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Pratheerth Padman
In this course, you'll run through everything involved while working on a machine learning problem with MATLAB - from data cleaning to model training, which will elevate your understanding of one of the most popular career choices!
These days, data is ubiquitous and abundant. Analyzing that data and then subjecting it to machine learning models which in turn generate predictions has become essential. Entire startups have been built using this as the basis and established businesses use it to extend their capabilities.
Read more
In this course, you'll run through everything involved while working on a machine learning problem with MATLAB - from data cleaning to model training, which will elevate your understanding of one of the most popular career choices!
These days, data is ubiquitous and abundant. Analyzing that data and then subjecting it to machine learning models which in turn generate predictions has become essential. Entire startups have been built using this as the basis and established businesses use it to extend their capabilities.
Read more
In this course, you'll run through everything involved while working on a machine learning problem with MATLAB - from data cleaning to model training, which will elevate your understanding of one of the most popular career choices!
These days, data is ubiquitous and abundant. Analyzing that data and then subjecting it to machine learning models which in turn generate predictions has become essential. Entire startups have been built using this as the basis and established businesses use it to extend their capabilities.
There are many tools that are used to do this and MATLAB is one of the big ones. In this course, Perform Predictive Modeling with MATLAB, you'll learn the ins and outs of data analysis and predictive modeling through MATLAB.
First, you'll kick off the course with a brief introduction to the business problem that’ll be extended throughout the duration of the course. Next, you'll learn to prepare our dataset for analysis and modeling.
Then, you'll identify how to choose the correct algorithm for the business problem.
Finally, you’ll discover how to train and test the model on the dataset and then evaluate its performance.
When you’re finished with this course, you’ll have a solid understanding of how to work through a machine learning problem with MATLAB.
Register for this course and see more details by visiting:
OpenCourser.com/course/h1mppf/perform
What's inside
Syllabus
Course Overview
Preparing Data for Modeling
Choosing the Correct Algorithm
Training, Testing, and Evaluating the Model
Read more
Syllabus
Course Overview
Preparing Data for Modeling
Choosing the Correct Algorithm
Training, Testing, and Evaluating the Model
Read more
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for beginners who want to understand machine learning with MATLAB
Covers the entire machine learning workflow, from data cleaning to model evaluation
Real-world focus through a business problem that runs throughout the course
Expert instructors with experience in the field
May require prior programming knowledge for some learners
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Activities
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your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Perform Predictive Modeling with MATLAB with these
activities:
Review linear algebra and calculus concepts
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Strengthens foundational knowledge, enhances understanding of machine learning algorithms.
Browse courses on
Machine Learning
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-
Review textbooks, online resources, or video lectures on linear algebra and calculus.
-
Solve practice problems to test your understanding.
Compile notes, assignments, and practice problems in a central location
Show steps
Promotes organization, improves study habits, and facilitates efficient review.
Show steps
-
Create a dedicated folder or notebook for the course.
-
Regularly add notes from lectures, readings, and discussions.
-
Keep all assignments and practice problems organized within the folder.
Review matrix and matrix operations
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Reviewing matrix operations will help you better understand the linear algebra concepts used in this course.
Browse courses on
Matrices
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-
Identify the types of matrices and their properties.
-
Practice performing matrix operations, such as addition, subtraction, and multiplication.
-
Solve systems of linear equations using matrices.
Nine other activities
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Show all 12 activities
Watch tutorials on MATLAB data analysis
Show steps
Watching tutorials will help you understand the basics of data analysis in MATLAB.
Browse courses on
MATLAB
Show steps
-
Find a tutorial on MATLAB data analysis that covers the basics.
-
Watch the tutorial and take notes.
-
Practice the concepts you learned in the tutorial.
Review the book, Foundations of Machine Learning
Show steps
Provides a comprehensive overview of fundamental concepts and provides a sound reference text for future studies in machine learning.
View
Foundations of Machine Learning, second edition
on Amazon
Show steps
-
Read Chapter 1-4 to familiarize yourself with the introduction to machine learning, supervised learning, unsupervised learning, and model evaluation
-
Solving the practice problems at the end of each chapter.
Solve practice problems on machine learning algorithms
Show steps
Reinforce understanding of core concepts, identify areas of strength and weakness, prepare for assessments.
Browse courses on
Machine Learning
Show steps
-
Identify practice problems from textbooks, online resources, or problem sets.
-
Attempt to solve the problems without referring to solutions.
-
Review solutions and identify areas for improvement.
Participate in online tutorials on advanced machine learning techniques
Show steps
Extend and supplement knowledge gained during the course by exploring advanced concepts and techniques.
Browse courses on
Machine Learning
Show steps
-
Identify relevant tutorials based on interests and areas for improvement.
-
Complete the tutorials, taking notes and experimenting with the provided code.
-
Apply what you have learned to your own projects or assignments.
Solve coding challenges on LeetCode
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Solving coding challenges will help you improve your algorithm development and implementation skills.
Browse courses on
Coding
Show steps
-
Find a coding challenge on LeetCode that is relevant to the topic you are learning.
-
Attempt to solve the challenge on your own.
-
If you get stuck, refer to the discussion forum or online resources for help.
Organize a study group or join a peer-learning community
Show steps
Fosters collaboration, encourages multiple perspectives, and provides opportunities for peer feedback.
Browse courses on
Machine Learning
Show steps
-
Identify peers who are interested in forming a study group.
-
Establish regular meeting times and a communication channel.
-
Take turns presenting concepts, leading discussions, and solving problems together.
Create a data visualization using MATLAB
Show steps
Creating a data visualization will help you understand how to present data in a visually appealing and informative way.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset to visualize.
-
Select a type of visualization that is appropriate for the data.
-
Create the visualization using MATLAB.
Build a predictive model using a real-world dataset
Show steps
Practical application of concepts learned throughout the course to reinforce knowledge and skills.
Browse courses on
Machine Learning
Show steps
-
Identify the business problem and the relevant dataset.
-
Prepare the data for modeling, including cleaning, transformations, and feature engineering.
-
Explore different machine learning algorithms and select the most appropriate one for the problem.
-
Train and evaluate the model using cross-validation techniques.
-
Deploy the model and monitor its performance.
Contribute to open-source machine learning projects
Show steps
Gain practical experience, learn from experts, and contribute to the advancement of the field.
Browse courses on
Machine Learning
Show steps
-
Identify open-source machine learning projects that align with your interests.
-
Review the project documentation and code.
-
Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
-
Submit a pull request with your contributions.
Review linear algebra and calculus concepts
Show steps
Strengthens foundational knowledge, enhances understanding of machine learning algorithms.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review textbooks, online resources, or video lectures on linear algebra and calculus.
- Solve practice problems to test your understanding.
Compile notes, assignments, and practice problems in a central location
Show steps
Promotes organization, improves study habits, and facilitates efficient review.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for the course.
- Regularly add notes from lectures, readings, and discussions.
- Keep all assignments and practice problems organized within the folder.
Review matrix and matrix operations
Show steps
Reviewing matrix operations will help you better understand the linear algebra concepts used in this course.
Browse courses on
Matrices
Show steps
Show steps
Show steps
- Identify the types of matrices and their properties.
- Practice performing matrix operations, such as addition, subtraction, and multiplication.
- Solve systems of linear equations using matrices.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Watch tutorials on MATLAB data analysis
Show steps
Watching tutorials will help you understand the basics of data analysis in MATLAB.
Browse courses on
MATLAB
Show steps
Show steps
Show steps
- Find a tutorial on MATLAB data analysis that covers the basics.
- Watch the tutorial and take notes.
- Practice the concepts you learned in the tutorial.
Review the book, Foundations of Machine Learning
Show steps
Provides a comprehensive overview of fundamental concepts and provides a sound reference text for future studies in machine learning.
View
Foundations of Machine Learning, second edition
on Amazon
Show steps
Show steps
Show steps
- Read Chapter 1-4 to familiarize yourself with the introduction to machine learning, supervised learning, unsupervised learning, and model evaluation
- Solving the practice problems at the end of each chapter.
Solve practice problems on machine learning algorithms
Show steps
Reinforce understanding of core concepts, identify areas of strength and weakness, prepare for assessments.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify practice problems from textbooks, online resources, or problem sets.
- Attempt to solve the problems without referring to solutions.
- Review solutions and identify areas for improvement.
Participate in online tutorials on advanced machine learning techniques
Show steps
Extend and supplement knowledge gained during the course by exploring advanced concepts and techniques.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify relevant tutorials based on interests and areas for improvement.
- Complete the tutorials, taking notes and experimenting with the provided code.
- Apply what you have learned to your own projects or assignments.
Solve coding challenges on LeetCode
Show steps
Solving coding challenges will help you improve your algorithm development and implementation skills.
Browse courses on
Coding
Show steps
Show steps
Show steps
- Find a coding challenge on LeetCode that is relevant to the topic you are learning.
- Attempt to solve the challenge on your own.
- If you get stuck, refer to the discussion forum or online resources for help.
Organize a study group or join a peer-learning community
Show steps
Fosters collaboration, encourages multiple perspectives, and provides opportunities for peer feedback.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify peers who are interested in forming a study group.
- Establish regular meeting times and a communication channel.
- Take turns presenting concepts, leading discussions, and solving problems together.
Create a data visualization using MATLAB
Show steps
Creating a data visualization will help you understand how to present data in a visually appealing and informative way.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset to visualize.
- Select a type of visualization that is appropriate for the data.
- Create the visualization using MATLAB.
Build a predictive model using a real-world dataset
Show steps
Practical application of concepts learned throughout the course to reinforce knowledge and skills.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify the business problem and the relevant dataset.
- Prepare the data for modeling, including cleaning, transformations, and feature engineering.
- Explore different machine learning algorithms and select the most appropriate one for the problem.
- Train and evaluate the model using cross-validation techniques.
- Deploy the model and monitor its performance.
Contribute to open-source machine learning projects
Show steps
Gain practical experience, learn from experts, and contribute to the advancement of the field.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify open-source machine learning projects that align with your interests.
- Review the project documentation and code.
- Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
- Submit a pull request with your contributions.
Career center
Learners who complete Perform Predictive Modeling with MATLAB will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists develop statistical models and algorithms to extract patterns and insights from large volumes of data. Given your interest in developing predictive models with MATLAB, you will find this course quite relevant to this role. It covers key steps in the data science workflow, including preparing and cleaning data, selecting appropriate models, training and evaluating models, as well as evaluating their performance on datasets, which are all essential skills for a Data Scientist to master.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use their programming skills and knowledge of algorithms to design, develop and implement predictive models. This course will provide you with a strong foundation in the basics of machine learning with MATLAB. You'll learn about the concepts of data analysis, predictive modeling, and model evaluation, which are crucial for a Machine Learning Engineer to succeed in their role.
Data Analyst
Data Analyst
Data Analysts collect, process, and analyze large amounts of data to help businesses in various ways. This course will help you build a foundation in data preparation, model selection, and model evaluation. These skills are highly valued by Data Analysts as they are responsible for ensuring that data is accurate and can be used to make informed decisions.
Business Analyst
Business Analyst
Business Analysts use data to analyze business processes and identify areas for improvement. This course will provide you with an understanding of data analysis and predictive modeling techniques that are used by Business Analysts to support decision-making and drive business growth.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. This course will help you to develop a foundation in predictive modeling, which is essential for Quantitative Analysts to make informed investment decisions.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines that ensure data is available for analysis. This course will provide you with an understanding of data preparation, model training, and model evaluation concepts, which are essential for Data Engineers to ensure data quality and integrity.
Research Analyst
Research Analyst
Research Analysts use data analysis and predictive modeling techniques to gather and interpret data for use in making recommendations. This course will help you develop a foundation in data preparation, model selection, and evaluating model performance, which are essential skills for Research Analysts to conduct rigorous research and analysis.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course will help you to develop a foundation in predictive modeling techniques, which can be applied to various software development projects, such as building recommendation systems or fraud detection systems.
Product Manager
Product Manager
Product Managers oversee the development and launch of new products. This course will provide you with an understanding of data analysis and predictive modeling techniques, which are increasingly used by Product Managers to understand customer needs and make data-driven decisions.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. This course will help you develop a foundation in predictive modeling, which can be applied to various operations research problems, such as optimizing supply chain management or scheduling.
Financial Analyst
Financial Analyst
Financial Analysts use data to analyze and make recommendations on financial investments. This course will provide you with an understanding of data analysis and predictive modeling techniques, which are increasingly used by Financial Analysts to make informed investment decisions.
Consultant
Consultant
Consultants provide advice and guidance to organizations on various business issues. This course will help you develop a foundation in data analysis and predictive modeling techniques, which are increasingly used by Consultants to help organizations make data-driven decisions.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. This course will provide you with a foundation in data analysis and predictive modeling techniques, which are essential for Statisticians to conduct rigorous statistical analysis and draw meaningful conclusions.
Market Researcher
Market Researcher
Market Researchers conduct research to understand consumer behavior and market trends. This course will provide you with an understanding of data analysis and predictive modeling techniques, which are increasingly used by Market Researchers to conduct market analysis and make data-driven recommendations.
Data Scientist at a startup
Data Scientist at a startup
Data Scientists at startups play a crucial role in leveraging data to drive growth and innovation. This course will provide you with a solid foundation in data analysis, predictive modeling, and model evaluation, which are essential skills for Data Scientists at startups to succeed in their role.
For more career information including salaries, visit:
OpenCourser.com/course/h1mppf/perform
Reading list
We've selected 14 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
Perform Predictive Modeling with MATLAB.
This comprehensive textbook covers the fundamental concepts and algorithms of statistical learning, providing a solid foundation for understanding and applying predictive modeling techniques.
Save
Provides a comprehensive overview of machine learning, with a focus on using MATLAB for practical applications. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation.
Save
Covers a wide range of topics in data analysis and machine learning, with a focus on using MATLAB for practical applications. It provides a solid foundation in the underlying principles of machine learning and includes numerous examples and exercises.
Save
This classic textbook provides a comprehensive overview of data mining techniques, including data preparation, feature selection, and model evaluation, offering valuable insights for understanding the process of predictive modeling.
Designed specifically for users of MATLAB, this book provides practical examples and step-by-step instructions for implementing various machine learning algorithms using the MATLAB platform.
Save
Provides a practical introduction to deep learning using MATLAB. It covers the fundamentals of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks. It also includes numerous examples and exercises.
This advanced textbook delves into the theoretical foundations of statistical learning, providing a comprehensive understanding of the underlying principles and assumptions of predictive modeling.
Provides a comprehensive overview of machine learning for data science. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It also includes numerous case studies and exercises.
Focuses on the practical aspects of data analytics using MATLAB, covering techniques such as data visualization, feature engineering, and model deployment.
Provides a comprehensive overview of machine learning algorithms, covering both supervised and unsupervised learning techniques, offering additional insights into the mathematical foundations.
This advanced textbook provides a probabilistic approach to machine learning, covering topics such as Bayesian inference, graphical models, and reinforcement learning.
This comprehensive textbook covers the foundational concepts of statistical modeling, providing a solid theoretical background that complements the practical aspects covered in the course.
While focused on Python, this book provides valuable insights into machine learning algorithms and techniques, offering a broader perspective on predictive modeling.
This classic textbook covers the fundamental concepts and algorithms of reinforcement learning, providing insights into a specialized area of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/h1mppf/perform
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Time
71 hours
Level
Advanced
Via
Pluralsight
Instructor
Pratheerth Padman
Language
English
Good to know
Suitable for beginners who want to understand machine learning with MATLAB
Covers the entire machine learning workflow, from data cleaning to model evaluation
Real-world focus through a business problem that runs throughout the course
Expert instructors with experience in the field
May require prior programming knowledge for some learners
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