MP4 | Video: h264, 1280x720 | Audio: AAC, 44100 Hz
Language: English | Size: 371 MB | Duration: 1h 8m

What you'll learn
K Nearest Neighbours Algorithm
Machine Learning
Data Science
Requirements
Python programming
Basics of machine learning
Zeal to learn
Not be able to eat 1 Pizza
Description
Hi , and welcome to the K Nearest Neighbours Course

Are you someone who is new to machine learning ?

Are you someone who wants to get started with machine learning ?

Can you sacrifice 1 McDonalds Meal for this amazing course ?

YES?

Then this course is for you -->

Machine learning which is a buzz word has been in the market for quite some time now . Suppose you are a pizza ? delivery guy , who has 3 stores A , B and C , and want to know which store to visit , or which store is closest to you . This is where KNN , comes in . Imagine you are a dot in the middle and A,B,C are stores . So KNN will help you in determining the closest distance .

Let us look at what you'll be requiring throught the course -

Materials Required -

Mac or Windows

At least 4 gb ram

Good coding skills

Python language

Zeal to learn

In this course you'll learn -

What is KNN

Machine learning

Visualising data

Splitting the dataset

How to apply KNN

Cosine Similarity

Confusion matrix

Work with really cool datasets and build real time projects

I believe in the concept of "Learn by doing " and this is emphasised in my class , I myself learn by doing things instead of listening to boring lectures !

You'll be able to use real time datasets after this class and learn all the necessary components required for getting started with machine learning

Will it be challenging ? YES

Will you get difficulty in understanding things ? YES (if you are a beginner)

But that is what my course is for , it will help you make an app in quick time and you will surely learn many things going forward !

Good luck !

Who this course is for:
Students who want to learn the first basic machine learning model
Python programmers who want to get into machine learning

Screenshots



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