Artificial Neural Network and Machine Learning using MATLAB

Name: Artificial Neural Network and Machine Learning using MATLAB
Rating: 4.4 (483 reviews)

Learn to Create Neural Network with Matlab Toolbox and Easy to Follow Codes; with Comprehensive Theoretical Concepts

Created byNastaran Reza Nazar Zadeh

Last updated 9/2025

English

What you'll learn

Develop a multilayer perceptron neural networks or MLP in MATLAB using Toolbox
Apply Artificial Neural Networks in practice
Building Artificial Neural Network Model
Knowledge on Fundamentals of Machine Learning and Artificial Neural Network
Understand Optimization methods
Understand the Mathematical Model of a Neural Network
Understand Function approximation methodology
Make powerful analysis
Knowledge on Performance Functions
Knowledge on Training Methods for Machine Learning

Course content

7 sections • 53 lectures • 4h 26m total length

Introduction3:16
Course Content Overview

History of A.I.9:14
The important events in the history of Artificial Intelligence
Artificial Intelligence4:05
Understand the Artificial Intelligence concepts and the area in which we can apply it.
Machine Learning2:50
Simple definition and sharp differentiation between Artificial Intelligence, Machine Learning and Deep Learning
Learning Methods1:22
Applications of different Learning Methods in ML
Supervised Learning3:33
Supervised Learning concepts with an example
Unsupervised Learning1:55
Unsupervised Learning concepts with an example
Reinforcement Learning1:39
Reinforcement Learning concepts with an example
Fundamentals of Machine Learning

Biological Model of Neural Network4:38
Biological inspiration for Artificial Neural Network
How our Brain makes decisions2:59
Classification theory5:01
Understand the Classification theory with an example and be able to drive a mathematical equation for it.
What is Artificial Neuron3:11
Understand the concepts of an Artificial Neuron with an example
Combinational Logic Gate: AND5:37
Understand the AND gate and be able to drive a classifier line on its graph
Logical Disjunction Gate: OR2:19
Understand the OR gate and be able to drive a classifier line on its graph
Artificial Neuron Model5:34
Create a simple model for artificial Neuron with an example and compare it with the biological model of a neuron.
Exclusive Logical Gate: XOR3:40
Review the XOR gate and represent it with a classifier on its graph.
Complementary Lecture on "Exclusive Logical Gate: XOR"7:38
This video is a complementary lecture to clarifies the concept XOR in AND/OR Space.
Multilayer Perceptron Concepts2:52
Create a model for Multilayer Perceptron based on XOR Example
Mathematical Model of Net function5:32
Mathematical Calculation for Net Function (activation function)
Mathematical Model of MPL3:39
Calculate the equation for Multilayer perceptron
Hardware Implementation of a Neuron2:32
MLP Structure2:50
Neural Network's Logic5:09
How the Neural Network Works
Calculating Mean Square Error6:45
Mathematical Model for MSE
Optimization Methods3:20
Different Methods and Algorithm for optimization
Techniques of Neural Networks5:06
Neural Network Architectures0:48
Fundamentals of Neural Network Models

MATLAB overview1:03
Get start with MATLAB Software
Function Approximation Example4:32
Understand the Function Approximation Concepts
Generate Data in MATLAB4:22
Data Selection4:36
Start working with MATLAB Toolbox (Net fitting tools)
Validation and Test Data7:06
Set data for Training Validation and testing
Train Network6:39
Understand the training items of MATLAB toolbox
Plots5:21
Understand different plots generated by MATLAB
Retrain the Neural Network1:34
Retrain the neural network to achieve better result
Effect of increasing the number of samples on NN5:43
Knowledge on Sigmoid and Linear Functions
Activation Function3:02
Knowledge on Sigmoid and Linear Functions
Activation Function Complementary Lecture7:49
Complete guide to activation functions in MATLAB Neural Network Toolbox covering Linear, Sigmoid, Tanh, ReLU, Softmax, and Radial Basis functions with comparison tables, decision flowcharts, and MATLAB implementation examples.
Affect of Neurons in Hidden Layer on NN8:01
Calculate Maximum Number of Neurons per Layer6:32
A novel solution and formula to calculate the Minimum and Maximum number of Neurons that are required for the hidden layer to train an efficient Neural Network and get the best result

Fitting Network10:26
Modify the Fitting Network with changing the structure of the NN; explore different net functions with various activation functions
Train Function3:33
Review different Training Functions
Performance Function1:49
Review different Performance Functions
Train Parameters9:18
Adjust the train Parameters (Epoch, Time, Performance, Gradient and etc.)
Recalculating Training, Validation and Test Performance8:13
Modify Plots4:19

Requirements

Basics of Mathematics
No programming experience is needed. You will learn everything you need to know.

Description

This course is uniquely designed to be suitable for both experienced developers seeking to make that jump to Machine learning or complete beginners who don't understand machine learning and Artificial Neural Network from the ground up.

In this course, we introduce a comprehensive training of multilayer perceptron neural networks or MLP in MATLAB, in which, in addition to reviewing the theories related to MLP neural networks, the practical implementation of this type of network in MATLAB environment is also fully covered.

MATLAB offers specialized toolboxes and functions for working with Machine Learning and Artificial Neural Networks which makes it a lot easier and faster for you to develop a NN.

At the end of this course, you'll be able to create a Neural Network for applications such as classification, clustering, pattern recognition, function approximation, control, prediction, and optimization.

Who this course is for:

Anyone with passion for learning!
Anyone who wants to develop a Neural Network with no programming skills!
Students who want to learn and apply machine learning for their projects.
Professionals in the field of Electronic, Computer, IT and Industry.
Researchers who want to explore and conduct a research with Machine Learning techniques.
Any business owner who wants to understand how to leverage the Machine Learning in their business
Anyone who is looking for salary growth and new opportunities in the field of Data Science and Machine Learning

Artificial Neural Network and Machine Learning using MATLAB

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 3min

Artificial Intelligence and Machine Learning7 lectures • 25min

Fundamentals of Artificial Neural Network19 lectures • 1hr 19min

MATLAB: Neural Net Fitting Tool13 lectures • 1hr 6min

MATLAB: Scripts3 lectures • 28min

MATLAB: Modified Advance Script6 lectures • 38min

MATLAB: Engine Data Set (Multiple Targets)4 lectures • 28min

Requirements

Description

Who this course is for: