Description

Introduction

This course provides an in-depth understanding of machine learning techniques and their implementation using MATLAB. Participants will explore key machine learning algorithms, including supervised and unsupervised learning, and learn to design, train, and optimize neural networks. Emphasis will be placed on practical applications of machine learning in real-world problems, using MATLAB’s powerful tools for data analysis, visualization, and neural network creation. By the end of the course, learners will be able to build and deploy machine learning models to solve complex problems.

Prerequisites

1. Basic Knowledge of Programming – Familiarity with MATLAB or similar programming languages.
2. Understanding of Basic Machine Learning Concepts – General understanding of algorithms such as classification, regression, and clustering.
3. Mathematical Foundations – A basic understanding of linear algebra and calculus will be beneficial.

Table of Contents

1. Introduction to Machine Learning in MATLAB

• 1.1 What is Machine Learning?
• 1.2 Overview of MATLAB for Machine Learning
• 1.3 Getting Started with MATLAB for Data Analysis

2. Data Preprocessing and Feature Engineering

• 2.1 Cleaning and Preparing Data for Machine Learning(Ref: KNIME for Machine Learning: From Data Preprocessing to Model Deployment)
• 2.2 Feature Selection and Extraction
• 2.3 Data Normalization and Scaling

3. Supervised Learning Algorithms

• 3.1 Linear Regression and Logistic Regression
• 3.2 Decision Trees and Random Forests
• 3.3 Support Vector Machines (SVM)
• 3.4 K-Nearest Neighbors (KNN)

4. Unsupervised Learning Algorithms

• 4.1 Clustering with K-Means
• 4.2 Principal Component Analysis (PCA)
• 4.3 Anomaly Detection

5. Introduction to Neural Networks

• 5.1 Basics of Neural Networks
• 5.2 Types of Neural Networks: Feedforward, Convolutional, and Recurrent
• 5.3 Activation Functions and Backpropagation

6. Creating and Training Neural Networks in MATLAB

• 6.1 Building Neural Networks Using MATLAB
• 6.2 Training Neural Networks with Backpropagation
• 6.3 Hyperparameter Tuning and Optimization Techniques
• 6.4 Evaluating Model Performance: Accuracy, Precision, and Recall

7. Advanced Neural Network Architectures

• 7.1 Convolutional Neural Networks (CNNs) for Image Recognition
• 7.2 Recurrent Neural Networks (RNNs) for Sequence Data
• 7.3 Transfer Learning and Fine-Tuning Pretrained Models

8. Deploying Machine Learning Models

• 8.1 Exporting Trained Models for Production Use
• 8.2 Integrating Machine Learning Models into Applications
• 8.3 Real-Time Data Prediction and Model Updates

Conclusion

“Machine Learning with MATLAB & Creating Neural Networks” provides participants with the skills to implement powerful machine learning algorithms and neural networks using MATLAB. With a focus on practical, hands-on learning, this course prepares professionals to apply machine learning techniques to solve real-world challenges and effectively deploy models in business applications.

Reference