Last updated 2/2018MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 420.26 MB | Duration: 3h 59m

Level up your machine learning skills to extract patterns and knowledge from your data with ease using MATLAB

What you'll learn

Learn the introductory concepts of machine learning

Explore 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

Requirements

Basic knowledge MATLAB is needed

Basic mathematical and statistical background is assumed

Basic programming knowledge of C, C, Java, and Python is needed

Description

How do you deal with data that’s messy, incomplete, or in varied formats? How do you choose the right model for the data?

The solution to these questions is MATLAB.

MATLAB is the language of choice for many researchers and mathematics experts when it comes to machine learning. Eeers and data scientists work with large amounts of data in a variety of formats such as sensor, image, video, telemetry, databases, and much more. They use machine learning to find patterns in data and to build models that predict future outcomes based on historical data. With MATLAB, you have immediate access to prebuilt functions, extensive toolboxes, and specialized apps for classification, regression, and clustering. MATLAB is designed to give developers fluency in MATLAB programming language. Problem-based MATLAB examples have been given in simple and easy way to make your learning fast and effective. If you're interested to learn and implement powerful machine learning techniques, using MATLAB, then go for this Learning Path.

Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

The highlights of this Learning Path are

Explore the different types of regression techniques such as simple and multiple linear regression, ordinary least squares estimation, correlations, and how to apply them to your data

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.

Let’s take a quick look at your learning journey. This Learning Path will help you build a foundation in machine learning using MATLAB. You'll start by getting your system ready with the MATLAB environment for machine learning and see how to easily interact with the MATLAB workspace. You'll then move on to data cleansing, mining, and analyzing various data types in machine learning. You’ll also learn to display data values on a plot. Next, you'll learn about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. You'll also explore feature selection and extraction techniques for dimensionality reduction to improve performance. Finally, you’ll learn to put it all together through real-world use cases covering major machine learning algorithms and will now be an expert in perfog machine learning with MATLAB.

By the end of this Learning Path, you'll have acquired a complete knowledge on powerful machine learning techniques of MATLAB

Meet Your Expert

We have combined the best works of the following esteemed author to ensure that your learning journey is smooth

Giuseppe Ciaburro holds a PhD in environmental technical physics and two master's degrees. His research was focused on machine learning applications in the study of the urban sound environments. He works at Built Environment Control Laboratory - UniversitadegliStudidella Campania Luigi Vanvitelli (Italy). He has more than 15 years of work experience in programming (Python, R, and MATLAB), first in the field of combustion and then in acoustics and noise control. He has several publications to his credit.

Overview

Section 1: Getting Started with MATLAB Machine Learning

Lecture 1 The Course Overview

Lecture 2 Familiarizing Yourself with the MATLAB Desktop

Lecture 3 Importing Data into MATLAB

Lecture 4 Exporting Data from MATLAB

Lecture 5 Data Organization

Lecture 6 Data Preparation

Lecture 7 Exploratory Statistics – Numerical Measures

Lecture 8 Exploratory Visualization

Lecture 9 Searching Linear Relationships

Lecture 10 Creating a Linear Regression Model

Section 2: Mastering Machine Learning with MATLAB

Lecture 11 The Course Overview

Lecture 12 Predicting a Response by Decision Trees

Lecture 13 Probabilistic Classification Algorithms – Naive Bayes

Lecture 14 Describing Differences by Discriminant Analysis

Lecture 15 Find Similarities Using Nearest Neighbor Classifiers

Lecture 16 Classification Learner App

Lecture 17 Introduction to Clustering

Lecture 18 Hierarchical Clustering

Lecture 19 Partitioning-Based Clustering Methods – K-means Algorithm

Lecture 20 Partitioning around the Actual Center – K-medoids Clustering

Lecture 21 Clustering Using Gaussian Mixture Models

Lecture 22 Getting Started with Neural Networks

Lecture 23 Basic Elements of a Neural Network

Lecture 24 Neural Network Toolbox

Lecture 25 Exploring Neural Network Start GUI

Lecture 26 Data Fitting with Neural Networks

Lecture 27 Feature Selection

Lecture 28 Feature Extraction

This Learning Path is for data analysts, data scientists, students, or anyone keen to get started with machine learning added with MATLAB and build efficient data processing and predictive applications.

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