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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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/y8n5gl/predictive
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.
Read more
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.
Read more
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
Good to know
Good to know
Know what's good ,
what to watch for , 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
Save this course
Save Predictive Modeling and Machine Learning with MATLAB to your list so you can find it easily later:
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Reviews summary
Matlab: machine learning star
Learners say this course is a well-organized and engaging introduction to predictive modeling and machine learning for MATLAB users. MATLAB experts provide crystal-clear explanations and engaging assignments that feature real-world datasets to help students apply what they learn. The friendly instructors make difficult concepts easy to understand, so even beginners should be able to follow along. It may be difficult for some to keep pace due to the fast-paced nature of the course, and some report that the provided materials may have errors. Overall, learners say they highly recommend this course to students with basic familiarity with MATLAB.
Course is engaging and interesting.
"Course kept me interested."
"Easy to understand approach."
"I am so glad I did something new."
Instructors are MATLAB experts.
"Clear explanations of MATLAB concepts."
"Instructors have a good understanding of MATLAB."
"Mathworks team did it again."
Course is well-organized and structured.
"Great course, well organized."
"Overall, my experience with the course is good."
"Very organized lessons."
Course is fast-paced.
"Very fast paced."
"Difficult to keep pace."
"Some of the provided material does not work."
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Review basic statistics and linear algebra concepts
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
Refreshes basic understanding of linear regression techniques
Browse courses on
Linear Regression
Show steps
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
Boost your coding skills and understanding of MATLAB by participating in a study group with fellow learners.
Browse courses on
MATLAB
Show steps
Show steps
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
Show steps
Enhance your MATLAB skills by practicing data analysis and machine learning tasks in the MATLAB environment.
Browse courses on
MATLAB
Show steps
Show steps
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
Show steps
Reinforce your understanding of regression modeling by practicing with various datasets and scenarios.
Browse courses on
Regression Modeling
Show steps
Show steps
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
Show steps
Reinforces hands-on experience in building and evaluating classification models
Browse courses on
Classification
Show steps
Show steps
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
Show steps
Network and engage with professionals in the field, exchanging ideas and insights
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Research and find relevant meetups
- Attend the meetup and actively participate in discussions
Follow tutorials on specific machine learning techniques
Show steps
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
Show steps
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
Show steps
Deepen your understanding by explaining a machine learning concept or technique in a blog post or article.
Browse courses on
Machine Learning
Show steps
Show steps
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
Show steps
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
Show steps
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
Show steps
Develops understanding of the key steps and concepts in the supervised machine learning workflow
Show steps
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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 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.
Machine Learning Engineer
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.
Business Analyst
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.
Operations Research 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.
Statistician
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.
Software Engineer
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.
Financial 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.
Data Engineer
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.
Quantitative 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.
Actuary
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.
Data Scientist
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.
Research 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.
For more career information including salaries, visit:
OpenCourser.com/course/y8n5gl/predictive
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.
Classic in the field of machine learning. It provides a comprehensive overview of statistical learning methods and algorithms. It valuable resource for anyone who wants to learn more about the theory and practice of machine learning.
Save
Provides a comprehensive overview of pattern recognition and machine learning. It covers a wide range of topics including supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone who wants to learn more about the theory and practice of machine learning.
Save
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.
Provides a comprehensive overview of reinforcement learning. It covers a wide range of topics including Markov decision processes, value iteration, policy iteration, and Q-learning. It valuable resource for anyone who wants to learn more about the theory and practice of reinforcement learning.
Provides a practical introduction to machine learning using Python. 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 Python for machine learning.
Save
Provides a practical introduction to machine learning using MATLAB. 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 MATLAB for machine learning.
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.
Save
Provides a practical introduction to machine learning for musicians. 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 apply machine learning to music problems.
Provides a practical introduction to machine learning for non-programmers. 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 apply machine learning to real-world problems without having to learn how to code.
Save
Provides a practical introduction to machine learning for business professionals. 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 apply machine learning to business problems.
Save
Provides a fun and easy introduction to machine learning for kids. 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 teach their kids about machine learning.
Provides a gentle introduction to machine learning for beginners. 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 the basics of machine learning without getting too technical.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/y8n5gl/predictive
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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
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
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فارسی
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پښتو
آذربایجان دیلی
This course belongs to a
collection
called
Practical Data Science with MATLAB. It's comprised of four courses:
- Predictive Modeling and Machine Learning with MATLAB (viewing)
- Data Processing and Feature Engineering with MATLAB
- Exploratory Data Analysis with MATLAB
- Data Science Project: MATLAB for the Real World
Good to know
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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