Artificial Intelligence #3:kNN & Bayes Classification method

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

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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!

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  • 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,

Sobhan

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.