MathWorks Courses

Predictive Modeling and Machine Learning with MATLAB

4.8

Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIn 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.

MATLAB Essentials

5

Expand your data analysis and modeling skills in MATLAB, a programming and numeric computing platform used to analyze data, develop algorithms, and create models. Millions of engineers and scientists worldwide use MATLAB to study and build advanced applications in machine learning, deep learning, signal processing, communications, image processing, and control systems. They are shaping the future by modeling rockets that may someday take you into space, developing autonomous vehicles to travel safely and efficiently, and designing wave farms that harness the power of ocean waves to generate clean energy.In this course, you'll use MATLAB to examine real-world problems and answer questions like:How far does a blue whale swim each day?What is the favorite topping in a pizza shop?What is the ride quality of a car suspension?How does the magnitude of an earthquake impact the strength of a tsunami?What is the most expensive failure in a factory?MATLAB makes it easy to see results quickly, so there are no pre-requisites for the course. Whether you're auditing or a verified learner, you will have free access to MATLAB for the duration of the course. You will learn how to process, analyze, and visualize data collected nearly everywhere in today's digital workplace. You'll use powerful templates and auto-generated code to start experimenting immediately and quickly process similar data sets. And you'll gain the essential programming skills needed to perform these exciting tasks.Throughout the course, you'll have ample opportunities to practice your newly acquired skills – through auto-graded assignments, practice quizzes, interactive readings, and projects. By the end of the course, you'll be ready to analyze your own data sets and impress colleagues with word clouds, geographic plots, animations, and more.Additionally, this course will give you the skills you need to prepare for the MathWorks Certified MATLAB Associate exam. Certification verifies valuable transferable skills, sets you apart in the job market, and can help accelerate professional growth.

Take a Swing at Baseball Analytics: Explore Player Careers

0

Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeFormer Major League Baseball (MLB) player Matt Kata joins MathWorks to introduce you to data analysis using baseball statistics. By analyzing historic batting statistics, you will explore player careers and answer the question: When do great hitters peak in their career? In this project, you will work in MATLAB, a programming environment used by millions of engineers and scientists, and now MLB players! You’ll have access to pitching, batting, and defensive statistics dating back to 1871, enabling you to explore and answer a wide variety of questions. You will compute statistics like On-base Plus Slugging (OPS), visualize results, and filter data to highlight players that meet criteria you specify, such as the number of home runs. Whether you’re analyzing sports data, financial markets, or electric engine performance, you can apply the data analysis skills you learn in this project to many other fields and applications. So, step up to the plate and take a swing at MATLAB for data analysis.

Data Processing and Feature Engineering with MATLAB

4.6

Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIn this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB to lay the foundation required for predictive modeling.This intermediate-level course is useful to anyone who needs to combine data from multiple sources or times and has an interest in modeling.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 Exploratory Data Analysis with MATLAB. Throughout the course, you will merge data from different data sets and handle common scenarios, such as missing data.In the last module of the course, you will explore special techniques for handling textual, audio, and image data, which are common in data science and more advanced modeling. By the end of this course, you will learn how to visualize your data, clean it up and arrange it for analysis, and identify the qualities necessary to answer your questions.You will be able to visualize the distribution of your data and use visual inspection to address artifacts that affect accurate modeling.

Data Science Project: MATLAB for the Real World

0

Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeLike most subjects, practice makes perfect in Data Science. In the capstone project, you will apply the skills learned across courses in the Practical Data Science with MATLAB specialization to explore, process, analyze, and model data. You will choose your own pathway to answer key questions with the provided data.To complete the project, you must have mastery of the skills covered in other courses in the specialization.The project will test your ability to import and explore your data, prepare the data for analysis, train a predictive model, evaluate and improve your model, and communicate your results.

Exploratory Data Analysis with MATLAB

5

Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIn this course, you will learn to think like a data scientist and ask questions of your data.You will use interactive features in MATLAB to extract subsets of data and to compute statistics on groups of related data. You will learn to useMATLAB to automatically generate code so you can learn syntax as you explore.You will also use interactive documents, called live scripts,to capture the steps of your analysis, communicate the results, and provide interactive controls allowing others to experiment by selecting groups of data.These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background is required. To be successful in this course, you should have some knowledge of basic statistics (e.g., histograms, averages, standard deviation, curve fitting, interpolation). By the end of this course, you will be able to load data into MATLAB, prepare it for analysis, visualize it, perform basic computations, and communicate your results to others. In your last assignment, you will combine these skills to assess damages following a severe weather event and communicate a polished recommendation based on your analysis of the data.You will be able to visualize the location of these events on a geographic map and create sliding controls allowing you to quickly visualize how a phenomenon changes over time.