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Learn to apply KNN for Regression from a Data Science expert. Code templates included.

What you will learn

Master K-Nearest Neighbors in Python

Become an advanced, confident, and modern data scientist from scratch

Become job-ready by understanding how KNN really works behind the scenes

Apply robust Data Science techniques for the K-Nearest Neighbors algorithm

Solve Machine Learning Prediction Problems using KNN

How to think and work like a data scientist: problem-solving, researching, workflows

Get fast and friendly support in the Q&A area


You’ve just stumbled upon the most complete, in-depth KNN for Regression course online.

Whether you want to:

– build the skills you need to get your first data science job

– move to a more senior software developer position

– become a computer scientist mastering in data science

– or just learn KNN to be able to create your own projects quickly.

…this complete K-Nearest Neighbors for Regression Masterclass is the course you need to do all of this, and more.

This course is designed to give you the KNN skills you need to become a data science expert. By the end of the course, you will understand the K-Nearest Neighbors for Regression method extremely well and be able to apply them in your own data science projects and be productive as a computer scientist and developer.

What makes this course a bestseller?

Like you, thousands of others were frustrated and fed up with fragmented Youtube tutorials or incomplete or outdated courses which assume you already know a bunch of stuff, as well as thick, college-like textbooks able to send even the most caffeine-fuelled coder to sleep.

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Like you, they were tired of low-quality lessons, poorly explained topics, and confusing info presented in the wrong way. That’s why so many find success in this complete K-Nearest Neighbors for Regression course. It’s designed with simplicity and seamless progression in mind through its content.

This course assumes no previous data science experience and takes you from absolute beginner core concepts. You will learn the core dimensionality reduction skills and master the K-Nearest Neighbors technique. It’s a one-stop shop to learn Multilayer Networks. If you want to go beyond the core content you can do so at any time.

What if I have questions?

As if this course wasn’t complete enough, I offer full support, answering any questions you have.

This means you’ll never find yourself stuck on one lesson for days on end. With my hand-holding guidance, you’ll progress smoothly through this course without any major roadblocks.

Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.

And as an extra, this course includes Python code templates which you can download and use on your own projects.

Ready to get started, developer?

Enroll now using the “Add to Cart” button on the right, and get started on your way to creative, advanced Multilayer Networks brilliance. Or, take this course for a free spin using the preview feature, so you know you’re 100% certain this course is for you.

See you on the inside (hurry, K-Nearest Neighbors is waiting!)



Course Introduction

Introduction to K-Nearest Neighbors

Code Environment Setup

Google Colab for Programming in Python

K-Nearest Neighbors (KNN) – Regression Data Science Project

Introduction to the Dataset
Partition of the Dataset – Target Variable
Partition of the Dataset – Time Series Windows
KNN – Training
KNN – Performance Evaluation