Artificial Intelligence #3-kNN Bayes Classification method

Artificial Intelligence #3:kNN & Bayes Classification method, available at $19.99, has an average rating of 2.9, with 20 lectures, based on 16 reviews, and has 1901 subscribers.

You will learn about 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. This course is ideal for individuals who are Anyone who wants to make the right choice when starting to learn kNN & Bayes Classification method. or Learners who want to work in data science and big data field or students who want to learn machine learning or Data analyser, Researcher, Engineers and Post Graduate Students need accurate and fast regression method. or Modelers, Statisticians, Analysts and Analytic Professional. It is particularly useful for Anyone who wants to make the right choice when starting to learn kNN & Bayes Classification method. or Learners who want to work in data science and big data field or students who want to learn machine learning or Data analyser, Researcher, Engineers and Post Graduate Students need accurate and fast regression method. or Modelers, Statisticians, Analysts and Analytic Professional.

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Summary

Title: Artificial Intelligence #3:kNN & Bayes Classification method

Price: $19.99

Average Rating: 2.9

Number of Lectures: 20

Number of Published Lectures: 20

Number of Curriculum Items: 20

Number of Published Curriculum Objects: 20

Original Price: ￡94.99

Quality Status: approved

Status: Live

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.

Who Should Attend

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.

Target Audiences

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.

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 classifiersare 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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Sobhan

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Required Softwares and Libraries

Chapter 2: k Nearest Neighbors Classification Method

Lecture 1: Theory of k Nearest Neighbors Classification Method

Lecture 2: k Nearest Neighbors Classification Method to classify random dataset Part 1

Lecture 3: k Nearest Neighbors Classification Method to classify random dataset Part 2

Lecture 4: k Nearest Neighbors Classification Method to classify random dataset Source Code

Lecture 5: k Nearest Neighbors Classification for IRIS Dataset

Lecture 6: k Nearest Neighbors Classification for IRIS Dataset Source Code

Lecture 7: Write k Nearest Neighbors Classification Method by yourself Part 1

Lecture 8: Write k Nearest Neighbors Classification Method by yourself Part 2

Lecture 9: Write k Nearest Neighbors Classification Method by yourself SourceCode

Chapter 3: Naive Bayes Classification Method

Lecture 1: Theory of Naive Bayes Classification Method

Lecture 2: Use Naive Bayes to Classify IRIS Dataset Part 1

Lecture 3: Use Naive Bayes to Classify IRIS Dataset Part 2

Lecture 4: Use Naive Bayes to Classify IRIS Dataset Source Code

Lecture 5: Use Naive Bayes to Classify Diabetes dataset

Lecture 6: Use Naive Bayes to Classify Diabetes dataset Source Code

Lecture 7: Write Naive Bayes Classification Method by Yourself Part 1

Lecture 8: Write Naive Bayes Classification Method by Yourself Part 2

Lecture 9: Write Naive Bayes Classification Method by Yourself Source Code

Instructors

Sobhan N.
AI Developer|Electrical Engineer (PhD)|21,000+ Students

Rating Distribution

1 stars: 4 votes

2 stars: 1 votes

3 stars: 2 votes

4 stars: 2 votes

5 stars: 7 votes

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