# Mastering Machine Learning with MATLAB

Mastering Machine Learning with MATLAB
English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 2h 10m | 616 MB

Raise your Machine Learning skills with MATLAB

Save time and effort by learning efficient clustering and neural network techniques

MATLAB is the language of choice for many researchers and mathematics experts for Machine Learning. This video course will help you build a foundation in Machine Learning using MATLAB. You’ll start by performing data fitting, pattern recognition, and clustering analysis. Then, you’ll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. Finally, you will learn to put it all together through real-world cases covering major Machine Learning algorithms and will now be an expert in performing Machine Learning with MATLAB.

The video takes a very comprehensive approach to enhance your understanding of Machine Learning using MATLAB. Numerous real-world examples and use cases are included in the video to help you grasp the required concepts quickly and apply them easily in your day-to-day work.

What You Will Learn

• Discover different ways to transform data using SAS XPORT, import, and export tools.
• Discover the basics of classification methods and how to implement the Naive Bayes algorithm and Decision Trees in the MATLAB environment.
• Use clustering methods such as hierarchical clustering to group data using similarity measures.
• Perform data fitting, pattern recognition, and clustering analysis with the help of the MATLAB Neural Network Toolbox.
• Use feature selection and extraction for dimensionality reduction, leading to improved performance.

01 The Course Overview
02 Predicting a Response by Decision Trees
03 Probabilistic Classification Algorithms – Naive Bayes
04 Describing Differences by Discriminant Analysis
05 Find Similarities Using Nearest Neighbor Classifiers
06 Classification Learner App
07 Introduction to Clustering
08 Hierarchical Clustering
09 Partitioning-Based Clustering Methods – K-means Algorithm
10 Partitioning around the Actual Center – K-medoids Clustering
11 Clustering Using Gaussian Mixture Models
12 Getting Started with Neural Networks
13 Basic Elements of a Neural Network
14 Neural Network Toolbox
15 Exploring Neural Network Start GUI
16 Data Fitting with Neural Networks
17 Feature Selection
18 Feature Extraction