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What Is MATLAB Programming?

MATLAB® is a programming platform designed specifically for engineers and scientists to analyze and design systems and products that transform our world. The heart of MATLAB is the MATLAB language, a matrix-based language allowing the most natural expression of computational mathematics.

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Other Programming Languages Used For Data Scientist & Machine Learning

There are many other programming languages and Tools which used to do Data Science and Machine Learning Task. Here we can discussing these programming languages in which our expert also providing the Data Science and Machine Learning programming Help:

Python: Python is the import programming language which start career as a Data Science and Machine Learning. This is first choice of professionals which learn data science. Now a day it became most popular programming language in the field of data science.

Java:  Java is also used for machine learning projects but it is difficult to implement compare to Python programming language. Syntax of this programming language is complex compare to python so developer not choose this as a first choice.

R: R programming is also became most familiar with Data Science and Machine Learning expert. This is also choose by Data Science expert like a python. It is also simple like python and it provide lots of in-built libraries which make it easy to implement.

JavaScript: When we need to create advance level GUI Application related to Machine Learning and Data Science for model prediction then JavaScript used to create the front end design.

MATLAB: MATLAB is also used to predict the Data Science and Machine Learning models. Basically it used to advance level scientific calculations which is related to machine learning.

SCALA: Scala is used in Data processing, distributed computing, and web development. It powers the data engineering infrastructure of many companies. It also used by Data Scientist Developer.

PySpark: It used to handle the Big Data related task. When data is too long then right choice to implement PySpark. It also support the SQL so it make easy to execute the query.

Hive/HADOOP: It also used by Big Data expert to implement big data related task. Now a day most of industries which is working over past decades then data of these industries is too long. To handle these data Hive/HADOOP is the right choice of developer. 

Tkinter: This is the python tool which used to create GUI applications. It also used to create Data Science and Machine Learning Application.

Django/Flask: These are the python Framework which is used to create GUI Applications. If you are looking to hire expert which can do your machine learning web application then you can choose these frameworks.

Machine Learning Project Sample 1

Problem 1

1 Read the paper on Canny Edge Detection discussed in the class: http://kiwi.bridgeport.edu/cpeg585/CannyEdgeDetector.pdf

2. Read the handout on Harris Corner Detection discussed in the class: http://kiwi.bridgeport.edu/cpeg585/HarrisCornerDetection_PSULecture.pdf

The Python code for Canny Edge detection and Harris Corner detection is implemented in the opencv library. If you are using Visual Studio for Python development, then you need to install the following two via pip (Python Environment tab under the Solution Explorer):

 

pip install opencv-python

pip install opencv-contrib-python

import numpy as np
import cv2
from matplotlib import pyplot as plt
# Load a color image in grayscale
img = cv2.imread('d:/images/obama1.jpg',1) # 0 for gray, 1 for color
#cv2.imshow('image',img)


#---------------canny edge detection-------------
img = cv2.imread('d:/images/obama1.jpg',0)
edges = cv2.Canny(img,50,200)
plt.subplot(121),plt.imshow(img,cmap = 'gray')
plt.title('Original Image'), plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(edges,cmap = 'gray')
plt.title('Edge Image'), plt.xticks([]), plt.yticks([])
plt.show()


#-------------------------------------------------
#--------------------Harris Corner Detection------
filename = 'd:/images/chessboard.jpg'
img = cv2.imread(filename)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# find Harris corners
gray = np.float32(gray)
dst = cv2.cornerHarris(gray,2,3,0.04)
#result is dilated for marking the corners, not important
dst = cv2.dilate(dst,None)

# Threshold for an optimal value, it may vary depending on the image.
img[dst>0.01*dst.max()]=[0,0,255]
cv2.imshow('dst',img)


#-------------------------------------------------
cv2.waitKey(0)
cv2.destroyAllWindows()
The test images for Canny Edge and Harris corner are shown below:


canny edge detection.JPG

Problem 2

Outlier Removal using Exhaustive Evaluation

Suppose you are given a set of points in two shapes. Some of these points are outliers. We can remove these via exhaustive evaluation or via random sampling through a technique called RANSAC. The following partial code shows how you will test for adding and removing outliers. You need to write the code for the outlier removal button handler, so that the two shapes get aligned as shown in the right most part of the screen shot below. The approach will be to remove iteratively one pair of points, and see if it reduces the transformation cost.

 

 

 

 

 

 

 

 

 

 

 

 

 

Problem 3

Outlier Removal using Exhaustive Evaluation

Implement the PCA for ATT&T dataset. The dataset consists of 400 images of 40 different people – each having 10 images. The dataset has been divided into training and test sets with each having 200 images (5 for each person). You will implement the PCA on the training dataset, and test it on the “test” dataset and measure the face recognition accuracy. You will experiment on the number of Eigen faces e.g. 20, 30, 40, 50 and see the effect on the accuracy.

 

Use the Mapack library provided under the “Video lectures for 2019” link. Download this library again as a newer version has been uploaded.

Problem 4

a. Study the LDA (Linear Discriminant Analysis) Algorithm in the handout provided on the CPEG 585 site.

b) Create an implementation in C# for implementing the LDA Algorithm for Face Recognition.

 

c) Compare the performance of the LDA algorithm ion the ATT dataset with the PCA implementation that you did in Assignment#8

Problem 5

1. Recreate the ImageProcesing_2010 C# project provided to you under handouts link in the CPEG 585 web site. Study the code for different Image Processing needs.

 

2. Implement the Histogram Equilization according to the explanation in the Wikipedia article. https://en.wikipedia.org/wiki/Histogram_equalizatio

Outlier Removal using computer vision.JPG

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Useful ML Algorithms in MATLAB

Linear Regression

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