Predictive Modeling and Machine Learning with MATLAB

MathWorks via Coursera

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Details

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Provider

Coursera
Pricing

Free Online Course (Audit)
Languages

English
Certificate

Paid Certificate Available
Duration & workload

18 hours 52 minutes
Sessions

On-Demand
Level

Beginner
Subtitles

English, Arabic, French, Portuguese, German, Thai, Indonesian, Spanish, Kazakh, Hindi, Russian, Swedish, Korean, Greek, Italian, Chinese, Ukrainian, Japanese, Polish, Dutch, Turkish, Hungarian, Bengali, Pashto, Urdu, Azerbaijani, Farsi

Found in

Part of

Practical Data Science with MATLAB

4.8

Overview

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In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB and Data Processing and Feature Engineering with MATLAB to increase your ability to harness the power of MATLAB to analyze data relevant to the work you do. These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background. To be successful in this course, you should have some background in basic statistics (histograms, averages, standard deviation, curve fitting, interpolation) and have completed courses 1 through 2 of this specialization. By the end of this course, you will use MATLAB to identify the best machine learning model for obtaining answers from your data. You will prepare your data, train a predictive model, evaluate and improve your model, and understand how to get the most out of your models.

Syllabus

Creating Regression Models

In this module you'll apply the skills gained from the first two courses in the specialization on a new dataset. You'll be introduced to the Supervised Machine Learning Workflow and learn key terms. You'll end the module by creating and evaluating regression machine learning models.

Creating Classification Models

In this module you'll learn the basics of classification models. You'll train several types of classification models and evaluation the results.

Applying the Supervised Machine Learning Workflow

In this module you'll apply the complete supervised machine learning workflow. You'll use validation data inform model creation. You'll apply different feature selection techniques to reduce model complexity. You'll create ensemble models and optimize hyperparameters. At the end of the module, you'll apply these concepts to a final project.

Advanced Topics and Next Steps

Taught by

Michael Reardon, Maria Gavilan-Alfonso, Erin Byrne, Matt Rich, Brandon Armstrong, Adam Filion, Nikola Trica, Isaac Bruss, Brian Buechel, Heather Gorr, and Sam Jones

Reviews

4.9 rating, based on 81 Class Central reviews

4.8 rating at Coursera based on 116 ratings

Start your review of Predictive Modeling and Machine Learning with MATLAB

Magesh John

The course offered by MATHWORKS provides an excellent entry into the vast and ever-growing field of data science. The course starts with how we can visualise and clean data for better analyses before introducing you to unsupervised learning. Followi…

The course offered by MATHWORKS provides an excellent entry into the vast and ever-growing field of data science. The course starts with how we can visualise and clean data for better analyses before introducing you to unsupervised learning. Following unsupervised learning, the principle behind principal component analyses (PCA) is also explained in the best simplest way. The math behind it can be obtained, based on personal interest, from other resources. Then you are taken to supervised learning. The machine learning apps are the best what MATLAB offers which not only helps you create different models very fast and analyse but also generate the code behind it which can help you to later use code only for creating the model. It is only when you have the raw code you can do any kind of adjustments or modifications you may want.

The course is condensed. Please be prepared to code along with the the lectures. You may want to repeat it more than one time to really get an intuition of what is happening. The forums of discussion also provide excellent information and you may raise your queries there which are usually responded to promptly by the teaching staff.

The course clearly demonstrates the 'muscle power' MATLAB has in dealing with data. Backed up with the already vast arrays of built-in functions, you can wrangle and manipulate the data in any way you want.

To MATHWORKS teaching staff: a BIG THANK YOU from my side. Looking forward to more such courses. I may request you to provide the students with a pdf file of the course contents covering the major aspects taught in the course.
Anonymous

I took this course after the DeepLearning.AI specialization for ML, and I can say that this course really helped me to get btter understanding of the courses in DeepLearning.AI specialization. The quizes and Assignments in this course were good examples of real-world applications.
The only thing I may complain about, is that the course project is peer review, which means it takes a long time for your project to be review. Also, some peers may cheat, since the results should be reported in a .pdf file rather than the .mlx code.
Anonymous

Some times the automated grader can not understand that C = ['A','B'] or C = ['B','A'] might be same. For example in one assignment I got 75% just for combinatio sequence of features which should not matter.

If accuracy is asked to be calculated I think it will be better how the answer should look like. For the final quiz initially I put 77.8 % it was wrong, then I put 0.778 it was wrong as well then I just placed 77.8 than it was right.
Anonymous

As a civil engineering student with a keen interest in integrating machine learning techniques into my field, the "Predictive Modeling and Machine Learning with MATLAB" course by MathWorks via Coursera has been a highly valuable and enriching experience. The course offers a comprehensive overview of predictive modeling and machine learning concepts, utilizing MATLAB as the primary tool for implementation.
Anonymous

II highly recommend the "Predictive Modeling and Machine Learning using MATLAB" course to anyone looking to gain a comprehensive understanding of machine learning techniques. Whether you're a beginner or looking to deepen your knowledge, this course provides a solid foundation and practical skills that are directly applicable to real-world projects. The investment in this course is undoubtedly worthwhile.
Masoud Fattahi Bandpey

The 'Predictive Modeling and Machine Learning with MATLAB' course offered a comprehensive dive into the nuances of MATLAB's machine learning toolbox. The real-world applications illustrated helped bridge the gap between theory and practice.
The instructors from MathWorks showcased a deep understanding of the subject, making even complex algorithms accessible for beginners.
I particularly appreciated the hands-on exercises and MATLAB code walkthroughs, which solidified my grasp of the topics.
Theophilus Kwofie

I had no experience with ML, but now I can boldly say that I can use ML to tackle real-world problems.
Anonymous

I highly recommend this course, it has many exercises to practice and not die trying. It is very easy to understand all the concepts because they explain it with examples.
Anonymous

This course is really good, they provide a real example of how to do the data analysis, it's also easy to understand, and I am very excited for the next course as well
Anonymous

Great Course. Well detailed and engaging. The content is highly interactive and is designed to ensure that one concentrates and pays attention to details.
Christina Jacob

The course gave students a thorough understanding of the different types of models, allowed them to quickly explore the dataset, and showed them how predictors related to one another. The tutorials go into great detail on how to use MATLAB tools eff…

The course gave students a thorough understanding of the different types of models, allowed them to quickly explore the dataset, and showed them how predictors related to one another. The tutorials go into great detail on how to use MATLAB tools efficiently. The course is quite well-designed, and there are quizzes to assess the student's understanding of the material. Additionally, scripts of functions that can be used with our own datasets are provided. As a person from civil engineering plunging into this field was totally new but the classes were eye-opening for my research works. Looking forward to learning more courses from Coursera. It was a wonderful learning opportunity and offered a solid foundation for putting machine learning into practice.
Anonymous

A thorough comprehension of various model types, quick dataset analysis skills, and the capacity to identify correlations between predictors were all offered by the certification. The lectures provided detailed instructions for using the MATLAB tools as well as several illustrations. Excellent tests are provided in the course to gauge students' comprehension of the subject matter. Additional scripts of functions are provided, which we can apply with our own datasets. It was a fantastic learning experience and provided a strong foundation for the application of machine learning.
Additionally, the instructors have all the necessary skills for the course and are very knowledgeable about it.

Thanks!!
Anupam Sharma

It was one of the best course I took among many in search of proper course.
I can say this course and specialization it belongs to is having potential to make you ready for being a more than beginner level data scientist and gives you strong foundation upon which you can excel in any role where data science and machine learning is utilized.
I am big fan of Mathworks and MATLAB so if my review seems biased, "IT IS NOT."
Try the course and I am sure you will not be afraid to implement ML next time.
Just one thing - its will be good to have a 5/10 level programming skills before you start, and any language will do but OOPs concept should be somewhat you should be aware of.
Anonymous

Now that I can understand the ML workflow, the next step is to try to apply it to a real problem and focus on implementing the model.
Zahra Nozarijouybari

Great course to interactively learn how to use Matlab Apps for Machine Learning Projects. Most of the assessments help to better understand the concepts and the pre-prepared live scripts and codes are very well-suited for getting one's hands into the work.
Anonymous

Hi
Predictive Modeling and Machine Learning with MATLAB provides a clear and practical approach, making complex concepts easily understandable. While beginner-friendly, those seeking advanced methodologies might find it introductory.
Saba Siddiqui

The course provided the deep and quick insight into the types of models, to explore the dataset, and to see relationship between predictors in a little time. The videos are detailed about using the MATLAB tools effectively. The course is very well designed and has quiz exercises to evaluate the students' concepts and learning. Scripts of functions are also provided which can be applicable to our own datasets. It was a great learning experience and provided the good starting point for implementing the machine learning.
Anonymous

The course showcased a thorough comprehension of diverse model varieties and efficiently examined the dataset. The tutorials presented elaborate guidelines on using MATLAB tools efficiently. The program is excellently structured and includes assessments to evaluate the learners' grasp of the content. It's highly beneficial for individuals who are novices in the field of machine learning.
Anonymous

The course demonstrated a comprehensive understanding of different model types, quickly exploring the dataset. The tutorials provide detailed information on how to use MATLAB tools effectively. The course is very well designed and there are tests to measure the student's understanding of the material. It is very helpful to people who are new to machine learning.
Anonymous

A really interesting course. Highly recommended for those engineers involved in decision making based on large datasets.
The tool is very powerful and easy to use. The possibilities to visualise data, test different configurations and models make it perfect for engineers without the need to delve into the mathematical layers underneath each model.