## Artificial Intelligence and Machine Learning Tutorial with Simple Solved Examples

## Introduction Machine Learning

In this section, you will learn, Basic Concepts of machine learning such as, Concept Learning, what is a consistent hypothesis, list-then-eliminate algorithm, Find-S algorithm, Candidate elimination algorithm.

Concept Learning in Machine Learning

Find-S Algorithm Machine Learning and Unanswered Questions of Find-S Algorithm

Find-S Algorithm – Maximally Specific Hypothesis and Solved Example – 1 and Solved Example -2

Consistent Hypothesis, Version Space and List Then Eliminate algorithm Machine Learning

Candidate Elimination Algorithm and Solved Example – 1 Machine Learning

Candidate Elimination Algorithm and Solved Example – 2 Machine Learning

Candidate Elimination Algorithm and Solved Example – 3 Machine Learning

## Decision Tree Learning

In this section, you will learn the decision tree learning algorithm, simple solved examples, issues in decision tree learning, etc.

1. How to build a decision Tree for Boolean Function Machine Learning

2. How to build a decision Tree for Boolean Function Machine Learning

3. How to build Decision Tree using ID3 Algorithm – Solved Numerical Example – 1

4. How to build Decision Tree using ID3 Algorithm – Solved Numerical Example -2

5. How to build Decision Tree using ID3 Algorithm – Solved Numerical Example -3

6. Appropriate Problems for Decision Tree Learning Machine Learning Big Data Analytics

7. How to find the Entropy and Information Gain in Decision Tree Learning

8. Issues in Decision Tree Learning Machine Learning

9. How to Avoid Overfitting in Decision Tree Learning, Machine Learning, and Data Mining

10. How to handle Continuous Valued Attributes in Decision Tree Learning, Machine Learning

11. How to find the Entropy – Decision Tree Learning – Machine Learning

## Artificial Neural Network

In this section you will learn, perceptron learning, delta rule, gradient descent learning, backpropagation algorithm, and its derivation.

Appropriate problems which can be solved using Artificial Neural Networks – Machine Learning

Perceptron Training Rule for Linear Classification – Artificial Neural Network

AND GATE Perceptron Training Rule – Artificial Neural Network

OR GATE Perceptron Training Rule – Artificial Neural Network

2. Gradient Descent Algorithm and the Delta Rule for Non-Linearly Separable Data

Back Propagation Algorithm – Artificial Neural Network – Machine Learning

## Bayesian Learning

In this section, you will learn, basic bayesian theory, maximum likelihood hypothesis, Bayes classifier, text classification using Bayes rule, etc.

Naive Bayes Theorem, Maximum A Posteriori Hypothesis, MAP Brute Force Algorithm

Maximum Likelihood Hypothesis and Least Squared Error Hypothesis

How to use Naive Bayes rule to check whether the Patient has Cancer or Not

1. Solved Example Naive Bayes Classifier to classify New Instance PlayTennis

2. Solved Example Naive Bayes Classifier to classify New Instance, Species Class M and H

3. Solved Example Naive Bayes Classifier to classify New Instance Car Example

4. Solved Example Naive Bayes Classifier to classify New Instance Football Match Example

5. Solved Example Naive Bayes Classifier to classify New Instance Naive Bayes Theorem

1. Bayesian Belief Network (BBN) Solved Numerical Example Burglar Alarm System

2. Bayesian Belief Network (BBN) Solved Numerical Example Burglar Alarm System

3. Bayesian Belief Network BBN Solved Numerical Example Battery Gauge Fuel and Start Car

KMeans Clustering Algorithm, Steps in KMeans Algorithm, Advantages and Disadvantages

Expectation-Maximization EM Algorithm Steps Uses Advantages and Disadvantages

How to do Text / Document Classification using Naive Bayes Classifier and TF-IDF features

## Instance-Based Learning

In this section you will learn, instatne based learning, k-nearest neighbour algorithm, Q-Leraning etc

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