K-Nearest Neighbors for Classification- Machine Learning

K-Nearest Neighbors for Classification: Machine Learning, available at $34.99, has an average rating of 4.83, with 8 lectures, based on 3 reviews, and has 601 subscribers.

You will learn about 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 This course is ideal for individuals who are Any people who want to start learning K-Nearest Neighbors in Data Science or Anyone interested in Machine Learning or Anyone who want to understand how to use K-Nearest Neighbors in datasets using Python It is particularly useful for Any people who want to start learning K-Nearest Neighbors in Data Science or Anyone interested in Machine Learning or Anyone who want to understand how to use K-Nearest Neighbors in datasets using Python.

Enroll now: K-Nearest Neighbors for Classification: Machine Learning

Summary

Title: K-Nearest Neighbors for Classification: Machine Learning

Price: $34.99

Average Rating: 4.83

Number of Lectures: 8

Number of Published Lectures: 8

Number of Curriculum Items: 8

Number of Published Curriculum Objects: 8

Original Price: $22.99

Quality Status: approved

Status: Live

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

Who Should Attend

Any people who want to start learning K-Nearest Neighbors in Data Science

Anyone interested in Machine Learning

Anyone who want to understand how to use K-Nearest Neighbors in datasets using Python

Target Audiences

Any people who want to start learning K-Nearest Neighbors in Data Science

Anyone interested in Machine Learning

Anyone who want to understand how to use K-Nearest Neighbors in datasets using Python

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

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 Classification 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 templateswhich you can download and use on your own projects.

Ready to get started, developer?

Enroll nowusing 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 Curriculum

Chapter 1: Course Introduction

Lecture 1: Introduction to K-Nearest Neighbors

Lecture 2: Introduction to Machine Learning

Chapter 2: Code Environment Setup

Lecture 1: Google Colab for Programming in Python

Chapter 3: K-Nearest Neighbors (KNN) – Classification Data Science Project

Lecture 1: Introduction to the Dataset

Lecture 2: Partition of the Dataset

Lecture 3: KNN – Preprocessing

Lecture 4: KNN – Training

Lecture 5: KNN – Performance Evaluation

Instructors

Lucas Bazilio
Engineer and Mathematician

Rating Distribution

1 stars: 0 votes

2 stars: 0 votes

3 stars: 0 votes

4 stars: 1 votes

5 stars: 2 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!