover how to handle them. Next, we will guide you through the process of building the so-called decision regions, which are a great way of visualizing the performance of your model. Finally, we will find out how to choose the best model parameters using a technique called ‘grid search’.

Random Dataset: Generating the Dataset
3 min
Random Dataset: Visualizing the Dataset
4 min
Random Dataset: Classification
7 min
Random Dataset: How to Break a Tie
3 min
Random Dataset: Decision Regions
6 min
Random Dataset: Choosing the Best K-value
5 min
Random Dataset: Grid Search
5 min
Random Dataset: Model Performance
4 min
4. KNN Regressor

3 Lessons 19 Min

Continuing the practical part of the course, we will dive into solving regression tasks using the K-Nearest Neighbors method. Similar to what we did in the previous section, we will tackle this problem by generating 2 random datasets. One would represent a linear problem, while the other would be non-linear. We will apply a linear (parametric) model and a KNN (non-parametric model) on both datasets and argue which one performs better.

Theory with a Practical Example
8 min
KNN vs Linear Regression: A Linear Problem
7 min
KNN vs Linear Regression: A Non-linear Problem
4 min
5. Pros and Cons of the KNN Algorithm

1 Lesson 7 Min

In this final section of the course, the pros and cons of the KNN algorithm are discussed at length. We will study this method’s limitations, together with its strong sides.

Pros and Cons
7 min

Topics

TheoryPythonMachine LearningProgrammingMachine and Deep Learning

Tools & Technologies

Course Requirements

You need to complete an introduction to Python before taking this course
It is highly recommended to take the Machine Learning in Python course first
You need to have Jupyter Notebook up and running

Who Should Take This Course?

Level of difficulty: Intermediate

Aspiring data scientists and ML engineers

Exams and Certification

A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Meet Your Instructor

Hristina Hristova

Head of Data Content at

6 Courses

1674 Reviews

14243 Students

Hristina Hristova is a Theoretical Physicist with experience in the fields of mathematics, physics, programming, and the creation of various educational content. For several years now, she has been tutoring physics and mathematics students online, following educational programs such as The IB Diploma, Cambridge IGCSE, and Cambridge AS & A Level, among many others. Hristina’s high qualification and adaptive teaching style have helped plenty of students successfully pass their exams, while also enjoying the learning process.