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Artificial Intelligence #3:kNN & Bayes Classification method (Udemy.com)

Classification methods for students and professionals. Learn k-Nearest Neighbors & Bayes Classification &code in python

3.3
16 reviews

Created by: Sobhan N.

Last updated December 2017

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What you will learn

  • Use k Nearest Neighbor classification method to classify datasets.
  • Learn main concept behind the k Nearest Neighbor classification method .
  • Write your own code to make k Nearest Neighbor classification method by yourself.
  • Use k Nearest Neighbor classification method to classify IRIS dataset.
  • Use Naive Bayes classification method to classify datasets.
  • Learn main concept behind Naive Bayes classification method.
  • Write your own code to make Naive Bayes classification method by yourself.
  • Use Naive Bayes classification method to classify Pima Indian Diabetes Dataset.
  • Use Naive Bayes classification method to obtain probability of being male or female based on Height, Weight and FootSize.

How it compares

Against the other 205 rated Udemy courses in Artificial Intelligence on CourseDuck:

  • Rating3.3 out of 5among the lower-rated ones
  • Reviews16#202 of 206 by number of reviews
  • Length2 hoursshorter than 77% of them
  • Last updatedDecember 2017earlier than 98% of them

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Course Description

In this Course you learn k-Nearest Neighbors & Naive Bayes Classification Methods.

In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression.
k-NN is a type of instance-based learning, or lazy learning, where the function is only approximated locally and all computation is deferred until classification. The k-NN algorithm is among the simplest of all machine learning algorithms.
For classification, a useful technique can be to assign weight to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones.
The neighbors are taken from a set of objects for which the class (for k-NN classification). This can be thought of as the training set for the algorithm, though no explicit training step is required.

In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features.
Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem. Maximum-likelihood training can be done by evaluating a closed-form expression, which takes linear time, rather than by expensive iterative approximation as used for many other types of classifiers.

In the statistics and computer science literature, Naive Bayes models are known under a variety of names, including simple Bayes and independence Bayes. All these names reference the use of Bayes' theorem in the classifier's decision rule, but naive Bayes is not (necessarily) a Bayesian method.
In this course you learn how to classify datasets by k-Nearest Neighbors Classification Method to find the correct class for data and reduce error. Then you go further You will learn how to classify output of model by using Naive Bayes Classification Method.
In the first section you learn how to use python to estimate output of your system. In this section you can classify:
  • Python Dataset
  • IRIS Flowers
  • Make your own k Nearest Neighbors Algorithm
In the Second section you learn how to use python to classify output of your system with nonlinear structure .In this section you can classify:
  • IRIS Flowers
  • Pima Indians Diabetes Database
  • Make your own Naive Bayes Algorithm


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Important information before you enroll:
  • In case you find the course useless for your career, don't forget you are covered by a 30 day money back guarantee, full refund, no questions asked!
  • Once enrolled, you have unlimited, lifetime access to the course!
  • You will have instant and free access to any updates I'll add to the course.
  • I will give you my full support regarding any issues or suggestions related to the course.
  • Check out the curriculum and FREE PREVIEW lectures for a quick insight.
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Best Regrads,
SobhanWho this course is for:
  • Anyone who wants to make the right choice when starting to learn kNN & Bayes Classification method.
  • Learners who want to work in data science and big data field
  • students who want to learn machine learning
  • Data analyser, Researcher, Engineers and Post Graduate Students need accurate and fast regression method.
  • Modelers, Statisticians, Analysts and Analytic Professional.
Read More All Levels All Levels product length iconTime: 2h

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Instructor Details

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My passion is teaching people through online courses. I love learning new skills, and since 2015 have been teaching people like you everything. I create courses that teach you how to become the better version of yourself with all kinds of skills.
What would you like to learn?
Would you like to learn Artificial Intelligence in python?
Would you like to make money creating landing pages?
Would you like to build your own AI programs & do something awesome for you?
Would you like to learn Xamarin to make both iOS/Android apps?
Would you like to learn how to write codes in HTML5 and CSS3?
Would you like to learn MATLAB the scientific language for researchers?

If you want to do any of these things, just enroll in the course. You have a 30-day money back guarantee if you don't like it. And I'm always improving my courses so that they stay up to date and the best that they can be. Check them out, and enroll today!
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About Sobhan N:
I have PhD degree in Electrical Engineering and like to learn anything about Electronics, Programming and Artificial Intelligence. I like electronic stuff like Arduino, Raspberry Pi and microcontrollers.
My passion is

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Reviews

3.3

16 ratings on Udemy

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By Abu Sayem Sarkar

Nice content! iam really setisfy.

By Ambujaksh Shah

beacause of its coding portion the way he is explaning is quite good

By Fred

I like this course because describe k nearest neighbors and Bayes algorithms properly. I think it's useful course for any body who want to learn basic classification algorithms. Thumbs Up!

By Nicodemus Omiwo Andiego

The course was an eye opener. Using real life examples makes it easier to relate and apply to my area of expertise.

It came in handy while working on client marketing project.

By Richard Alan Robey

This course is opening new doorways for me. I am beginning to form new skills, hopefully to re-enter the workforce.

By Tharindu Buddhika Adhikari

This course is amazing and above my expectations! Very good exercises, good speed, well communicated. The instructor made me feel very comfortable and was able to take many things away. Excellent content and very knowledgeable instructor!

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Overall Score : 66 / 100

Artificial Intelligence #3:kNN & Bayes Classification method
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  • Video Duration: icon 2h
  • Price: $19.99
  • Provider: icon Udemy
  • Updated: icon December 2017
  • Level: All Levels All Levels
  • Language: icon English
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