The document discusses feature engineering for machine learning. It defines feature engineering as the process of transforming raw data into features that better represent the data and improve machine learning performance. Some key techniques discussed include feature selection, construction, transformation, and extraction. Feature construction involves generating new features from existing ones, such as calculating apartment area from length and breadth. Feature extraction techniques discussed are principal component analysis, which transforms correlated features into linearly uncorrelated components capturing maximum variance. The document provides examples and steps for principal component analysis.
This document discusses feature engineering, which involves transforming raw data into features that better represent the data for machine learning purposes. It covers various techniques used in feature engineering like feature selection, feature transformation, feature construction, and dimensionality reduction. Feature selection techniques like mutual information and correlation are used to identify relevant and non-redundant features. Feature transformation techniques like encoding categorical variables and binning continuous variables are also discussed. Dimensionality reduction techniques like principal component analysis and linear discriminant analysis are explained for reducing the number of features. The document provides examples and steps to apply various feature engineering techniques.
Statistical theory is a branch of mathematics and statistics that provides the foundation for understanding and working with data, making inferences, and drawing conclusions from observed phenomena. It encompasses a wide range of concepts, principles, and techniques for analyzing and interpreting data in a systematic and rigorous manner. Statistical theory is fundamental to various fields, including science, social science, economics, engineering, and more.
Feature extraction and selection are important techniques in machine learning. Feature extraction transforms raw data into meaningful features that better represent the data. This reduces dimensionality and complexity. Good features are unique to an object and prevalent across many data samples. Principal component analysis is an important dimensionality reduction technique that transforms correlated features into linearly uncorrelated principal components. This both reduces dimensionality and preserves information.
The document provides an overview of principal component analysis (PCA), including:
- PCA is a dimensionality reduction technique that transforms variables into uncorrelated principal components.
- The steps of PCA involve standardizing data, calculating the covariance matrix, and determining principal components through eigendecomposition of the covariance matrix.
- PCA reduces dimensionality while preserving as much information as possible, helping with issues like data storage, algorithm speed, and visualization. However, it does not directly interpret the original features.
This document provides an overview of machine learning concepts including feature selection, dimensionality reduction techniques like principal component analysis and singular value decomposition, feature encoding, normalization and scaling, dataset construction, feature engineering, data exploration, machine learning types and categories, model selection criteria, popular Python libraries, tuning techniques like cross-validation and hyperparameters, and performance analysis metrics like confusion matrix, accuracy, F1 score, ROC curve, and bias-variance tradeoff.
1) Interval classifiers are machine learning algorithms that originated in artificial intelligence research but are now being applied to database mining. They generate decision trees to classify data into intervals based on attribute values.
2) The author implemented the IC interval classifier algorithm and tested it on small datasets, finding higher classification errors than reported in literature due to small training set sizes. Parameter testing showed accuracy improved with larger training sets and more restrictive interval definitions.
3) While efficiency couldn't be fully tested, results suggest interval classifiers may perform well for database applications if further tuned based on dataset characteristics. More research is still needed on algorithm modifications and dynamic training approaches.
Feature engineering is an important step in machine learning that involves transforming raw data into features better suited for building models. It includes techniques like feature selection, extraction, transformation, encoding, and augmentation. Feature selection involves choosing the most relevant existing features, while extraction creates new features from existing ones. The goal is to improve model performance by reducing noise and bias from irrelevant or redundant features.
This document discusses feature engineering, which involves transforming raw data into features that better represent the data for machine learning purposes. It covers various techniques used in feature engineering like feature selection, feature transformation, feature construction, and dimensionality reduction. Feature selection techniques like mutual information and correlation are used to identify relevant and non-redundant features. Feature transformation techniques like encoding categorical variables and binning continuous variables are also discussed. Dimensionality reduction techniques like principal component analysis and linear discriminant analysis are explained for reducing the number of features. The document provides examples and steps to apply various feature engineering techniques.
Statistical theory is a branch of mathematics and statistics that provides the foundation for understanding and working with data, making inferences, and drawing conclusions from observed phenomena. It encompasses a wide range of concepts, principles, and techniques for analyzing and interpreting data in a systematic and rigorous manner. Statistical theory is fundamental to various fields, including science, social science, economics, engineering, and more.
Feature extraction and selection are important techniques in machine learning. Feature extraction transforms raw data into meaningful features that better represent the data. This reduces dimensionality and complexity. Good features are unique to an object and prevalent across many data samples. Principal component analysis is an important dimensionality reduction technique that transforms correlated features into linearly uncorrelated principal components. This both reduces dimensionality and preserves information.
The document provides an overview of principal component analysis (PCA), including:
- PCA is a dimensionality reduction technique that transforms variables into uncorrelated principal components.
- The steps of PCA involve standardizing data, calculating the covariance matrix, and determining principal components through eigendecomposition of the covariance matrix.
- PCA reduces dimensionality while preserving as much information as possible, helping with issues like data storage, algorithm speed, and visualization. However, it does not directly interpret the original features.
This document provides an overview of machine learning concepts including feature selection, dimensionality reduction techniques like principal component analysis and singular value decomposition, feature encoding, normalization and scaling, dataset construction, feature engineering, data exploration, machine learning types and categories, model selection criteria, popular Python libraries, tuning techniques like cross-validation and hyperparameters, and performance analysis metrics like confusion matrix, accuracy, F1 score, ROC curve, and bias-variance tradeoff.
1) Interval classifiers are machine learning algorithms that originated in artificial intelligence research but are now being applied to database mining. They generate decision trees to classify data into intervals based on attribute values.
2) The author implemented the IC interval classifier algorithm and tested it on small datasets, finding higher classification errors than reported in literature due to small training set sizes. Parameter testing showed accuracy improved with larger training sets and more restrictive interval definitions.
3) While efficiency couldn't be fully tested, results suggest interval classifiers may perform well for database applications if further tuned based on dataset characteristics. More research is still needed on algorithm modifications and dynamic training approaches.
Feature engineering is an important step in machine learning that involves transforming raw data into features better suited for building models. It includes techniques like feature selection, extraction, transformation, encoding, and augmentation. Feature selection involves choosing the most relevant existing features, while extraction creates new features from existing ones. The goal is to improve model performance by reducing noise and bias from irrelevant or redundant features.
This document discusses feature engineering, which is the process of transforming raw data into features that better represent the underlying problem for predictive models. It covers feature engineering categories like feature selection, feature transformation, and feature extraction. Specific techniques covered include imputation, handling outliers, binning, log transforms, scaling, and feature subset selection methods like filter, wrapper, and embedded methods. The goal of feature engineering is to improve machine learning model performance by preparing proper input data compatible with algorithm requirements.
1. Singular Value Decomposition (SVD) is a matrix factorization technique that decomposes a matrix into three other matrices.
2. SVD is primarily used for dimensionality reduction, information extraction, and noise reduction.
3. Key applications of SVD include matrix approximation, principal component analysis, image compression, recommendation systems, and signal processing.
EDAB Module 5 Singular Value Decomposition (SVD).pptxrajalakshmi5921
1. Singular Value Decomposition (SVD) is a matrix factorization technique that decomposes a matrix into three other matrices.
2. SVD is primarily used for dimensionality reduction, information extraction, and noise reduction.
3. Key applications of SVD include matrix approximation, principal component analysis, image compression, recommendation systems, and signal processing.
Dimensionality reduction techniques are used to reduce the number of features or variables in a dataset. This helps simplify models and improve performance. Principal component analysis (PCA) is a common technique that transforms correlated variables into linearly uncorrelated principal components. Other techniques include backward elimination, forward selection, filtering out low variance or highly correlated features. Dimensionality reduction benefits include reducing storage space, faster training times, and better visualization of data.
Dimensionality reduction techniques transform high-dimensional data into a lower-dimensional representation while retaining important information. Principal component analysis (PCA) is a common linear technique that projects data along directions of maximum variance to obtain principal components as new uncorrelated variables. It works by computing the covariance matrix of standardized data to identify correlations, then computes the eigenvalues and eigenvectors of the covariance matrix to identify the principal components that capture the most information with fewer dimensions.
Lecture 8 - Feature Engineering and Optimization, a lecture in subject module...Maninda Edirisooriya
This lesson covers the core data science related content required for applying ML. This was one of the lectures of a full course I taught in University of Moratuwa, Sri Lanka on 2023 second half of the year.
IRJET- Study and Evaluation of Classification Algorithms in Data MiningIRJET Journal
The document discusses classification algorithms in data mining. It describes classification as a supervised learning technique that predicts categorical class labels. Six classification algorithms are evaluated: Naive Bayes, neural networks, decision trees, random forests, support vector machines, and K-nearest neighbors. The algorithms are evaluated using metrics like accuracy, precision, recall, F1-score and time using the WEKA tool on various datasets. Building accurate and efficient classifiers is an important task in data mining.
The document discusses key concepts related to data structures and algorithms. It defines data as values or sets of values that can be organized hierarchically into fields, records, and files. Entities have attributes that can be assigned values. Related entities form entity sets. Data structures organize data through fields, records, and files while supporting operations like searching, insertion, and deletion. Algorithms are step-by-step processes to solve problems in a finite number of steps. The efficiency of algorithms is measured by time and space complexity.
This document discusses various machine learning concepts related to data processing, feature selection, dimensionality reduction, feature encoding, feature engineering, dataset construction, and model tuning. It covers techniques like principal component analysis, singular value decomposition, correlation, covariance, label encoding, one-hot encoding, normalization, discretization, imputation, and more. It also discusses different machine learning algorithm types, categories, representations, libraries and frameworks for model tuning.
Working with the data for Machine LearningMehwish690898
The document discusses various techniques for dimensionality reduction in machine learning. It explains that dimensionality reduction transforms high-dimensional data into a lower-dimensional representation while retaining important information. Techniques include feature selection, which selects a subset of relevant features, and feature extraction, which transforms existing features into a new set of features. Principal component analysis (PCA) is presented as a feature extraction method that finds new axes along which the data has maximum variance.
SPEECH CLASSIFICATION USING ZERNIKE MOMENTScscpconf
Speech recognition is very popular field of research and speech classification improves the performance
for speech recognition. Different patterns are identified using various characteristics or features of speech
to do there classification. Typical speech features set consist of many parameters like standard deviation,
magnitude, zero crossing representing speech signal. By considering all these parameters, system
computation load and time will increase a lot, so there is need to minimize these parameters by selecting
important features. Feature selection aims to get an optimal subset of features from given space, leading to
high classification performance. Thus feature selection methods should derive features that should reduce
the amount of data used for classification. High recognition accuracy is in demand for speech recognition
system. In this paper Zernike moments of speech signal are extracted and used as features of speech signal.
Zernike moments are the shape descriptor generally used to describe the shape of region. To extract
Zernike moments, one dimensional audio signal is converted into two dimensional image file. Then various
feature selection and ranking algorithms like t-Test, Chi Square, Fisher Score, ReliefF, Gini Index and
Information Gain are used to select important feature of speech signal. Performances of the algorithms are
evaluated using accuracy of classifier. Support Vector Machine (SVM) is used as the learning algorithm of
classifier and it is observed that accuracy is improved a lot after removing unwanted features.
With these components in place, we present the Data
Science Machine — an automated system for generating
predictive models from raw data. It starts with a relational
database and automatically generates features to be used
for predictive modeling.
What is Feature Engineering?
Feature engineering is the process of creating or selecting relevant
features from raw data to improve the performance of machine
learning models.
Feature engineering is the process of transforming raw data into
features that are suitable for machine learning models. In other
words, it is the process of selecting, extracting, and transforming the
most relevant features from the available data to build more accurate
and efficient machine learning models.
In the context of machine learning, features are individual measurable
properties or characteristics of the data that are used as inputs for the
learning algorithms. The goal of feature engineering is to transform the
raw data into a suitable format that captures the underlying patterns
and relationships in the data, thereby enabling the machine learning
model to make accurate predictions or classifications
This document discusses conceptual data modeling using the entity-relationship (ER) model. It defines key concepts of the ER model including entities, attributes, relationships, entity sets, relationship sets, keys, and ER diagrams. It explains how the ER model is used in the early conceptual design phase of database design to capture the essential data requirements and produce a conceptual schema that can be later mapped to a logical and physical database implementation.
This chapter discusses analysis and design modeling. It describes various analysis modeling approaches like structured analysis and object-oriented analysis. Structured analysis uses diagrams like data flow diagrams, entity-relationship diagrams, and state transition diagrams. Object-oriented analysis focuses on identifying classes, objects, attributes, and relationships. The chapter also covers data modeling concepts, flow-oriented modeling using data flow diagrams, scenario-based modeling with use cases, and developing behavioral models to represent system behavior. Analysis modeling creates representations of the system to understand requirements and lay the foundation for design.
This document provides an overview of machine learning using Python. It introduces machine learning applications and key Python concepts for machine learning like data types, variables, strings, dates, conditional statements, loops, and common machine learning libraries like NumPy, Matplotlib, and Pandas. It also covers important machine learning topics like statistics, probability, algorithms like linear regression, logistic regression, KNN, Naive Bayes, and clustering. It distinguishes between supervised and unsupervised learning, and highlights algorithm types like regression, classification, decision trees, and dimensionality reduction techniques. Finally, it provides examples of potential machine learning projects.
The document provides an overview of machine learning activities including data exploration, preprocessing, model selection, training and evaluation. It discusses exploring different data types like numerical, categorical, time series and text data. It also covers identifying and addressing data issues, feature engineering, selecting appropriate models for supervised and unsupervised problems, training models using methods like holdout and cross-validation, and evaluating model performance using metrics like accuracy, confusion matrix, F-measure etc. The goal is to understand the data and apply necessary steps to build and evaluate effective machine learning models.
This document provides guidance on using MATLAB to implement parameter estimation techniques such as output error and filter error methods. It describes using a "run object" in MATLAB to define the key components of a numerical simulation for parameter estimation of an aircraft constrained to 1 degree of freedom. The run object stores properties that specify the state and observation equations, number of states and parameters, and files needed to perform the simulation and parameter estimation. The document provides details on setting up and running the simulation, implementing the output error method for parameter estimation, and using continuation methods to analyze bifurcations of the system.
This document describes a Raspberry Pi-based health monitoring system that measures heartbeat and pulse using a Pulse Sensor. The system uses an ADS1115 ADC module to read analog voltage signals from the Pulse Sensor and send the data over I2C to the Raspberry Pi. Python code is used to analyze the sensor signals and calculate the heartbeat rate, which is displayed in Processing and also sent over serial to other devices. Circuit diagrams and instructions for installing required libraries and configuring the Raspberry Pi I2C and serial interfaces are provided.
1. The document discusses how AI and IoT can be used together in various industries like manufacturing, agriculture, transportation and healthcare.
2. In manufacturing, AI and IoT are used for predictive maintenance and quality control to reduce downtime and increase operational efficiency. Sensors in IoT devices collect data that AI analyzes to detect maintenance issues.
3. In agriculture, smart sensors monitor crop fields and automate irrigation, while AI and data analysis provide insights into crop health. AI is also used in autonomous vehicles, using sensors to navigate roads and share information.
4. In healthcare, AI and IoT are applied to remote patient monitoring, improving diagnostics, reducing wait times and tracking medical equipment.
More Related Content
Similar to Feature Engineering in Machine Learning
This document discusses feature engineering, which is the process of transforming raw data into features that better represent the underlying problem for predictive models. It covers feature engineering categories like feature selection, feature transformation, and feature extraction. Specific techniques covered include imputation, handling outliers, binning, log transforms, scaling, and feature subset selection methods like filter, wrapper, and embedded methods. The goal of feature engineering is to improve machine learning model performance by preparing proper input data compatible with algorithm requirements.
1. Singular Value Decomposition (SVD) is a matrix factorization technique that decomposes a matrix into three other matrices.
2. SVD is primarily used for dimensionality reduction, information extraction, and noise reduction.
3. Key applications of SVD include matrix approximation, principal component analysis, image compression, recommendation systems, and signal processing.
EDAB Module 5 Singular Value Decomposition (SVD).pptxrajalakshmi5921
1. Singular Value Decomposition (SVD) is a matrix factorization technique that decomposes a matrix into three other matrices.
2. SVD is primarily used for dimensionality reduction, information extraction, and noise reduction.
3. Key applications of SVD include matrix approximation, principal component analysis, image compression, recommendation systems, and signal processing.
Dimensionality reduction techniques are used to reduce the number of features or variables in a dataset. This helps simplify models and improve performance. Principal component analysis (PCA) is a common technique that transforms correlated variables into linearly uncorrelated principal components. Other techniques include backward elimination, forward selection, filtering out low variance or highly correlated features. Dimensionality reduction benefits include reducing storage space, faster training times, and better visualization of data.
Dimensionality reduction techniques transform high-dimensional data into a lower-dimensional representation while retaining important information. Principal component analysis (PCA) is a common linear technique that projects data along directions of maximum variance to obtain principal components as new uncorrelated variables. It works by computing the covariance matrix of standardized data to identify correlations, then computes the eigenvalues and eigenvectors of the covariance matrix to identify the principal components that capture the most information with fewer dimensions.
Lecture 8 - Feature Engineering and Optimization, a lecture in subject module...Maninda Edirisooriya
This lesson covers the core data science related content required for applying ML. This was one of the lectures of a full course I taught in University of Moratuwa, Sri Lanka on 2023 second half of the year.
IRJET- Study and Evaluation of Classification Algorithms in Data MiningIRJET Journal
The document discusses classification algorithms in data mining. It describes classification as a supervised learning technique that predicts categorical class labels. Six classification algorithms are evaluated: Naive Bayes, neural networks, decision trees, random forests, support vector machines, and K-nearest neighbors. The algorithms are evaluated using metrics like accuracy, precision, recall, F1-score and time using the WEKA tool on various datasets. Building accurate and efficient classifiers is an important task in data mining.
The document discusses key concepts related to data structures and algorithms. It defines data as values or sets of values that can be organized hierarchically into fields, records, and files. Entities have attributes that can be assigned values. Related entities form entity sets. Data structures organize data through fields, records, and files while supporting operations like searching, insertion, and deletion. Algorithms are step-by-step processes to solve problems in a finite number of steps. The efficiency of algorithms is measured by time and space complexity.
This document discusses various machine learning concepts related to data processing, feature selection, dimensionality reduction, feature encoding, feature engineering, dataset construction, and model tuning. It covers techniques like principal component analysis, singular value decomposition, correlation, covariance, label encoding, one-hot encoding, normalization, discretization, imputation, and more. It also discusses different machine learning algorithm types, categories, representations, libraries and frameworks for model tuning.
Working with the data for Machine LearningMehwish690898
The document discusses various techniques for dimensionality reduction in machine learning. It explains that dimensionality reduction transforms high-dimensional data into a lower-dimensional representation while retaining important information. Techniques include feature selection, which selects a subset of relevant features, and feature extraction, which transforms existing features into a new set of features. Principal component analysis (PCA) is presented as a feature extraction method that finds new axes along which the data has maximum variance.
SPEECH CLASSIFICATION USING ZERNIKE MOMENTScscpconf
Speech recognition is very popular field of research and speech classification improves the performance
for speech recognition. Different patterns are identified using various characteristics or features of speech
to do there classification. Typical speech features set consist of many parameters like standard deviation,
magnitude, zero crossing representing speech signal. By considering all these parameters, system
computation load and time will increase a lot, so there is need to minimize these parameters by selecting
important features. Feature selection aims to get an optimal subset of features from given space, leading to
high classification performance. Thus feature selection methods should derive features that should reduce
the amount of data used for classification. High recognition accuracy is in demand for speech recognition
system. In this paper Zernike moments of speech signal are extracted and used as features of speech signal.
Zernike moments are the shape descriptor generally used to describe the shape of region. To extract
Zernike moments, one dimensional audio signal is converted into two dimensional image file. Then various
feature selection and ranking algorithms like t-Test, Chi Square, Fisher Score, ReliefF, Gini Index and
Information Gain are used to select important feature of speech signal. Performances of the algorithms are
evaluated using accuracy of classifier. Support Vector Machine (SVM) is used as the learning algorithm of
classifier and it is observed that accuracy is improved a lot after removing unwanted features.
With these components in place, we present the Data
Science Machine — an automated system for generating
predictive models from raw data. It starts with a relational
database and automatically generates features to be used
for predictive modeling.
What is Feature Engineering?
Feature engineering is the process of creating or selecting relevant
features from raw data to improve the performance of machine
learning models.
Feature engineering is the process of transforming raw data into
features that are suitable for machine learning models. In other
words, it is the process of selecting, extracting, and transforming the
most relevant features from the available data to build more accurate
and efficient machine learning models.
In the context of machine learning, features are individual measurable
properties or characteristics of the data that are used as inputs for the
learning algorithms. The goal of feature engineering is to transform the
raw data into a suitable format that captures the underlying patterns
and relationships in the data, thereby enabling the machine learning
model to make accurate predictions or classifications
This document discusses conceptual data modeling using the entity-relationship (ER) model. It defines key concepts of the ER model including entities, attributes, relationships, entity sets, relationship sets, keys, and ER diagrams. It explains how the ER model is used in the early conceptual design phase of database design to capture the essential data requirements and produce a conceptual schema that can be later mapped to a logical and physical database implementation.
This chapter discusses analysis and design modeling. It describes various analysis modeling approaches like structured analysis and object-oriented analysis. Structured analysis uses diagrams like data flow diagrams, entity-relationship diagrams, and state transition diagrams. Object-oriented analysis focuses on identifying classes, objects, attributes, and relationships. The chapter also covers data modeling concepts, flow-oriented modeling using data flow diagrams, scenario-based modeling with use cases, and developing behavioral models to represent system behavior. Analysis modeling creates representations of the system to understand requirements and lay the foundation for design.
This document provides an overview of machine learning using Python. It introduces machine learning applications and key Python concepts for machine learning like data types, variables, strings, dates, conditional statements, loops, and common machine learning libraries like NumPy, Matplotlib, and Pandas. It also covers important machine learning topics like statistics, probability, algorithms like linear regression, logistic regression, KNN, Naive Bayes, and clustering. It distinguishes between supervised and unsupervised learning, and highlights algorithm types like regression, classification, decision trees, and dimensionality reduction techniques. Finally, it provides examples of potential machine learning projects.
The document provides an overview of machine learning activities including data exploration, preprocessing, model selection, training and evaluation. It discusses exploring different data types like numerical, categorical, time series and text data. It also covers identifying and addressing data issues, feature engineering, selecting appropriate models for supervised and unsupervised problems, training models using methods like holdout and cross-validation, and evaluating model performance using metrics like accuracy, confusion matrix, F-measure etc. The goal is to understand the data and apply necessary steps to build and evaluate effective machine learning models.
This document provides guidance on using MATLAB to implement parameter estimation techniques such as output error and filter error methods. It describes using a "run object" in MATLAB to define the key components of a numerical simulation for parameter estimation of an aircraft constrained to 1 degree of freedom. The run object stores properties that specify the state and observation equations, number of states and parameters, and files needed to perform the simulation and parameter estimation. The document provides details on setting up and running the simulation, implementing the output error method for parameter estimation, and using continuation methods to analyze bifurcations of the system.
This document describes a Raspberry Pi-based health monitoring system that measures heartbeat and pulse using a Pulse Sensor. The system uses an ADS1115 ADC module to read analog voltage signals from the Pulse Sensor and send the data over I2C to the Raspberry Pi. Python code is used to analyze the sensor signals and calculate the heartbeat rate, which is displayed in Processing and also sent over serial to other devices. Circuit diagrams and instructions for installing required libraries and configuring the Raspberry Pi I2C and serial interfaces are provided.
1. The document discusses how AI and IoT can be used together in various industries like manufacturing, agriculture, transportation and healthcare.
2. In manufacturing, AI and IoT are used for predictive maintenance and quality control to reduce downtime and increase operational efficiency. Sensors in IoT devices collect data that AI analyzes to detect maintenance issues.
3. In agriculture, smart sensors monitor crop fields and automate irrigation, while AI and data analysis provide insights into crop health. AI is also used in autonomous vehicles, using sensors to navigate roads and share information.
4. In healthcare, AI and IoT are applied to remote patient monitoring, improving diagnostics, reducing wait times and tracking medical equipment.
The document discusses various types of testing for Internet of Things (IoT) infrastructure. It covers component testing of devices, communications, and computing. It also discusses user experience testing, including usability, target audiences, and user behavior analysis. Finally, it discusses different types of infrastructure testing like integration testing, load testing, compatibility testing, and performance testing to evaluate how the IoT system performs under various conditions.
This document provides steps to deploy a web application using Azure DevOps. It outlines 35 steps to create an Azure DevOps project, clone a repository, commit code changes locally using Git, and push the code to the Azure DevOps repository. The aim is to deploy a web application by hosting HTML code in Azure DevOps. The result is that the web application is successfully deployed using Azure DevOps.
The document provides steps to create a website using the Drupal content management system (CMS). It outlines 31 steps to install Drupal, configure the database, download and extract Drupal files, set the default theme, add custom blocks of content, and place blocks in specific regions of the site. The result is a successfully created website using Drupal that can be viewed locally.
The document provides steps to launch an AWS RDS MySQL database instance and connect to it from a local MySQL database. It involves opening the AWS console, selecting RDS, choosing MySQL, configuring a free-tier database, providing credentials, copying the endpoint, and connecting the local MySQL database using the endpoint and credentials. The result is that queries can successfully be executed on the AWS RDS database instance.
This document provides steps to create an S3 bucket in AWS and upload and move files within the bucket. It begins by signing into the AWS console as a root user and searching for S3. It then outlines 23 steps to create a bucket, upload a file, and move that file into a new folder within the bucket. The overall aim is demonstrated - to create an S3 bucket and work with files within it.
Dr. M. Pyingkodi of the Department of MCA at Kongu Engineering College in Erode, Tamil Nadu, India wrote a document about working with AWS EC2 instances. The 14 step process began by opening a browser to search and select EC2 instances on AWS. It then covered downloading an RDP file, using a PEM file to decrypt passwords, installing remote desktop and a Python compiler on the instance, and writing and executing a C program to test the setup. The result was that the AWS instance was successfully created and a sample program was executed.
The document discusses Amazon Web Services (AWS), which provides cloud computing services including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). It describes key AWS services such as Amazon EC2 for virtual servers, S3 for object storage, EBS for block storage volumes, RDS for SQL databases, and CloudFront for content delivery. It also covers AWS features like scalability, security, and tools for monitoring and messaging.
This document discusses various aspects of cloud security including cloud security challenges, areas of concern in cloud computing, how to evaluate risks, cloud computing categories, the cloud security alliance, security service boundaries, responsibilities by service models, securing data, auditing and compliance, identity management protocols, and Windows Azure identity standards. It provides information on policies, controls, and technologies used to secure cloud environments, applications, and data.
This document discusses cloud computing concepts presented by Dr. M. Pyingkodi of Kongu Engineering College in India. It covers topics such as virtualization, service-oriented architecture, grid computing, utility computing, cloud service models (IaaS, PaaS, SaaS), deployment models, essential cloud concepts, cloud types, reference models, communication protocols, REST, composability, connecting to the cloud, applications services, and the Chromium OS. Examples of cloud providers and technologies are provided throughout the document.
This document provides an overview of supervised machine learning algorithms. It explains that supervised learning involves training a model on labeled data so it can predict the correct output for new input data. Some examples of supervised learning tasks include image classification, disease prediction, and spam detection. Classification algorithms are used for predicting categorical outputs, like dog vs cat images. Regression algorithms predict continuous outputs, like housing prices. Common classification algorithms mentioned are random forest, decision trees, logistic regression, and support vector machines. Linear regression is also discussed as a basic regression algorithm that finds a linear relationship between variables.
The document discusses various unsupervised learning techniques including clustering algorithms like k-means, k-medoids, hierarchical clustering and density-based clustering. It explains how k-means clustering works by selecting initial random centroids and iteratively reassigning data points to the closest centroid. The elbow method is described as a way to determine the optimal number of clusters k. The document also discusses how k-medoids clustering is more robust to outliers than k-means because it uses actual data points as cluster representatives rather than centroids.
This document discusses normalization in database management systems (DBMS). It defines normalization as a process of decomposing complex relations into simpler, stable relations to eliminate inconsistencies, redundancies, and anomalies during data modification. The document outlines several normal forms including 1NF, 2NF, 3NF, and BCNF, and provides examples to illustrate the conditions that make a relation qualified for each normal form. The goal of normalization is to minimize data redundancy, reduce update anomalies, and simplify the relational design.
Relational databases use relational algebra and relational calculus to manipulate data. Relational algebra consists of operations like select, project, join, and divide that take relations as inputs and outputs. Relational calculus specifies queries using predicates and quantifiers without describing how to retrieve data. Structured Query Language (SQL) is the standard language used to communicate with relational database management systems. SQL allows users to define schemas, retrieve, insert, update, and delete data.
This document discusses transaction processing in database management systems (DBMS). It describes the ACID properties that transactions must satisfy - atomicity, consistency, isolation, and durability. An example of a fund transfer transaction is provided to illustrate these properties. Concurrency control is discussed as a mechanism for allowing concurrent transactions while maintaining isolation. The concepts of schedules, conflicting instructions, conflict serializability, and view serializability are introduced for evaluating the correctness of concurrent transaction executions.
The document discusses Internet of Things (IoT) frameworks. It describes that an IoT framework consists of interconnected components like sensors, gateways, apps, and data/analytical platforms that enable machine-to-machine interactions by providing secure connectivity and reliable data transfer. Real-time Innovations and Cisco are mentioned as examples of IoT framework companies that provide connectivity software and platforms for industrial IoT systems. Salesforce's IoT cloud platform is also summarized, which uses Apache technologies like Kafka, Spark, and Cassandra to process and store IoT data.
The document discusses various real world applications of Internet of Things (IoT) technology. It describes how IoT is used in industrial settings to improve processes and productivity through automated equipment monitoring and predictive maintenance. It also discusses consumer IoT applications for personal devices and smart home appliances. Additional sections cover IoT applications in retail supply chain management, banking security and fraud detection, healthcare remote patient monitoring, transportation fleet management, agriculture environmental monitoring, energy use monitoring, smart cities infrastructure, and military command and control systems.
The document discusses Internet of Things (IoT) and provides an overview. It defines IoT as a network of physical objects embedded with sensors, software and network connectivity that enables the collection and exchange of data. IoT allows objects to be sensed and controlled remotely across existing network infrastructure, creating opportunities to directly integrate physical systems with computer-based systems. Common applications of IoT mentioned include smart homes, infrastructure management, industrial uses, healthcare, transportation and more.
The document discusses key concepts in database management including primary keys, candidate keys, alternate keys, and foreign keys. It defines primary keys as columns that uniquely identify rows in a table. Candidate keys are attributes that could serve as primary keys. Alternate keys are candidate keys that were not selected as the primary key. Foreign keys link data between tables by referencing the primary key of another table. Maintaining proper keys is important for uniquely identifying rows, enforcing data integrity, and establishing relationships between database tables.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
1. Machine Learning : Feature Engineering
Dr.M.Pyingkodi
Dept of MCA
Kongu Engineering College
Erode, Tamilnadu, India
2. Basics of Feature Engineering(FE)
• the process of translating a data set into features such that these features are able to represent
the data set more effectively and result in a better learning performance.
• refers to manipulation — addition, deletion, combination, mutation — of your data set to improve
machine learning model training, leading to better performance and greater accuracy.
• a part of the preparatory activities
• It is responsible for taking raw input data and converting that to well-aligned features which are
ready to be used by the machine learning models.
• FE encapsulates various data engineering techniques such as selecting relevant features, handling
missing data, encoding the data, and normalizing it.
• It has two major elements
❖ feature transformation
❖ feature subset selection
3. Feature
• A feature is an attribute of a data set that is used in a machine learning process.
• selection of the subset of features which are meaningful for machine learning is a sub-area of
feature engineering.
• The features in a data set are also called its dimensions.
• a data set having ‘n’ features is called an n-dimensional data set.
Class variable
- Species
predictor variables
5 dimensional dataset
4. Feature transformation
Apply a mathematical formula to a particular column(feature) and transform the values which are
useful for our further analysis.
Creating new features from existing features that may help in improving the model performance.
A set of features (m) to create a new feature set (n) while retaining as much information as possible
It has two major elements:
1. feature construction
2. feature extraction
Both are sometimes known as feature discovery
There are two distinct goals of feature transformation:
1. Achieving best reconstruction of the original features in the data set
2. Achieving highest efficiency in the learning task
5. Feature Construction
Involves transforming a given set of input features to generate a new set of more powerful features.
The data set has three features –
apartment length,
apartment breadth, and
price of the apartment.
If it is used as an input to a regression problem, such data can be training data for the regression model.
So given the training data, the model should be able to predict the price of an apartment whose price is not
known or which has just come up for sale.
However, instead of using length and breadth of the apartment as a predictor, it is much convenient and
makes more sense to use the area of the apartment, which is not an existing feature of the data set.
So such a feature, namely apartment area, can be added to the data set.
In other words, we transform the three- dimensional data set to a four-dimensional data set,
with the newly ‘discovered’ feature apartment area being added to the original data set.
6. Feature Construction
• when features have categorical value and machine learning needs numeric value inputs
• when features having numeric (continuous) values and need to be converted to ordinal values
• when text-specific feature construction needs to be done
Ordinal data are discrete integers that can be ranked or sorted
8. Encoding categorical (ordinal) variables
• The grade is an ordinal variable
with values A, B, C, and D.
• To transform this variable to a
numeric variable, we can create
a feature num_grade mapping a
numeric value against each
ordinal value.
• mapped to values 1, 2, 3, and 4
in the transformed variable
9. Transforming numeric (continuous) features to
categorical features
• As a real estate price category prediction,
which is a classification problem.
• In that case, we can ‘bin’ the numerical data
into multiple categories based on the data
range.
• In the context of the real estate price
prediction example, the original data set has a
numerical feature apartment_price
10. Text-specific feature construction
Text is arguably the most predominant medium of communication.
Text mining is an important area of research
unstructured nature of the data
All machine learning models need numerical data as input.
So the text data in the data sets need to be transformed into numerical features
EX:
Facebook or micro-blogging channels like Twitter or emails or short messaging services such as
Whatsapp, Text plays a major role in the flow of information.
Vectorization:
Turning text into vectors/ arrays
To turn text to integer (or boolean, or floating numbers) vectors.
vectors are lists with n positions.
11. Vectorization
I want to turn my text into data.
One-hot encoding for “I want to turn my text into data”
One-hot encoding for “I want my data”.
One hot encoding only treats values as “present” and “not present”.
12. Three major steps
1. Tokenize
In order to tokenize a corpus, the blank spaces and punctuations are used as delimiters to separate
out the words, or tokens
A corpus is a collection of authentic text or audio organized into datasets.
2. Count
Then the number of occurrences of each token is counted, for each document.
3. Normalize
Tokens are weighted with reducing importance when they occur in the majority of the documents.
A matrix is then formed with each token representing a column and a specific document of the
corpus representing each row.
Each cell contains the count of occurrence of the token in a specific document.
This matrix is known as a document-term matrix / term- document matrix
14. Feature Extraction
New features are created from a combination of original features.
Operators for combining the original features include
1. For Boolean features:
Conjunctions, Disjunctions, Negation, etc.
2. For nominal features:
Cartesian product, M of N, etc.
3. For numerical features:
Min, Max, Addition, Subtraction,Multiplication, Division, Average, Equivalence, Inequality, etc.
16. Principal Component Analysis
Dimensionality-reduction method.
has multiple attributes or dimensions – many of which might have similarity with each other.
EX: If the height is more, generally weight is more and vice versa
In PCA, a new set of features are extracted from the original features which are quite dissimilar in nature.
transforming a large set of variables into a smaller one.
reduce the number of variables of a data set, while preserving as much information as possible.
Objective:
1.The new features are distinct, i.e. the covariance between the new features, i.e. the principal components is
0.
2. The principal components are generated in order of the variability in the data that it captures. Hence, the
first principal component should capture the maximum variability, the second principal component should
capture the next highest variability etc.
3. The sum of variance of the new features or the principal components should be equal to the sum of
variance of the original features.
17. Principal Component Analysis
converts the observations of correlated features into a set of linearly uncorrelated features with the
help of orthogonal transformation
These new transformed features are called the Principal Components.
it contains the important variables and drops the least important variable.
Examples:
image processing, movie recommendation system, optimizing the power allocation in various
communication channels.
Eigen vectors are the principal components of the data set.
Eigenvectors and values exist in pairs: every eigen vector has a corresponding eigenvalue.
Eigen vector is the direction of the line (vertical, horizontal, 45 degrees etc.).
An eigenvalue is a number, telling you how much variance there is in the data in that direction,
Eigenvalue is a number telling you how spread out the data is on the line.
18. PCA algorithm Terms
Dimensionality
It is the number of features or variables present in the given dataset.
Correlation
It signifies that how strongly two variables are related to each other. Such as if one changes, the other
variable also gets changed. The correlation value ranges from -1 to +1.
Here, -1 occurs if variables are inversely proportional to each other, and +1 indicates that variables are
directly proportional to each other.
Orthogonal
It defines that variables are not correlated to each other, and hence the correlation between the pair of
variables is zero.
Eigenvectors
column vector.
Eigenvalue can be referred to as the strength of the transformation
Covariance Matrix
A matrix containing the covariance between the pair of variables is called the Covariance Matrix.
19. Steps for PCA algorithm
1. Standardizing the data
those variables with larger ranges will dominate over those with small ranges (For example, a
variable that ranges between 0 and 100 will dominate over a variable that ranges between 0 and
2. Covariance Matrix Calculation
how the variables of the input data set are varying from the mean with respect to each other.
if positive then : the two variables increase or decrease together (correlated)
if negative then : One increases when the other decreases (Inversely correlated)
20. Steps for PCA algorithm
3.Compute The Eigenvectors & Eigenvalues of The Covariance Matrix To Identify The Principal
Components
Principal components are new variables that are constructed as linear combinations or mixtures of
the initial variables.
These combinations are done in such a way that the new variables (i.e., principal components) are
uncorrelated and most of the information within the initial variables is squeezed or compressed
into the first components.
principal components represent the directions of the data that explain a maximal amount of
variance
21. 4. The eigenvector having the next highest eigenvalue represents the direction in which data has
the highest remaining variance and also orthogonal to the first direction. So this helps in
identifying the second principal component.
5. Like this, identify the top ‘k’ eigenvectors having top ‘k’ eigenvalues so as to get the ‘k’ principal
components.
eigenvectors of the Covariance matrix are actually the directions of the axes where there is the
most variance(most information) and that we call Principal Components.
eigenvalues are simply the coefficients attached to eigenvectors, which give the amount of
variance carried in each Principal Component.
Steps for PCA algorithm
22. • a matrix is a factorization of that matrix into three matrices.
• linear transformations
SVD of a matrix A (m × n) is a factorization of the form:
• two orthogonal matrices U and V and diagonal matrix D.
• where, U and V are orthonormal matrices
• U is an m × m unitary matrix,
• V is an n × n unitary matrix and
• Σ is an m × n rectangular diagonal matrix.
• The diagonal entries of Σ are known as singular values of matrix A.
• The columns of U and V are called the left-singular and right-singular vectors of matrix A
• The square roots of these eigenvalues are called singular values.
Singular Value Decomposition
23. 1. Patterns in the attributes are captured by the right-singular vectors, i.e.the columns of V.
2. Patterns among the instances are captured by the left-singular, i.e. the
columns of U.
3. Larger a singular value, larger is the part of the matrix A that it accounts for and its
associated vectors.
4. New data matrix with k’ attributes is obtained using the equation
D = D × [v , v , … , v ]
Thus, the dimensionality gets reduced to k
SVD of a data matrix : properties
24. PCA, is capture the data set variability.
PCA that calculates eigenvalues of the covariancematrix of the data set
LDA focuses on class separability.
To reduce the number of features to a more manageable number before classification.
commonly used for supervised classification problems
Separating the features based on class separability so as to avoid over-fitting of the machine
learning model.
• LDA calculates eigenvalues and eigenvectors within a class and inter-class scatter matrices.
Linear Discriminant Analysis
25. 1. Calculate the mean vectors for the individual classes.
2. Calculate intra-class and inter-class scatter matrices.
3. Calculate eigenvalues and eigenvectors for S and S , where S is the intra-class scatter matrix and S
is the inter-class scatter matrix
where, mi is the sample mean for each class, m is the overall mean of the data set,
Ni is the sample size of each class
4. Identify the top k’ eigenvectors having top k’ eigenvalues
Linear Discriminant Analysis
where, m is the mean vector of the i-th class