Tag: Matlab

Overview

Prescriptive analytics is a branch of business analytics, together with descriptive and predictive analytics. It uses predictive models to suggest actions to take for optimal outcomes, relying on optimization and rules-based techniques as a basis for decision making.

In this instructor-led, live training, participants will learn how to use Matlab to carry out prescriptive analytics on a set of sample data.

By the end of this training, participants will be able to:

• Understand the key concepts and frameworks used in prescriptive analytics
• Use MATLAB and its toolboxes to acquire, clean and explore data
• Use rules-based techniques including inference engines, scorecards, and decision trees to make decisions based on different business scenarios
• Use Monte Carlo simulation to analyze uncertainties and ensure sound decision making
• Deploy predictive and prescriptive models to enterprise systems

Audience

• Business analysts
• Operations planners
• Functional managers
• BI (Business Intelligence) team members

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Course Outline

To request a customized course outline for this training, please contact us.

Duration

21 hours (usually 3 days including breaks)

Requirements

• Experience with Matlab
• No previous experience with data science is required

Overview

Predictive analytics is the process of using data analytics to make predictions about the future. This process uses data along with data mining, statistics, and machine learning techniques to create a predictive model for forecasting future events.

In this instructor-led, live training, participants will learn how to use Matlab to build predictive models and apply them to large sample data sets to predict future events based on the data.

By the end of this training, participants will be able to:

• Create predictive models to analyze patterns in historical and transactional data
• Use predictive modeling to identify risks and opportunities
• Build mathematical models that capture important trends
• Use data from devices and business systems to reduce waste, save time, or cut costs

Audience

• Developers
• Engineers
• Domain experts

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Course Outline

Introduction

• Predictive analytics in finance, healthcare, pharmaceuticals, automotive, aerospace, and manufacturing

Overview of Big Data concepts

Capturing data from disparate sources

What are data-driven predictive models?

Overview of statistical and machine learning techniques

Case study: predictive maintenance and resource planning

Applying algorithms to large data sets with Hadoop and Spark

Predictive Analytics Workflow

Accessing and exploring data

Preprocessing the data

Developing a predictive model

Training, testing and validating a data set

Applying different machine learning approaches (time-series regression, linear regression, etc.)

Integrating the model into existing web applications, mobile devices, embedded systems, etc.

Matlab and Simulink integration with embedded systems and enterprise IT workflows

Creating portable C and C++ code from MATLAB code

Deploying predictive applications to large-scale production systems, clusters, and clouds

Acting on the results of your analysis

Next steps: Automatically responding to findings using Prescriptive Analytics

Closing remarks

Duration

14 hours (usually 2 days including breaks)

Requirements

• Experience with Matlab
• No previous experience with data science is required

Overview

In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition.

By the end of this training, participants will be able to:

• Build a deep learning model
• Automate data labeling
• Work with models from Caffe and TensorFlow-Keras
• Train data using multiple GPUs, the cloud, or clusters

Audience

• Developers
• Engineers
• Domain experts

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Course Outline

To request a customized course outline for this training, please contact us.

Theory of Bisection, Secant and Newton-Raphson Methods

Python Implementation of 3 Root Finding Methods

MATLAB Implementation of 3 Root Finding Methods

Requirements

• Basic Mathematics
• Python Programming
• MATLAB Programming

Description

This series of video tutorials covers the numerical methods for Root Finding (Solving Algebraic Equations) from theory to implementation. In this course, three methods are reviewed and implemented using Python and MATLAB from scratch.

At first, two interval-based methods, namely Bisection method and Secant method, are reviewed and implemented. Then, a point-based method which is known as Newton’s method for root finding, a.k.a. Newton–Raphson method, is reviewed and implemented. This course is instructed by Dr. Mostapha Kalami Heris, who has years of practical work and active teaching in the field of programming, mathematics, control engineering and computational intelligence.

By the end of this course you will be able to know about the fundamental theory of this root finding methods and implementing them using Python and MATLAB programming languages.

Who this course is for:

• Engineering Students
• Applied Math and Science Students
• Anyone interested in numerical methods

Course content

Theory of Principal Component Analysis (PCA)

Concept of Dimensionality Reduction

Step-by-step Implementation of PCA

PCA using Scikit-Learn (Python Library for Machine Learning)

PCA using MATLAB (Using Statistics and Machine Learning Toolbox)

Requirements

• Python Programming
• MATLAB Programming
• Basics of Data Analysis

Description

Principal Component Analysis (PCA) is an unsupervised learning algorithms and it is mainly used for dimensionality reduction, lossy data compression and feature extraction. It is the mostly used unsupervised learning algorithm in the field of Machine Learning.

In this video tutorial, after reviewing the theoretical foundations of Principal Component Analysis (PCA), this method is implemented step-by-step in Python and MATLAB. Also, PCA is performed on Iris Dataset and images of hand-written numerical digits, using Scikit-Learn (Python library for Machine Learning) and Statistics Toolbox of MATLAB. Also the projects files are available to download at the end of this post.

Who this course is for:

• Data Scientists and Analysts
• Computer Science and Engineering Students
• Anyone interested in Data Science

Course content

Implementation of Runge-Kutta in Python

Implementation of Runge-Kutta in MATLAB

Solving System of Nonlinear Differential Equations

Simulation of a Lotka-Volterra (Predator-Prey) System

Requirements

• Python and/or MATLAB Programming
• Differential Equations

Description

In this video tutorial, the theory of Runge-Kutta Method (RK4) for numerical solution of ordinary differential equations (ODEs), is discussed and then implemented using MATLAB and Python from scratch. As an example, the well-know Lotka-Volterra model (aka. the Predator-Prey model) is numerically simulated and solved using Runge-Kutta 4th order (RK4), in both languages, Python and MATLAB.

Who this course is for:

• Applied Math and Science Students
• Engineering Students
• Anyone Interested in Numerical Computation
• Software Engineers and Programmers

Course content

A Practical and Hands-on Approach

Requirements

• Basic Math and Optimization
• Python Programming
• MATLAB Programming

Description

Genetic Algorithms (GAs) are members of a general class of optimization algorithms, known as Evolutionary Algorithms (EAs), which simulate a fictional environment based on theory of evolution to deal with various types of mathematical problem, especially those related to optimization. Also Genetic Algorithms can be categorized as a subset of Metaheuristics, which are general-purpose tools and algorithms to solve optimization and unsupervised learning problems.

In this series of video tutorials, we are going to learn about Genetic Algorithms, from theory to implementation. After having a brief review of theories behind EA and GA, two main versions of genetic algorithms, namely Binary Genetic Algorithm and Real-coded Genetic Algorithm, are implemented from scratch and line-by-line, using both Python and MATLAB. This course is instructed by Dr. Mostapha Kalami Heris, who has years of practical work and active teaching in the field of computational intelligence.

Components of the genetic algorithms, such as initialization, parent selection, crossover, mutation, sorting and selection, are discussed in this tutorials, and backed by practical implementation. Theoretical concepts of these operators and components can be understood very well using this practical and hands-on approach.

At the end of this course, you will be fully familiar with concepts of evolutionary computation and will be able to implement genetic algorithms from scratch and also, utilize them to solve your own optimization problems.

Who this course is for:

• Computer Science Students
• Engineering and Applied Math Students
• Anyone interested in Optimization
• Anyone interested in Computational Intelligence
• Anyone interested in Metaheuristics
• Anyone interested in Evolutionary Computation

Course content

4 sections • 42 lectures • 4h 12m total length

About this Course

By the end of this course, learners will understand what computer vision is, as well as its mission of making computers see and interpret the world as humans do, by learning core concepts of the field and receiving an introduction to human vision capabilities. They are equipped to identify some key application areas of computer vision and understand the digital imaging process. The course covers crucial elements that enable computer vision: digital signal processing, neuroscience and artificial intelligence. Topics include color, light and image formation; early, mid- and high-level vision; and mathematics essential for computer vision. Learners will be able to apply mathematical techniques to complete computer vision tasks.

This course is ideal for anyone curious about or interested in exploring the concepts of computer vision. It is also useful for those who desire a refresher course in mathematical concepts of computer vision. Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (Mathworks provides the basics here: https://www.mathworks.com/learn/tutorials/matlab-onramp.html). Learners should also be familiar with the following: basic linear algebra (matrix vector operations and notation), 3D co-ordinate systems and transformations, basic calculus (derivatives and integration) and basic probability (random variables). Material includes online lectures, videos, demos, hands-on exercises, project work, readings and discussions. Learners gain experience writing computer vision programs through online labs using MATLAB* and supporting toolboxes. * A free license to install MATLAB for the duration of the course is available from MathWorks.

WHAT YOU WILL LEARN

• Understand what computer vision is and its goals
• Identify some of the key application areas of computer vision
• Understand the digital imaging process
• Apply mathematical techniques to complete computer vision tasks

SKILLS YOU WILL GAIN

• Computer Vision
• Computer Programming
• Matlab