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Predictive Modeling and Machine Learning with MATLAB

Approx. 22 hours to complete

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Course Summary

This course teaches the foundations of predictive modeling and machine learning, with a focus on practical applications in Python. Students will learn how to build and evaluate predictive models, as well as how to select and preprocess data for optimal performance.

Key Learning Points

Gain practical experience in building and evaluating predictive models
Learn how to select and preprocess data for optimal performance
Develop a solid understanding of machine learning concepts and techniques

Learning Outcomes

Build and evaluate predictive models using Python
Select and preprocess data for optimal performance
Understand and apply machine learning concepts and techniques

Prerequisites or good to have knowledge before taking this course

Basic programming knowledge in Python
Familiarity with linear algebra and statistics

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced

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Description

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.

Outline

Creating Regression Models
Practical Data Science with MATLAB
Instructor Introduction
Introduction to Supervised Machine Learning
Introduction to the Taxi Data
Creating and Cleaning Features
Introduction to Regression
Using the Regression Learner App
Customizing Model Parameters
Evaluating Regression Models
Evaluate Your Model in MATLAB
Summary of Regression
Download and Install MATLAB
Data and Code Files
Supervised Machine Learning Reference
Introduction to Module 1
Variables in the Taxi Data
Note regarding updates to MATLAB
Summary of Regression Models
Regression Metrics
Feature Engineering Review
Train a Regression Model
Apply the Regression Workflow

Creating Classification Models
Introduction to Classification
Using the Classification Learner App
Evaluating Classification Models
Evaluating Classification Models in MATLAB
Training a Multiclass Model
Summary of Classification
Introduction to Module 2
Note regarding updates to MATLAB
Summary of Classification Models
Binary Classification Metrics Reference
Evaluate and Customize Classification Models
Multiclass Classification Metrics Reference
Customizing Multiclass Models
Train a Classification Model
Apply The Classification Workflow

Applying the Supervised Machine Learning Workflow
Addressing Underfitting and Overfitting
Using Validation Data During Training
Embedded Methods for Feature Selection
Using Regularization to Prevent Overfitting
Introduction to Ensemble Models
Training Ensemble Models
Introduction to Hyperparameters
Optimizing Hyperparameters
Summary of Module 3
Introduction to Module 3
Examining Bias Variance Trade-off
Practice Partitioning Data
Using Wrapper Methods to Select Features
Introduction to the Course Project
Practice Reducing Model Complexity
Applying Ensemble Models

Advanced Topics and Next Steps
Handling Class Imbalance
Reducing Specific Errors Using Cost Matrices
Integrating Your Model
A Discussion with Heather
Summary of Predictive Modeling and Machine Learning
Introduction to Module 4
Sampling Data
Practice Handling Class Imbalance
Oversampling the Minority Class
Examples of Integrating Machine Learning Models
Automated Machine Learning
Provide Feedback on Your Course Experience
Practice Reducing Prediction Errors
Quiz: Advanced Topics and Next Steps

Summary of User Reviews

Discover the art of Predictive Modeling and Machine Learning with Coursera. This course has received positive feedback from users for its comprehensive curriculum and real-world applications. Many users found the course to be a great starting point for enhancing their skills in the field of data science.

Key Aspect Users Liked About This Course

The course has a comprehensive curriculum with real-world applications.

Pros from User Reviews

Great starting point for enhancing skills in data science.
Hands-on exercises and assignments help to reinforce concepts.
Instructors are knowledgeable and engaging.
The course is well-structured and easy to follow.
The course is self-paced, allowing users to take their time and review concepts as needed.

Cons from User Reviews

Some users found the course to be too basic.
The course can be challenging for those without a background in statistics or programming.
The course is quite lengthy and may require a significant time commitment.
Some users found the course to be too theoretical without enough practical applications.
The cost of the course may be prohibitive for some users.

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