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Courses>Data Science>Artificial Intelligence #3:kNN & Bayes Classification method
DevelopmentArtificial Intelligence #3:kNN & Bayes Classification method
Price:Paid
Length:2 hours
Content type:video
level:all levels
Updated:19 February 2024
Published:21 August 2022
Similar courses
Opportunities
Courses>Data Science>Artificial Intelligence #3:kNN & Bayes Classification method
Artificial Intelligence #3:kNN & Bayes Classification method
2.8 (1.9k)
2 hours
1900 students
What you will learn
1Use k Nearest Neighbor classification method to classify datasets.
2Learn main concept behind the k Nearest Neighbor classification method .
3Write your own code to make k Nearest Neighbor classification method by yourself.
4Use k Nearest Neighbor classification method to classify IRIS dataset.
5Use Naive Bayes classification method to classify datasets.
6Learn main concept behind Naive Bayes classification method.
7Write your own code to make Naive Bayes classification method by yourself.
8Use Naive Bayes classification method to classify Pima Indian Diabetes Dataset.
9Use Naive Bayes classification method to obtain probability of being male or female based on Height, Weight and FootSize.
Target audiences
1Anyone who wants to make the right choice when starting to learn kNN & Bayes Classification method.
2Learners who want to work in data science and big data field
3students who want to learn machine learning
4Data analyser, Researcher, Engineers and Post Graduate Students need accurate and fast regression method.
5Modelers, Statisticians, Analysts and Analytic Professional.
Requirements
1You should know about basic statistics
2You must know basic python programming
3Install Sublime and required library for python
4You should have a great desire to learn programming and do it in a hands-on fashion, without having to watch countless lectures filled with slides and theory.
5All you need is a decent PC/Laptop (2GHz CPU, 4GB RAM). You will get the rest from me.
FAQ
You can view and review the lecture materials indefinitely, like an on-demand channel.
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!
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


___________________________________________________________________________

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.

___________________________________________________________________________

It's time to take Action!

Click the "Take This Course" button at the top right now!

...Don't waste time! Every second of every day is valuable...

I can't wait to see you in the course!

Best Regrads,

Sobhan

Similar courses
Opportunities
Make the most out of your online education
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All rights reserved
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Careertail
Courses>Data Science>Artificial Intelligence #3:kNN & Bayes Classification method
DevelopmentArtificial Intelligence #3:kNN & Bayes Classification method
Price:Paid
Length:2 hours
Content type:video
level:all levels
Updated:19 February 2024
Published:21 August 2022
Similar courses
Opportunities
Courses>Data Science>Artificial Intelligence #3:kNN & Bayes Classification method
Artificial Intelligence #3:kNN & Bayes Classification method
2.8 (1.9k)
2 hours
1900 students
What you will learn
1Use k Nearest Neighbor classification method to classify datasets.
2Learn main concept behind the k Nearest Neighbor classification method .
3Write your own code to make k Nearest Neighbor classification method by yourself.
4Use k Nearest Neighbor classification method to classify IRIS dataset.
5Use Naive Bayes classification method to classify datasets.
6Learn main concept behind Naive Bayes classification method.
7Write your own code to make Naive Bayes classification method by yourself.
8Use Naive Bayes classification method to classify Pima Indian Diabetes Dataset.
9Use Naive Bayes classification method to obtain probability of being male or female based on Height, Weight and FootSize.
Target audiences
1Anyone who wants to make the right choice when starting to learn kNN & Bayes Classification method.
2Learners who want to work in data science and big data field
3students who want to learn machine learning
4Data analyser, Researcher, Engineers and Post Graduate Students need accurate and fast regression method.
5Modelers, Statisticians, Analysts and Analytic Professional.
Requirements
1You should know about basic statistics
2You must know basic python programming
3Install Sublime and required library for python
4You should have a great desire to learn programming and do it in a hands-on fashion, without having to watch countless lectures filled with slides and theory.
5All you need is a decent PC/Laptop (2GHz CPU, 4GB RAM). You will get the rest from me.
FAQ
You can view and review the lecture materials indefinitely, like an on-demand channel.
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!
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


___________________________________________________________________________

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.

___________________________________________________________________________

It's time to take Action!

Click the "Take This Course" button at the top right now!

...Don't waste time! Every second of every day is valuable...

I can't wait to see you in the course!

Best Regrads,

Sobhan

Similar courses
Opportunities
Make the most out of your online education
Careertail
Copyright © 2021 Careertail.
All rights reserved
Quick Links
Get StartedLog InAbout UsCourses
Company
BlogContactsPrivacy PolicyCookie PolicyTerms and Conditions
Stay up to date
Trustpilot