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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

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Courses>Data Science>Artificial Intelligence #3:kNN & Bayes Classification method

Artificial Intelligence #3:kNN & Bayes Classification methodWhat you will learn

1. Use k Nearest Neighbor classification method to classify datasets.

2. Learn main concept behind the k Nearest Neighbor classification method .

3. Write your own code to make k Nearest Neighbor classification method by yourself.

4. Use k Nearest Neighbor classification method to classify IRIS dataset.

5. Use Naive Bayes classification method to classify datasets.

6. Learn main concept behind Naive Bayes classification method.

7. Write your own code to make Naive Bayes classification method by yourself.

8. Use Naive Bayes classification method to classify Pima Indian Diabetes Dataset.

9. Use Naive Bayes classification method to obtain probability of being male or female based on Height, Weight and FootSize.

Target audiences

1. Anyone who wants to make the right choice when starting to learn kNN & Bayes Classification method.

2. Learners who want to work in data science and big data field

3. students who want to learn machine learning

4. Data analyser, Researcher, Engineers and Post Graduate Students need accurate and fast regression method.

5. Modelers, Statisticians, Analysts and Analytic Professional.

Requirements

1. You should know about basic statistics

2. You must know basic python programming

3. Install Sublime and required library for python

4. You 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.

5. All you need is a decent PC/Laptop (2GHz CPU, 4GB RAM). You will get the rest from me.

FAQ

1. How long do I have access to the course materials?

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

2. 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!

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

Copyright © 2021 Careertail.

All rights reserved

All rights reserved

Quick Links

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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 methodWhat you will learn

1. Use k Nearest Neighbor classification method to classify datasets.

2. Learn main concept behind the k Nearest Neighbor classification method .

3. Write your own code to make k Nearest Neighbor classification method by yourself.

4. Use k Nearest Neighbor classification method to classify IRIS dataset.

5. Use Naive Bayes classification method to classify datasets.

6. Learn main concept behind Naive Bayes classification method.

7. Write your own code to make Naive Bayes classification method by yourself.

8. Use Naive Bayes classification method to classify Pima Indian Diabetes Dataset.

9. Use Naive Bayes classification method to obtain probability of being male or female based on Height, Weight and FootSize.

Target audiences

1. Anyone who wants to make the right choice when starting to learn kNN & Bayes Classification method.

2. Learners who want to work in data science and big data field

3. students who want to learn machine learning

4. Data analyser, Researcher, Engineers and Post Graduate Students need accurate and fast regression method.

5. Modelers, Statisticians, Analysts and Analytic Professional.

Requirements

1. You should know about basic statistics

2. You must know basic python programming

3. Install Sublime and required library for python

4. You 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.

5. All you need is a decent PC/Laptop (2GHz CPU, 4GB RAM). You will get the rest from me.

FAQ

1. How long do I have access to the course materials?

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

2. 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!

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

Copyright © 2021 Careertail.

All rights reserved

All rights reserved

Quick Links

Get StartedLog InAbout UsCourses

Company

BlogContactsPrivacy PolicyCookie PolicyTerms and Conditions

Stay up to date

Trustpilot