Artificial Neural Network and Machine Learning using MATLAB

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

4.17 (382 reviews)
Data Science
Artificial Neural Network and Machine Learning using MATLAB
4.5 hours
Jul 2022
last update
regular price

What you will 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

Why take this course?

๐Ÿš€ Artificial Neural Network and Machine Learning using MATLAB ๐Ÿง ๐Ÿค–

Course Instructor: Nastaran Reza Nazar Zadeh

Course Headline: ๐ŸŽ“ "Learn to Create Neural Network with Matlab Toolbox and Easy to Follow Codes; with Comprehensive Theoretical Concepts"

Are you ready to unlock the potential of Artificial Neural Networks (ANN) and Machine Learning within the powerful MATLAB environment? Whether you're an experienced developer aiming to expand your skillset or a complete beginner eager to grasp the core principles of machine learning, this course is tailor-made for you!

Why This Course?

  • Dual Focus: Offers a balanced blend of theoretical knowledge and practical implementation.
  • For All Levels: Accessible to everyone, from novices to experts.
  • Real-World Applications: Teaches you how to apply neural networks in various domains like classification, clustering, pattern recognition, function approximation, control, prediction, and optimization.

What You Will Learn

  • ๐Ÿ“š Theoretical Foundations: Understand the fundamental concepts of ANN and Machine Learning, including learning algorithms and data processing strategies.

  • ๐Ÿงช Practical Implementation: Master the art of designing and training multilayer perceptron (MLP) neural networks using MATLAB's specialized toolboxes and functions.

  • ๐Ÿ› ๏ธ MATLAB Toolbox Skills: Utilize MATLAB's powerful tools designed specifically for machine learning tasks to streamline your development process.

Course Highlights

  • Comprehensive Curriculum: From the basics to advanced topics, this course covers it all.

  • Interactive Learning: Engage with interactive examples and hands-on projects to solidify your understanding.

  • Expert Guidance: Learn from the expertise of Nastaran Reza Nazar Zadeh, an experienced instructor who will guide you through each concept.

  • Easy-to-Follow Codes: Get access to clear and concise MATLAB code examples that make implementing neural networks a breeze.

By the End of This Course, You Will Be Able To:

  • Design and implement neural network models in MATLAB for various machine learning tasks.
  • Analyze and visualize data using MATLAB's robust tools.
  • Make informed decisions about model parameters and optimize your neural networks for better performance.
  • Apply the knowledge you gain to solve real-world problems across diverse sectors such as finance, healthcare, automotive, and more!

Join Us Now!

Embark on a journey to master Artificial Neural Networks and Machine Learning with MATLAB. Enroll in this course today and transform your career tomorrow. ๐ŸŒŸ

Don't miss out on the opportunity to be at the forefront of AI development with MATLAB. Sign up now and start learning with Nastaran Reza Nazar Zadeh, your expert guide on this exciting journey into the world of machine learning and neural networks! ๐Ÿš€๐Ÿ“šโœจ


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Our review

๐ŸŒŸ Course Review Summary ๐ŸŒŸ

Overall Rating: 4.52/5

The course has received overwhelmingly positive reviews, with a consensus that it is an excellent resource for understanding and applying neural networks (NNs) using MATLAB. The course is praised for its clear explanations, practical examples, and the ability to apply learned concepts to real-world problems promptly.


  • Excellent Explanations: The mathematical and programming aspects of NNs are thoroughly explained, making it accessible even for those with no prior knowledge.
  • Practical Application: The course is immediately applicable in professional settings, as evidenced by users able to apply what they learned to their jobs within a week.
  • Well-Structured Content: The pace of the course is considered just right, with lessons following the theoretical explanations with practical coding sessions that reinforce learning.
  • Beginner-Friendly: Recommended for beginners, particularly those already familiar with MATLAB, as it provides a clear understanding of how to use the software for NNs.
  • Real-World Examples: The course includes examples from various fields, such as chemical engineering and hydrological forecasting, demonstrating the versatility of NNs.


  • Theoretical Weaknesses: Some users found the initial theoretical sections to be basic and something that could be quickly grasped through a short YouTube video.
  • MATLAB Focus: The course is very focused on MATLAB's NFtool, which some users feel doesn't cover the broader aspects of neural networks or other programming environments like Python.
  • Limited Scope: A few reviews mention that the course could include more complex models, optimization methods for determining layer and neuron configurations, and a wider range of neural network types, such as Multilayer Perception (MLP) networks.
  • Instructional Style: Some users were inconvenienced by the instructor's voice tone, which they found distracting and difficult to focus on.
  • Expectations Unmet: A few users expected a more comprehensive coverage of NNs, including tutorials on MATLAB's Deep Learning Toolbox and more advanced topics beyond NN fitting.

Additional Notes:

  • The course is highly recommended for those who use MATLAB and are new to neural networks or are looking to deepen their understanding of NNs within the MATLAB environment.
  • Users with experience in other programming environments might find the course somewhat one-sided in its focus on MATLAB.
  • For civil engineering students without coding skills, this course is a valuable resource for integrating neural networks into their projects.
  • The course is a great starting point but may not be suitable for individuals seeking an in-depth exploration of all types of neural networks or those looking to learn about the broader application of NNs beyond MATLAB.

In conclusion, the course is well-regarded and effective for its intended purpose of teaching neural networks within the context of MATLAB. It could be improved by including more advanced topics, a broader range of examples, and potentially addressing the pacing and scope of the theoretical introduction. The instructor's presentation style, while not a substantial drawback, is worth noting for potential students.



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