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Predictive Modeling and Machine Learning with MATLAB

4.8 Filled star Filled star Filled star Filled star Half star
Based on 118 ratings
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Maria Gavilan-Alfonso, Michael Reardon, Isaac Bruss, Brandon Armstrong, Brian Buechel, Nikola Trica, Matt Rich, Heather Gorr, Erin Byrne, Sam Jones, and Adam Filion

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.

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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.

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What's inside

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.
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Focuses on applying supervised machine learning to address real-world problems
Provides a solid foundation in machine learning concepts and techniques
Provides hands-on experience through practical exercises and projects
Taught by experienced instructors with expertise in machine learning and data analysis
Recommended for individuals with some background in basic statistics and data analysis
May require additional resources or knowledge for those with limited programming background

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Create your own learning path. Save this course to your list so you can find it easily later.
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Reviews summary

Predictive modeling with matlab tools

According to learners, this course offers a solid introduction to predictive modeling and machine learning specifically within the MATLAB environment. Many appreciate the focus on using MATLAB's built-in tools, which allows for practical application without deep coding knowledge. The course is often described as a good next step for those who have completed the preceding courses in the specialization, building effectively on prior concepts. While generally well-received for its practical approach, some learners note that certain topics could benefit from more in-depth explanation or that the pace might be challenging if prerequisites aren't firmly met. The use of hands-on exercises and the final project are frequently highlighted as valuable components.
Connects theory to practice effectively.
"The assignments and the final project were key to solidifying my understanding. Applying the workflow end-to-end was very beneficial."
"I liked that the course emphasized applying different models and evaluating their performance on real data."
"The hands-on coding and projects are the strongest part of the course for me, providing practical experience."
Effective continuation of specialization.
"This course felt like a natural progression from the previous two, weaving in the data processing and exploratory analysis skills we learned."
"It integrated concepts from the earlier modules smoothly, making the supervised workflow feel logical."
"Having completed the first two courses really helped me grasp the material here much faster."
Leverages MATLAB's ML capabilities well.
"I found the course very practical, focusing on how to use the specific machine learning tools available in MATLAB, which is exactly what I needed."
"Using the Regression Learner and Classification Learner apps was a great way to apply the concepts immediately. Made it very hands-on."
"This course excels at showing you the MATLAB way of doing machine learning, which is efficient for existing MATLAB users."
Requires solid stats/prior course base.
"Make sure your basic statistics knowledge is solid; the course does assume you're comfortable with concepts like regression and evaluation metrics."
"Although it says no programming background needed beyond MATLAB basics, a strong grasp of the previous courses is crucial."
"If you're rusty on the prerequisites, you might find yourself needing to review material outside the course."
Some topics may feel rushed or basic.
"While broad, some advanced topics or the nuances of specific algorithms felt a bit glossed over."
"The pace can be quite fast, especially in modules covering multiple model types. It requires you to keep up."
"I felt that some explanations, while clear, could use more theoretical depth for a better understanding of *why* certain steps are taken."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Predictive Modeling and Machine Learning with MATLAB with these activities:
Review basic statistics and linear algebra concepts
Strengthen your foundational knowledge by reviewing basic statistics and linear algebra concepts that are essential for understanding machine learning.
Browse courses on Statistics
Show steps
  • Review notes or textbooks on statistics and linear algebra
  • Solve practice problems and exercises
  • Attend a refresher course or workshop (optional)
Seek Guidance from MATLAB Experts
Enhance your learning by seeking guidance from experienced MATLAB users or industry professionals who can provide valuable insights and support.
Browse courses on Mentorship
Show steps
  • Attend industry events or online forums to connect with potential mentors.
  • Reach out to professors, teaching assistants, or colleagues who have expertise in MATLAB.
  • Describe your interests and goals to find a mentor who aligns with your aspirations.
Review linear regression
Refreshes basic understanding of linear regression techniques
Browse courses on Linear Regression
Show steps
  • Read textbook section on linear regression
  • Complete practice problems on linear regression
14 other activities
Expand to see all activities and additional details
Show all 17 activities
Organize your course materials and create a study guide
Improve your learning efficiency by organizing your course materials and creating a comprehensive study guide that includes key concepts, formulas, and examples.
Browse courses on Study Skills
Show steps
  • Gather and organize your course materials
  • Create a study guide that outlines the key concepts
  • Include formulas, examples, and practice questions
  • Review and update the study guide regularly
Refresher in Basic Statistics
Review basic statistical concepts like histograms, averages, standard deviation, curve fitting, and interpolation to strengthen your foundation for this course.
Browse courses on Statistics
Show steps
  • Review lecture notes or textbooks on basic statistics covering the topics mentioned above.
  • Take practice quizzes or solve practice problems to test your understanding.
MATLAB Coding Study Group
Boost your coding skills and understanding of MATLAB by participating in a study group with fellow learners.
Browse courses on MATLAB
Show steps
  • Find or form a study group with other students enrolled in the course.
  • Set regular meeting times to discuss course material, work on coding challenges, and share knowledge.
  • Collaborate on projects or assignments to gain diverse perspectives and enhance your learning.
Practice using MATLAB for data analysis and machine learning
Enhance your MATLAB skills by practicing data analysis and machine learning tasks in the MATLAB environment.
Browse courses on MATLAB
Show steps
  • Find a dataset and import it into MATLAB
  • Clean and prepare the data for analysis
  • Perform exploratory data analysis (EDA) using MATLAB functions and visualizations
  • Train and evaluate machine learning models using MATLAB toolboxes
Regression Modeling Exercises
Reinforce your understanding of regression modeling by practicing with various datasets and scenarios.
Browse courses on Regression Modeling
Show steps
  • Find a dataset that aligns with your interests or a specific industry.
  • Apply regression techniques to build models and analyze results.
  • Experiment with different regression algorithms and compare their performance.
Practice building classification models
Reinforces hands-on experience in building and evaluating classification models
Browse courses on Classification
Show steps
  • Build a classification model using logistic regression
  • Build a classification model using a decision tree
  • Compare the performance of the two models
Attend a meetup on machine learning
Network and engage with professionals in the field, exchanging ideas and insights
Browse courses on Machine Learning
Show steps
  • Research and find relevant meetups
  • Attend the meetup and actively participate in discussions
Follow tutorials on specific machine learning techniques
Expand your knowledge by following tutorials that provide step-by-step guidance on implementing specific machine learning techniques.
Browse courses on Machine Learning Algorithms
Show steps
  • Find tutorials on topics of interest
  • Follow the instructions and implement the techniques
  • Experiment with different parameters and data sets
Write a blog post or article on a machine learning topic
Deepen your understanding by explaining a machine learning concept or technique in a blog post or article.
Browse courses on Machine Learning
Show steps
  • Choose a machine learning topic
  • Research and gather information on the topic
  • Write a blog post or article that clearly explains the concept or technique
  • Proofread and edit your writing
Classification Model Tutorial
Deepen your comprehension of classification models by creating a tutorial that explains key concepts and demonstrates their application.
Browse courses on Classification Modeling
Show steps
  • Choose a specific classification algorithm and research its underlying principles.
  • Develop a step-by-step guide on how to implement the algorithm using MATLAB.
  • Include examples and visualizations to illustrate the process and results.
Create a presentation on the supervised machine learning workflow
Develops understanding of the key steps and concepts in the supervised machine learning workflow
Show steps
  • Outline the key steps in the supervised machine learning workflow
  • Explain the purpose and importance of each step
  • Provide examples of how the workflow is used in real-world applications
Create a presentation on a machine learning model
Solidify your understanding of machine learning models by creating a presentation that explains a specific model's functionality, applications, and limitations.
Browse courses on Machine Learning Models
Show steps
  • Choose a machine learning model
  • Gather data and prepare it for modeling
  • Train and evaluate the model
  • Create a presentation that includes an explanation of the model, its results, and its potential applications
Predictive Model Evaluation Dashboard
Enhance your ability to evaluate and refine predictive models by creating a dashboard that visualizes key metrics and performance indicators.
Browse courses on Model Evaluation
Show steps
  • Identify relevant metrics to measure the effectiveness of your models.
  • Design and develop a dashboard using visualization tools like Tableau or Power BI.
  • Configure the dashboard to display metrics, charts, and insights in a user-friendly manner.
Develop a machine learning model to predict customer churn
Applies machine learning concepts to a real-world business problem and builds a model that helps mitigate customer attrition
Browse courses on Machine Learning
Show steps
  • Collect and clean the necessary data
  • Select and train a machine learning model
  • Evaluate and refine the model
  • Deploy the model and monitor its performance

Career center

Learners who complete Predictive Modeling and Machine Learning with MATLAB will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts use their knowledge of data analysis techniques, programming languages, and software applications to analyze data and extract insights. They use their skills to develop and implement data-driven solutions to business problems. Individuals in this role often work with other stakeholders to develop and implement data-driven solutions to business problems. Predictive Modeling and Machine Learning with MATLAB may be useful for someone pursuing a career as a Data Analyst because it provides an introduction to many of the tools and concepts used in the field.
See salaries and explore the career path for Data Analyst
Research Analyst
Research Analysts collect, analyze, and interpret data to develop insights and make recommendations for businesses. They use their knowledge of statistical methods, data analysis techniques and industry best practices to provide insights into market trends, customer behavior, and other important business issues. Individuals in this role often work with other stakeholders to develop and implement data-driven solutions to business problems. Predictive Modeling and Machine Learning with MATLAB may be useful for someone pursuing a career as a Research Analyst because it provides an introduction to many of the tools and concepts used in the field.
See salaries and explore the career path for Research Analyst
Financial Analyst
Financial Analysts use their knowledge of financial markets, accounting principles, and data analysis techniques to evaluate investment opportunities and make recommendations to clients. They use their skills to analyze financial data, develop financial models, and make investment recommendations. Individuals in this role often work with clients to develop and implement investment strategies. Predictive Modeling and Machine Learning with MATLAB may be useful for someone pursuing a career as a Financial Analyst because it provides an introduction to many of the tools and concepts used in the field.
See salaries and explore the career path for Financial Analyst
Actuary
Actuaries use their knowledge of mathematics, statistics, and business principles to assess risk and uncertainty. They use their skills to develop and implement solutions to financial problems, such as pricing insurance policies and managing investment portfolios. Individuals in this role often work with clients to develop and implement risk management strategies. Predictive Modeling and Machine Learning with MATLAB may be useful for someone pursuing a career as an Actuary because it provides an introduction to many of the tools and concepts used in the field.
See salaries and explore the career path for Actuary
Statistician
Statisticians use their knowledge of statistical methods and data analysis techniques to collect, analyze, and interpret data. They use their skills to develop and implement statistical models, and to make predictions about complex real-world situations. Individuals in this role often work with other stakeholders to develop and implement data-driven solutions to business problems. Predictive Modeling and Machine Learning with MATLAB may be useful for someone pursuing a career as a Statistician because it provides an introduction to many of the tools and concepts used in the field.
See salaries and explore the career path for Statistician
Data Engineer
Data Engineers design, build, and maintain the infrastructure that is used to store and process data. They use their knowledge of data management techniques, programming languages, and cloud computing platforms to develop and implement solutions for data storage, data processing, and data analysis. Individuals in this role often work with other stakeholders to develop and implement data-driven solutions to business problems. Predictive Modeling and Machine Learning with MATLAB may be useful for someone pursuing a career as a Data Engineer because it provides an introduction to many of the tools and concepts used in the field.
See salaries and explore the career path for Data Engineer
Data Scientist
Data Scientists leverage their extensive programming knowledge, their mastery of mathematics and statistics, and their understanding of business practices to analyze large sets of data. Their goal is to use this data to extract insights and make predictions about complex real-world situations. Individuals in this role often collaborate with domain experts and other stakeholders to create solutions for important problems. Predictive Modeling and Machine Learning with MATLAB may be useful for someone pursuing a career as a Data Scientist because it provides an introduction to many of the tools and concepts used in the field.
See salaries and explore the career path for Data Scientist
Software Engineer
Software Engineers design, develop, and maintain software applications. They use their knowledge of programming languages, software development methodologies, and cloud computing platforms to develop and implement software solutions for a variety of business needs. Individuals in this role often work with other stakeholders to develop and implement data-driven solutions to business problems. Predictive Modeling and Machine Learning with MATLAB may be useful for someone pursuing a career as a Software Engineer because it provides an introduction to many of the tools and concepts used in the field.
See salaries and explore the career path for Software Engineer
Business Analyst
Business Analysts use their knowledge of business processes, data analysis techniques, and software applications to identify and solve business problems. They use their skills to analyze data, develop recommendations, and implement solutions to improve business outcomes. Individuals in this role often work with other stakeholders to develop and implement data-driven solutions to business problems. Predictive Modeling and Machine Learning with MATLAB may be useful for someone pursuing a career as a Business Analyst because it provides an introduction to many of the tools and concepts used in the field.
See salaries and explore the career path for Business Analyst
Operations Research Analyst
Operations Research Analysts use their knowledge of mathematical modeling, optimization techniques, and data analysis techniques to solve complex business problems. They use their skills to develop and implement solutions for a variety of business problems, such as supply chain management, logistics, and scheduling. Individuals in this role often work with other stakeholders to develop and implement data-driven solutions to business problems. Predictive Modeling and Machine Learning with MATLAB may be useful for someone pursuing a career as an Operations Research Analyst because it provides an introduction to many of the tools and concepts used in the field.
See salaries and explore the career path for Operations Research Analyst
Quantitative Analyst
Quantitative Analysts use their knowledge of mathematical modeling, statistical methods, and computer programming to develop and implement financial models. They use their skills to analyze financial data, develop trading strategies, and manage investment portfolios. Individuals in this role often work with clients to develop and implement investment strategies. Predictive Modeling and Machine Learning with MATLAB may be useful for someone pursuing a career as a Quantitative Analyst because it provides an introduction to many of the tools and concepts used in the field.
See salaries and explore the career path for Quantitative Analyst
Machine Learning Engineer
Machine Learning Engineers use their knowledge of machine learning algorithms, programming languages, and cloud computing platforms to develop and implement machine learning models. They use their skills to train and deploy machine learning models, and to evaluate the performance of these models. Individuals in this role often work with other stakeholders to develop and implement data-driven solutions to business problems. Predictive Modeling and Machine Learning with MATLAB may be useful for someone pursuing a career as a Machine Learning Engineer because it provides an introduction to many of the tools and concepts used in the field.
See salaries and explore the career path for Machine Learning Engineer

Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Predictive Modeling and Machine Learning with MATLAB.
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The Elements of Statistical Learning
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Deep Learning: Foundations and Concepts
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Deep Learning
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Provides a comprehensive overview of deep learning. It covers a wide range of topics including neural networks, convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for anyone who wants to learn more about the theory and practice of deep learning.
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Reinforcement Learning, second edition
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The Molecular Basis of Cancer
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Machine Learning with R
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Provides a practical introduction to machine learning using R. It covers a wide range of topics including data preprocessing, feature selection, model training, and model evaluation. It valuable resource for anyone who wants to learn how to use R for machine learning.
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Rating
4.8 Filled star Filled star Filled star Filled star Half star
Effort
18 hours to complete
Level
Intermediate
Via
Coursera
Institution
MathWorks
Instructors
Maria Gavilan-Alfonso
Michael Reardon
Isaac Bruss
Brandon Armstrong
Brian Buechel
Nikola Trica
Matt Rich
Heather Gorr
Erin Byrne
Sam Jones
Adam Filion
Language
English
Transcripts
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This course belongs to a collection called Practical Data Science with MATLAB. It's comprised of four courses:
  1. Exploratory Data Analysis with MATLAB
  2. Data Processing and Feature Engineering with MATLAB
  3. Predictive Modeling and Machine Learning with MATLAB (viewing)
  4. Data Science Project: MATLAB for the Real World
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Focuses on applying supervised machine learning to address real-world problems
Provides a solid foundation in machine learning concepts and techniques
Provides hands-on experience through practical exercises and projects
Taught by experienced instructors with expertise in machine learning and data analysis
Recommended for individuals with some background in basic statistics and data analysis
May require additional resources or knowledge for those with limited programming background
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