Principal Component Analysis, or PCA, is a factual method that permits you to sum up the data contained in enormous information tables by methods for a littler arrangement of "synopsis files" that can be all the more handily envisioned and broke down.
Principal Component Analysis (PCA) and LDA PPT SlidesAbhishekKumar4995
Machine learning (ML) technique use for Dimension reduction, feature extraction and analyzing huge amount of data are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are easily and interactively explained with scatter plot graph , 2D and 3D projection of Principal components(PCs) for better understanding.
Principal Component Analysis (PCA) and LDA PPT SlidesAbhishekKumar4995
Machine learning (ML) technique use for Dimension reduction, feature extraction and analyzing huge amount of data are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are easily and interactively explained with scatter plot graph , 2D and 3D projection of Principal components(PCs) for better understanding.
Principal Component Analysis and ClusteringUsha Vijay
Identifying the borrower segments from the give bank data set which has 27000 rows and 77 variable using PROC PRINCOMP. variables, it is important to reduce the data set to a smaller set of variables to derive a feasible
conclusion. With the effect of multicollinearity two or more variables can share the same plane in the in dimensions. Each row of the data can
be envisioned as a 77 dimensional graph and when we project the data as orthonormal, it is expected that the certain characteristics of the
data based on the plots to cluster together as principal components. In order to identify these principal components. PROC PRINCOMP is
executed with all the variables except the constant variables(recoveries and collection fees) and we derive a plot of Eigen values of all the
principal components
In this presentation is given an introduction to Bayesian networks and basic probability theory. Graphical explanation of Bayes' theorem, random variable, conditional and joint probability. Spam classifier, medical diagnosis, fault prediction. The main software for Bayesian Networks are presented.
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
Intro to SVM with its maths and examples. Types of SVM and its parameters. Concept of vector algebra. Concepts of text analytics and Natural Language Processing along with its applications.
Principal Component Analysis and ClusteringUsha Vijay
Identifying the borrower segments from the give bank data set which has 27000 rows and 77 variable using PROC PRINCOMP. variables, it is important to reduce the data set to a smaller set of variables to derive a feasible
conclusion. With the effect of multicollinearity two or more variables can share the same plane in the in dimensions. Each row of the data can
be envisioned as a 77 dimensional graph and when we project the data as orthonormal, it is expected that the certain characteristics of the
data based on the plots to cluster together as principal components. In order to identify these principal components. PROC PRINCOMP is
executed with all the variables except the constant variables(recoveries and collection fees) and we derive a plot of Eigen values of all the
principal components
In this presentation is given an introduction to Bayesian networks and basic probability theory. Graphical explanation of Bayes' theorem, random variable, conditional and joint probability. Spam classifier, medical diagnosis, fault prediction. The main software for Bayesian Networks are presented.
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
Intro to SVM with its maths and examples. Types of SVM and its parameters. Concept of vector algebra. Concepts of text analytics and Natural Language Processing along with its applications.
Vectors Preparation Tips for IIT JEE | askIITiansaskiitian
Give your IIT JEE preparation a boost by delving into the world of vectors with the help of preparation tips for IIT JEE offered by askIITians. Read to know more….
This presentation educates you about top data science project ideas for Beginner, Intermediate and Advanced. the ideas such as Fake News Detection Using Python, Data Science Project on, Detecting Forest Fire, Detection of Road Lane Lines, Project on Sentimental Analysis, Speech Recognition, Developing Chatbots, Detection of Credit Card Fraud and Customer Segmentations etc:
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This presentation educate you about how to create table using Python MySQL with example syntax and Creating a table in MySQL using python.
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This presentation educates you about Python MySQL - Create Database and Creating a database in MySQL using python with sample program.
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This presentation educates you about Python MySQL - Database Connection, Python MySQL - Database Connection, Establishing connection with MySQL using python with sample program.
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This presentation educates you about Python MySQL, Python Database, What is mysql-connector-python?, How do I Install mysql-connector-python? and Installing mysql-connector-python with sample program.
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This presentation educates you about AI - Issues and the types of issue, AI - Terminology with its list of frequently used terms in the domain of AI.
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This presentation educates you about AI - Fuzzy Logic Systems and its Implementation, Why Fuzzy Logic?, Why Fuzzy Logic?, Membership Function, Example of a Fuzzy Logic System and its Algorithm.
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This presentation educates you about AI - Working of ANNs, Machine Learning in ANNs, Back Propagation Algorithm, Bayesian Networks (BN), Building a Bayesian Network and Gather Relevant Information of Problem.
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This presentation educates you about AI- Neural Networks, Basic Structure of ANNs with a sample of ANN and Types of Artificial Neural Networks are Feedforward and Feedback.
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This presentation educates you about Artificial Intelligence - Robotics, What is Robotics?, Difference in Robot System and Other AI Program, Robot Locomotion, Components of a Robot and Applications of Robotics.
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This presentation educates you about Applications of Expert System, Expert System Technology, Development of Expert Systems: General Steps and Benefits of Expert Systems.
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This presentation educates you about AI - Components and Acquisition of Expert Systems and those are Knowledge Base, Knowledge Base and User Interface, AI - Expert Systems Limitation.
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This presentation educates you about AI - Expert Systems, Characteristics of Expert Systems, Capabilities of Expert Systems and Components of Expert Systems.
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This presentation educates you about AI - Natural Language Processing, Components of NLP (NLU and NLG), Difficulties in NLU and NLP Terminology and steps of NLP.
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This presentation educates you about AI - Popular Search Algorithms, Single Agent Pathfinding Problems, Search Terminology, Brute-Force Search Strategies, Breadth-First Search and Depth-First Search with example chart.
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This presentation educates you about AI - Agents & Environments, Agent Terminology, Rationality, What is Ideal Rational Agent?, The Structure of Intelligent Agents and Properties of Environment.
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This presentation educates you about Artificial Intelligence - Research Areas, Speech and Voice Recognition., Working of Speech and Voice Recognition Systems and Real Life Applications of Research Areas.
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This presentation educates you about Artificial intelligence composed and those are Reasoning, Learning, Problem Solving, Perception and Linguistic Intelligence.
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This presentation educates you about Artificial Intelligence - Intelligent Systems, Types of Intelligence, Linguistic intelligence, Musical intelligence, Logical-mathematical intelligence, Spatial intelligence, Bodily-Kinesthetic intelligence, Intra-personal intelligence and Interpersonal intelligence.
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This presentation educates you about Applications of Artificial Intelligence such as Intelligent Robots, Handwriting Recognition, Speech Recognition, Vision Systems and so more.
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2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
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Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
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This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
2. Introduction
1. What is Standard Deviation?
2. What is Variance ?
The last two measures we have looked at are purely 1-dimensional.
However many datasets have more than one dimension, the aim of the statistical
analysis is to see if there is any relationship between these dimensions.
For example, we might have as our data set both the height of all the students in a
class, and the mark they received for that paper. We could then perform statistical
analysis to see if the height of a student has any effect on their mark.
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3. It is useful to have a measure to find out how much the dimensions vary from the
mean with respect to each other.
Covariance is such a measure. Covariance is always measured between 2
dimensions. If you calculate the covariance between one dimension and itself, you
get the variance
So, if you have a 3-dimensional data (x, y, z) then covariance will be calculated
Between x, y and y,z and z,x and x,x and y,y and z,z
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4. Covariance result
● Positive
● Negative
● Zero
Is Cov(x,y) same as Cov(y,x)?
What is Covariance Matrix?
For a 3-Dimensional dataset, we can calculate Covariance etween x, y and y,zand
z,x
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8. Eigenvectors
As you know, two matrices can be multiplied if they are compatible. Eigen vectors
are special case. When you multiply with Eigenvectors, the resulting vector will be
a scalar multiplication ofthat vector.
What does this signify ?
Let us look atan example
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9. The vector is a vector in 2 dimensional space. The vector (3 2 )T
(from the second example multiplication) represents an arrow pointing
from the origin (0,0) to (3 2)
The other matrix, the square one, can be thought of as a transformation matrix. If
you multiply this matrix on the left of a vector, the answer is another vector that is
transformed from it’s original position.
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14. Few properties of Eigenvectors
● Given a 3x3 matrix, there are 3 eigenvectors
● Another property of eigenvectors is that even if I scale the vector by some
amount before I multiply it, I still get the same multiple of it as a result, as in
Figure 2.3. This is because if you scale a vector by some amount, all you are
doing is making it longer
● All Eigenvectors of a matrix are orthogonal. This is important because, we can
express the data instead of x,y axes
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15. ● Another important thing to know is that when mathematicians find
eigenvectors, they like to find the eigenvectors whose length is exactly one.
This is because, as you know, the length of a vector doesn’t affect whether it’s
an eigenvector or not, whereas the direction does. So, in order to keep
eigenvectors standard, whenever we find an eigenvector we usually scale it to
make it have a length of 1, so that all eigenvectors have the same length
For example,
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16. Eigenvalues
Eigenvalues are closely related to Eigenvectors. Eigenvalue was 4 in our earlier
example. Notice even if v is scaled the eigenvalue still remains 4.
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17. PCA …finally!
It is a way of identifying patterns in data, and expressing the data in such a way
as to highlight their similarities and differences.
The other main advantage of PCA is that once you have found these patterns in
the data, and you compress the data, ie. by reducing the number of dimensions,
without much loss of information.
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21. Step: 4
Calculate the Eigenvectors andEigenvalues for the Covariance Matrix
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22. It is important to notice that these eigenvectors are both unit eigenvectors ie. their
lengths are both 1
If you look at the plot of the data in Figure 3.2 then you can see how the data has
quite a strong pattern. As expected from the covariance matrix, they two variables
do indeed increase together
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23. What information does Eigenvectorsgive?
See how one of the eigenvectors goes through the middle of the points, like
drawing a line of best fit? That eigenvector is showing us how these two data sets
are related along that line. The second eigenvector gives us the other, less
important, pattern in the data, that all the points follow the main line, but are off to
the side of the main line by some amount.
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25. Choosing the components
It turns out that the eigenvector with the highest eigenvalue is the principle
component of the data set. In our example, the eigenvector with the largest
eigenvalue was the one that pointed down the middle of the data. It is the most
significant relationship between the data dimensions
once eigenvectors are found from the covariance matrix, the next step is to order
them by eigenvalue, highest to lowest. This gives you the components in order of
significance.
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26. Now, if you like, you can decide to ignore the components of lesser significance.
You do lose some information, but if the eigenvalues are small, you don’t lose
much. If you leave out some components, the final data set will have less
dimensions than the original.
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