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
If you like the tutorial please share it with your friends. Like the Facebook page for regular updates and YouTube channel for video tutorials.