Introduction

Machine learning is a powerful tool for extracting insights from data and making predictions. In the field of data science, MATLAB provides a rich environment for building and deploying machine learning models. Its extensive libraries and toolboxes allow data scientists to quickly implement machine learning algorithms, visualize data, and analyze results effectively.

This course focuses on using MATLAB for data science, specifically applying machine learning techniques to real-world problems. You will learn how to preprocess data, select the right machine learning algorithms, and evaluate models using MATLAB’s tools. By the end of this course, you will be able to apply machine learning concepts to solve complex data problems and make data-driven decisions.

Prerequisites

• Basic knowledge of MATLAB
• Familiarity with fundamental data science and statistical concepts
• Understanding of basic machine learning concepts is a plus but not required

Table of Contents

1. Introduction to Machine Learning in MATLAB
   1.1 What is Machine Learning?
   1.2 Overview of MATLAB for Data Science
   1.3 Machine Learning Toolbox in MATLAB
   1.4 MATLAB Workflow for Machine Learning
   1.5 Installing and Setting Up MATLAB for Machine Learning
2. Data Preprocessing in MATLAB
   2.1 Importing Data into MATLAB
   2.2 Data Cleaning and Handling Missing Values
   2.3 Feature Selection and Feature Engineering
   2.4 Data Normalization and Scaling
   2.5 Splitting Data into Training and Testing Sets
3. Supervised Learning Algorithms
   3.1 Introduction to Supervised Learning
   3.2 Linear Regression for Predictive Modeling
   3.3 Logistic Regression for Classification Problems
   3.4 Decision Trees: Building and Visualizing Trees
   3.5 Support Vector Machines (SVM) for Classification
   3.6 K-Nearest Neighbors (k-NN) Algorithm
   3.7 Implementing Supervised Learning in MATLAB
4. Evaluating Supervised Learning Models
   4.1 Model Evaluation Metrics: Accuracy, Precision, Recall, F1-Score
   4.2 Confusion Matrix and ROC Curve
   4.3 Cross-Validation in MATLAB
   4.4 Hyperparameter Tuning and Grid Search
   4.5 Model Selection and Overfitting/Underfitting
5. Unsupervised Learning Algorithms
   5.1 What is Unsupervised Learning?
   5.2 Clustering Techniques: K-Means and Hierarchical Clustering
   5.3 Dimensionality Reduction with PCA (Principal Component Analysis)
   5.4 Anomaly Detection in Data
   5.5 Implementing Unsupervised Learning in MATLAB
6. Advanced Machine Learning Techniques
   6.1 Random Forests and Ensemble Learning
   6.2 Neural Networks and Deep Learning Basics
   6.3 Boosting and Bagging Algorithms (e.g., AdaBoost)
   6.4 Model Stacking and Meta-Learning
   6.5 Implementing Advanced Techniques in MATLAB
7. Deep Learning with MATLAB
   7.1 Introduction to Deep Learning
   7.2 Neural Networks for Classification and Regression
   7.3 Convolutional Neural Networks (CNNs) for Image Processing
   7.4 Recurrent Neural Networks (RNNs) for Time Series Forecasting
   7.5 Training and Fine-tuning Deep Learning Models in MATLAB
8. Deploying Machine Learning Models in MATLAB
   8.1 Saving and Loading Models in MATLAB
   8.2 Model Deployment with MATLAB Compiler
   8.3 Integrating Models into Real-Time Applications
   8.4 Exporting Models to Other Platforms(Ref: Scikit learn for Machine Learning with Supervised Methods )
   8.5 MATLAB Apps for Machine Learning Deployment
9. Project: Solving a Real-World Problem
   9.1 Defining a Data Science Problem
   9.2 Collecting and Preprocessing Data
   9.3 Building and Evaluating Models
   9.4 Model Selection and Optimization
   9.5 Presenting Results and Drawing Conclusions
10. Conclusion
    10.1 Summary of Key Concepts Learned
    10.2 Real-World Applications of Machine Learning
    10.3 Next Steps: Advanced Topics and Further Learning
    10.4 Resources and Communities for Continuous Improvement

Conclusion

Machine learning is a fundamental skill for data scientists, and MATLAB offers a powerful environment to apply various machine learning techniques to solve complex problems. Throughout this course, you’ve gained practical experience with supervised and unsupervised learning algorithms, advanced techniques such as neural networks and ensemble methods, and deep learning.

With the skills and techniques learned, you are equipped to build and evaluate machine learning models, process large datasets, and deploy solutions to real-world challenges. Whether for predictive analytics, classification, or anomaly detection, MATLAB provides the tools necessary to take your data science career to the next level.

Reference