Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed. There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning uses labeled training data to infer a function that maps inputs to outputs, unsupervised learning looks for hidden patterns in unlabeled data, and reinforcement learning allows an agent to learn from interaction with an environment through trial-and-error using feedback in the form of rewards. Some common machine learning algorithms include support vector machines, discriminant analysis, naive Bayes classification, and k-means clustering.
Machine learning was discussed including definitions, types, and examples. The three main types are supervised, unsupervised, and reinforcement learning. Supervised learning uses labeled training data to predict target variables for new data. Unsupervised learning identifies patterns in unlabeled data through clustering and association analysis. Reinforcement learning involves an agent learning through rewards and penalties as it interacts with an environment. Examples of machine learning applications were also provided.
The document provides an overview of concepts and topics to be covered in the MIS End Term Exam for AI and A2 on February 6th 2020, including: decision trees, classifier algorithms like ID3, CART and Naive Bayes; supervised and unsupervised learning; clustering using K-means; bias and variance; overfitting and underfitting; ensemble learning techniques like bagging and random forests; and the use of test and train data.
1) Machine learning involves analyzing data to find patterns and make predictions. It uses mathematics, statistics, and programming.
2) Key aspects of machine learning include understanding the business problem, collecting and preparing data, building and evaluating models, and different types of machine learning algorithms like supervised, unsupervised, and reinforcement learning.
3) Common machine learning algorithms discussed include linear regression, logistic regression, KNN, K-means clustering, decision trees, and handling issues like missing values, outliers, and feature engineering.
Machine learning Method and techniquesMarkMojumdar
In this article you will get various methods of machine learning and techniques.
More Details https://www.fossguru.com/machine-learning-methods-and-techniques/
This document discusses educational data mining and various methods used in EDM. It begins with an introduction to EDM, defining it as an emerging discipline concerned with exploring unique data from educational settings to better understand students and learning environments. It then outlines several common classes of EDM methods including information visualization, web mining, clustering, classification, outlier detection, association rule mining, sequential pattern mining, and text mining. The rest of the document focuses on specific EDM methods like prediction, clustering, relationship mining, discovery with models, and distillation of data for human judgment. It provides examples and explanations of how these methods are used in EDM.
This document provides an overview of machine learning. It begins with an introduction and discusses the basics, types (supervised, unsupervised, reinforcement learning), technologies, applications, and vision for the next few years. Key points covered include definitions of machine learning, examples of applications (search engines, spam filters, personalized recommendations), and descriptions of different problem types (classification, regression, clustering) and learning approaches (decision trees, neural networks, Bayesian methods).
This document provides an overview of machine learning. It begins with an introduction and definitions, explaining that machine learning allows computers to learn without being explicitly programmed by exploring algorithms that can learn from data. The document then discusses the different types of machine learning problems including supervised learning, unsupervised learning, and reinforcement learning. It provides examples and applications of each type. The document also covers popular machine learning techniques like decision trees, artificial neural networks, and frameworks/tools used for machine learning.
Machine Learning Interview Questions and AnswersSatyam Jaiswal
Practice Best Machine Learning Interview Questions and Answers for the best preparation of the machine learning interview. these questions are very popular and asked various times in machine learning interview.
Machine learning was discussed including definitions, types, and examples. The three main types are supervised, unsupervised, and reinforcement learning. Supervised learning uses labeled training data to predict target variables for new data. Unsupervised learning identifies patterns in unlabeled data through clustering and association analysis. Reinforcement learning involves an agent learning through rewards and penalties as it interacts with an environment. Examples of machine learning applications were also provided.
The document provides an overview of concepts and topics to be covered in the MIS End Term Exam for AI and A2 on February 6th 2020, including: decision trees, classifier algorithms like ID3, CART and Naive Bayes; supervised and unsupervised learning; clustering using K-means; bias and variance; overfitting and underfitting; ensemble learning techniques like bagging and random forests; and the use of test and train data.
1) Machine learning involves analyzing data to find patterns and make predictions. It uses mathematics, statistics, and programming.
2) Key aspects of machine learning include understanding the business problem, collecting and preparing data, building and evaluating models, and different types of machine learning algorithms like supervised, unsupervised, and reinforcement learning.
3) Common machine learning algorithms discussed include linear regression, logistic regression, KNN, K-means clustering, decision trees, and handling issues like missing values, outliers, and feature engineering.
Machine learning Method and techniquesMarkMojumdar
In this article you will get various methods of machine learning and techniques.
More Details https://www.fossguru.com/machine-learning-methods-and-techniques/
This document discusses educational data mining and various methods used in EDM. It begins with an introduction to EDM, defining it as an emerging discipline concerned with exploring unique data from educational settings to better understand students and learning environments. It then outlines several common classes of EDM methods including information visualization, web mining, clustering, classification, outlier detection, association rule mining, sequential pattern mining, and text mining. The rest of the document focuses on specific EDM methods like prediction, clustering, relationship mining, discovery with models, and distillation of data for human judgment. It provides examples and explanations of how these methods are used in EDM.
This document provides an overview of machine learning. It begins with an introduction and discusses the basics, types (supervised, unsupervised, reinforcement learning), technologies, applications, and vision for the next few years. Key points covered include definitions of machine learning, examples of applications (search engines, spam filters, personalized recommendations), and descriptions of different problem types (classification, regression, clustering) and learning approaches (decision trees, neural networks, Bayesian methods).
This document provides an overview of machine learning. It begins with an introduction and definitions, explaining that machine learning allows computers to learn without being explicitly programmed by exploring algorithms that can learn from data. The document then discusses the different types of machine learning problems including supervised learning, unsupervised learning, and reinforcement learning. It provides examples and applications of each type. The document also covers popular machine learning techniques like decision trees, artificial neural networks, and frameworks/tools used for machine learning.
Machine Learning Interview Questions and AnswersSatyam Jaiswal
Practice Best Machine Learning Interview Questions and Answers for the best preparation of the machine learning interview. these questions are very popular and asked various times in machine learning interview.
Supervised learning uses labeled training data to predict outcomes for new data. Unsupervised learning uses unlabeled data to discover patterns. Some key machine learning algorithms are described, including decision trees, naive Bayes classification, k-nearest neighbors, and support vector machines. Performance metrics for classification problems like accuracy, precision, recall, F1 score, and specificity are discussed.
This document discusses different machine learning paradigms including supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves predicting outputs given labeled inputs through regression or classification problems. Unsupervised learning finds patterns in unlabeled data through clustering. Reinforcement learning uses rewards and punishments to maximize desirable behaviors over time through trial-and-error interactions. Examples of applications are discussed such as predicting house prices, cancer diagnosis, voice separation, robot control, and web crawling.
Lecture #1: Introduction to machine learning (ML)butest
1. Machine learning (ML) is a subfield of artificial intelligence concerned with building computer programs that learn from data and improve their abilities to perform tasks.
2. ML programs build models from example data to predict future examples or describe relationships in the data. For example, an ML program given patient cases could predict diseases in new patients or describe relationships between diseases and symptoms.
3. There are different types of learning including supervised learning (classification, regression), unsupervised learning (clustering), and reinforcement learning (sequential decision making). The goal is to learn patterns in data and generalize to new examples.
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
1. Machine learning is a set of techniques that use data to build models that can make predictions without being explicitly programmed.
2. There are two main types of machine learning: supervised learning, where the model is trained on labeled examples, and unsupervised learning, where the model finds patterns in unlabeled data.
3. Common machine learning algorithms include linear regression, logistic regression, decision trees, support vector machines, naive Bayes, k-nearest neighbors, k-means clustering, and random forests. These can be used for regression, classification, clustering, and dimensionality reduction.
The document discusses supervised and unsupervised machine learning, with supervised learning using labeled training data to map inputs to outputs, while unsupervised learning discovers hidden patterns in unlabeled data. Supervised learning is more accurate but complex, using techniques like regression, classification and decision trees, while unsupervised techniques include clustering, association, and dimensionality reduction to group and structure unlabeled data.
Machine learning is a type of artificial intelligence that allows software to learn from data without being explicitly programmed. The document discusses several machine learning techniques including supervised learning algorithms like linear regression, logistic regression, decision trees, support vector machines, K-nearest neighbors, and Naive Bayes. Unsupervised learning algorithms covered include clustering techniques like K-means and hierarchical clustering. Applications of machine learning include spam filtering, fraud detection, image recognition, and medical diagnosis.
The document discusses machine learning concepts including supervised learning, unsupervised learning, and reinforcement learning. It describes several machine learning algorithms like decision trees, k-nearest neighbors, naive bayes, and support vector machines that are used in supervised learning. Unsupervised learning techniques like clustering, association, and k-means clustering are also covered. The document concludes that machine learning approaches can help with systematic reviews by assisting in document screening and improving reviewer agreement.
This was part of my inaugural lecture of Summer Internship on Machine Learning at NMAM Institute of Technology, Nitte on 7th June, 2018. A lot more than what was on this presentation was discussed. We spoke on the ethics of choices we make as developers, socio-cultural impact of AI and ML and the political repercussions of deploying ML and AI.
This document provides an overview of machine learning presented by Mr. Raviraj Solanki. It discusses topics like introduction to machine learning, model preparation, modelling and evaluation. It defines key concepts like algorithms, models, predictor variables, response variables, training data and testing data. It also explains the differences between human learning and machine learning, types of machine learning including supervised learning and unsupervised learning. Supervised learning is further divided into classification and regression problems. Popular algorithms for supervised learning like random forest, decision trees, logistic regression, support vector machines, linear regression, regression trees and more are also mentioned.
- Machine learning is a method of data analysis that automates analytical model building to understand and analyze patterns in data to make decisions without explicit programming. Common applications include virtual assistants, traffic predictions, fraud detection, and recommendations.
- There are two main types of machine learning - supervised learning, where the training data is labeled and the algorithm learns from examples to predict labels for new data, and unsupervised learning, where the training data is unlabeled and the algorithm looks for hidden patterns in the data.
- Bayesian decision theory provides a statistical framework for classification problems based on quantifying costs and probabilities to determine optimal predictions. It uses Bayes' rule to calculate the posterior probability of a class given predictor values.
This document provides an introduction to machine learning, including definitions, examples of tasks well-suited to machine learning, and different types of machine learning problems. It discusses how machine learning algorithms learn from examples to produce a program or model, and contrasts this with hand-coding programs. It also briefly covers supervised vs. unsupervised vs. reinforcement learning, hypothesis spaces, regularization, validation sets, Bayesian learning, and maximum likelihood learning.
Machine Learning jobs are one of the top emerging jobs in the industry currently, and standing out during an interview is key for landing your desired job. Here are some Machine Learning interview questions you should know about, if you plan to build a successful career in the field.
The document discusses machine learning and provides information about several key concepts:
1) Machine learning allows computer systems to learn from data without being explicitly programmed by using statistical techniques to identify patterns in large amounts of data.
2) There are three main approaches to machine learning: supervised learning which uses labeled data to build predictive models, unsupervised learning which finds patterns in unlabeled data, and reinforcement learning which learns from success and failures.
3) Effective machine learning requires balancing model complexity, amount of training data, and ability to generalize to new examples in order to avoid underfitting or overfitting the data. Learning algorithms aim to minimize these risks.
Gradient boosted trees are an ensemble machine learning technique that produces a prediction model as an ensemble of weak prediction models, typically decision trees. It builds models sequentially to minimize a loss function using gradient descent. Each new model is fit to the negative gradient of the loss function to reduce error. This allows weak learners to be combined into a stronger learner with better predictive performance than a single decision tree. Key advantages are it is fast, easy to tune, and achieves good performance.
This is a presentation about Gradient Boosted Trees which starts from the basics of Data Mining, building up towards Ensemble Methods like Bagging,Boosting etc. and then building towards Gradient Boosted Trees.
Hrjeet Singh completed a 42-day online industrial training from Internshala located in Gurgaon, India. During the training, Singh learned about machine learning concepts including classification, regression, linear regression, logistic regression, decision trees, and K-means clustering. Singh also completed a project using machine learning classifiers to detect breast cancer by analyzing features of breast cancer patient and normal cells.
This document discusses computational intelligence and supervised learning techniques for classification. It provides examples of applications in medical diagnosis and credit card approval. The goal of supervised learning is to learn from labeled training data to predict the class of new unlabeled examples. Decision trees and backpropagation neural networks are introduced as common supervised learning algorithms. Evaluation methods like holdout validation, cross-validation and performance metrics beyond accuracy are also summarized.
Machine learning involves using algorithms to learn from data and make predictions. There are different types of machine learning problems including supervised learning (classification and regression), unsupervised learning (clustering and dimensionality reduction), and reinforcement learning. Supervised learning involves predicting outcomes based on labeled training data, while unsupervised learning finds patterns in unlabeled data. The document provides definitions and examples to explain machine learning concepts such as learning types, variables, linear separability, and the generalization process.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Supervised learning uses labeled training data to predict outcomes for new data. Unsupervised learning uses unlabeled data to discover patterns. Some key machine learning algorithms are described, including decision trees, naive Bayes classification, k-nearest neighbors, and support vector machines. Performance metrics for classification problems like accuracy, precision, recall, F1 score, and specificity are discussed.
This document discusses different machine learning paradigms including supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves predicting outputs given labeled inputs through regression or classification problems. Unsupervised learning finds patterns in unlabeled data through clustering. Reinforcement learning uses rewards and punishments to maximize desirable behaviors over time through trial-and-error interactions. Examples of applications are discussed such as predicting house prices, cancer diagnosis, voice separation, robot control, and web crawling.
Lecture #1: Introduction to machine learning (ML)butest
1. Machine learning (ML) is a subfield of artificial intelligence concerned with building computer programs that learn from data and improve their abilities to perform tasks.
2. ML programs build models from example data to predict future examples or describe relationships in the data. For example, an ML program given patient cases could predict diseases in new patients or describe relationships between diseases and symptoms.
3. There are different types of learning including supervised learning (classification, regression), unsupervised learning (clustering), and reinforcement learning (sequential decision making). The goal is to learn patterns in data and generalize to new examples.
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
1. Machine learning is a set of techniques that use data to build models that can make predictions without being explicitly programmed.
2. There are two main types of machine learning: supervised learning, where the model is trained on labeled examples, and unsupervised learning, where the model finds patterns in unlabeled data.
3. Common machine learning algorithms include linear regression, logistic regression, decision trees, support vector machines, naive Bayes, k-nearest neighbors, k-means clustering, and random forests. These can be used for regression, classification, clustering, and dimensionality reduction.
The document discusses supervised and unsupervised machine learning, with supervised learning using labeled training data to map inputs to outputs, while unsupervised learning discovers hidden patterns in unlabeled data. Supervised learning is more accurate but complex, using techniques like regression, classification and decision trees, while unsupervised techniques include clustering, association, and dimensionality reduction to group and structure unlabeled data.
Machine learning is a type of artificial intelligence that allows software to learn from data without being explicitly programmed. The document discusses several machine learning techniques including supervised learning algorithms like linear regression, logistic regression, decision trees, support vector machines, K-nearest neighbors, and Naive Bayes. Unsupervised learning algorithms covered include clustering techniques like K-means and hierarchical clustering. Applications of machine learning include spam filtering, fraud detection, image recognition, and medical diagnosis.
The document discusses machine learning concepts including supervised learning, unsupervised learning, and reinforcement learning. It describes several machine learning algorithms like decision trees, k-nearest neighbors, naive bayes, and support vector machines that are used in supervised learning. Unsupervised learning techniques like clustering, association, and k-means clustering are also covered. The document concludes that machine learning approaches can help with systematic reviews by assisting in document screening and improving reviewer agreement.
This was part of my inaugural lecture of Summer Internship on Machine Learning at NMAM Institute of Technology, Nitte on 7th June, 2018. A lot more than what was on this presentation was discussed. We spoke on the ethics of choices we make as developers, socio-cultural impact of AI and ML and the political repercussions of deploying ML and AI.
This document provides an overview of machine learning presented by Mr. Raviraj Solanki. It discusses topics like introduction to machine learning, model preparation, modelling and evaluation. It defines key concepts like algorithms, models, predictor variables, response variables, training data and testing data. It also explains the differences between human learning and machine learning, types of machine learning including supervised learning and unsupervised learning. Supervised learning is further divided into classification and regression problems. Popular algorithms for supervised learning like random forest, decision trees, logistic regression, support vector machines, linear regression, regression trees and more are also mentioned.
- Machine learning is a method of data analysis that automates analytical model building to understand and analyze patterns in data to make decisions without explicit programming. Common applications include virtual assistants, traffic predictions, fraud detection, and recommendations.
- There are two main types of machine learning - supervised learning, where the training data is labeled and the algorithm learns from examples to predict labels for new data, and unsupervised learning, where the training data is unlabeled and the algorithm looks for hidden patterns in the data.
- Bayesian decision theory provides a statistical framework for classification problems based on quantifying costs and probabilities to determine optimal predictions. It uses Bayes' rule to calculate the posterior probability of a class given predictor values.
This document provides an introduction to machine learning, including definitions, examples of tasks well-suited to machine learning, and different types of machine learning problems. It discusses how machine learning algorithms learn from examples to produce a program or model, and contrasts this with hand-coding programs. It also briefly covers supervised vs. unsupervised vs. reinforcement learning, hypothesis spaces, regularization, validation sets, Bayesian learning, and maximum likelihood learning.
Machine Learning jobs are one of the top emerging jobs in the industry currently, and standing out during an interview is key for landing your desired job. Here are some Machine Learning interview questions you should know about, if you plan to build a successful career in the field.
The document discusses machine learning and provides information about several key concepts:
1) Machine learning allows computer systems to learn from data without being explicitly programmed by using statistical techniques to identify patterns in large amounts of data.
2) There are three main approaches to machine learning: supervised learning which uses labeled data to build predictive models, unsupervised learning which finds patterns in unlabeled data, and reinforcement learning which learns from success and failures.
3) Effective machine learning requires balancing model complexity, amount of training data, and ability to generalize to new examples in order to avoid underfitting or overfitting the data. Learning algorithms aim to minimize these risks.
Gradient boosted trees are an ensemble machine learning technique that produces a prediction model as an ensemble of weak prediction models, typically decision trees. It builds models sequentially to minimize a loss function using gradient descent. Each new model is fit to the negative gradient of the loss function to reduce error. This allows weak learners to be combined into a stronger learner with better predictive performance than a single decision tree. Key advantages are it is fast, easy to tune, and achieves good performance.
This is a presentation about Gradient Boosted Trees which starts from the basics of Data Mining, building up towards Ensemble Methods like Bagging,Boosting etc. and then building towards Gradient Boosted Trees.
Hrjeet Singh completed a 42-day online industrial training from Internshala located in Gurgaon, India. During the training, Singh learned about machine learning concepts including classification, regression, linear regression, logistic regression, decision trees, and K-means clustering. Singh also completed a project using machine learning classifiers to detect breast cancer by analyzing features of breast cancer patient and normal cells.
This document discusses computational intelligence and supervised learning techniques for classification. It provides examples of applications in medical diagnosis and credit card approval. The goal of supervised learning is to learn from labeled training data to predict the class of new unlabeled examples. Decision trees and backpropagation neural networks are introduced as common supervised learning algorithms. Evaluation methods like holdout validation, cross-validation and performance metrics beyond accuracy are also summarized.
Machine learning involves using algorithms to learn from data and make predictions. There are different types of machine learning problems including supervised learning (classification and regression), unsupervised learning (clustering and dimensionality reduction), and reinforcement learning. Supervised learning involves predicting outcomes based on labeled training data, while unsupervised learning finds patterns in unlabeled data. The document provides definitions and examples to explain machine learning concepts such as learning types, variables, linear separability, and the generalization process.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
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.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
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
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
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.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
2. What is MACHINE LEARNING ?....
Not a well defined definition. But
Arthur Samuel (1959):
Machine learning: "Field of study that gives computers the ability to learn
without being explicitly programmed"
Samuel wrote a checkers playing program
Had the program play 10000 games against itself
Work out which board positions were good and bad depending on
wins/losses
Example
3. Tom Michel (1999):
Well posed learning problem: "A computer program is said to learn from
experience E with respect to some class of tasks T and performance measure P, if
its performance at tasks in T, as measured by P, improves with experience E."
An other definition……
The checkers example,
E = 10000s games
T is playing checkers
P if you win or loss
7. Supervised Learning (Train me)
It is a data mining task of inferring a function from labeled training data.
The training data consist of a set of training examples.
In supervised learning, each example is a pair consisting of an input object (typically a
vector) and the desired output value (also called the supervisory signal)
Unsupervised Learning (I am self sufficient in learning)
That learns from test data that has not been labeled, classified or categorized.
Instead of responding to feedback, unsupervised learning identifies commonalities in
the data and reacts based on the presence or absence of such commonalities in each new
piece of data
8. Reinforcement Learning (My life My rules! (Hit & Trail))
It is the ability of an agent to interact with the environment and find out what is the best
outcome. It follows the concept of hit and trial method.
The agent is rewarded with a point for a correct or a wrong answer, and on the basis of
the positive reward points gained the model trains itself.
Reinforcement learning differs from the supervised learning in a way that in supervised
learning, the training data has the answer key with it so the model is trained with the
correct answer itself whereas in reinforcement learning, there is no answer but the
reinforcement agent decides what to do to perform the given task
In the absence of training dataset, it is bound to learn from its experience.
11. Real Life example
Task is to arrange them as
groups
NO SIZE Colour Shape Fruit Name
1 Big Red
Rounded shape with a
depression at the top
Apple
2 Small Red
Heart-shaped to nearly
globular
Cherry
3 Big Green Long curving cylinder Banana
4 Small Green
Round to oval, Bunch
shape cylindrical
Grape
12. For Supervised Learning
Already learn from previous work about the physical characters of fruits
So arranging the same type of fruits at one place.
Your previous work is called as training data in data mining
You already learn the things from your train data, this is because of
response variable
Response variable means just a decision variable
13. For Unsupervised Learning
This time we don’t know any thing about the fruits, honestly saying this
is the first time you have seen them. You have no clue about those.
So, how will we arrange them?
What will we do first???
We will take a fruit and you will arrange them by considering physical
character of that particular fruit.
14. Suppose We have considered color
•RED COLOR GROUP: apples & cherry fruits.
•GREEN COLOR GROUP: bananas & grapes.
Consider The Size along with previous consideration:
•RED COLOR AND BIG SIZE: apple.
•RED COLOR AND SMALL SIZE: cherry fruits.
•GREEN COLOR AND BIG SIZE: bananas.
•GREEN COLOR AND SMALL SIZE: grapes.
This type of learning is known as unsupervised learning.
Clustering comes under unsupervised learning.
17. Selecting the Right Algorithm
selecting a machine learning algorithm is a process of trial and error.
It’s also a trade-off between specific characteristics of the algorithms,
such as:
Speed of training
Memory usage
Predictive accuracy on new data
Transparency or interpretability (how easily you can
understand the reasons an algorithm makes its predictions)
18. SUPERVISEDLEARNING
Classification Regression
Classification techniques predict
discrete responses
—for example,
whether an email is genuine or spam, or
whether a tumor is small,
medium, or large. Classification models
are trained to classify data into
categories. Applications include
medical imaging, speech
recognition, and credit scoring.
Regression techniques predict
continuous responses
—for example,
changes in temperature or fluctuations
in electricity demand.
Applications include forecasting stock
prices, handwriting recognition, and
acoustic signal processing
If the data can be separated into
specific groups or classes, use
classification algorithms.
If the nature of your response is a
real number –such as temperature,
or the time until failure for a piece
of equipment—use regression
techniques.
19. Let’s take a closer look at the most commonly used
classification and regression algorithms.
20. Binaryvs. Multiclass Classification
When we are working on a classification problem, begin by determining whether
the problem is binary or multiclass.
BinaryClassification Multiclass Classification
A single training or test item (instance)
can only be divided into two classes
—for example, Determine whether an
email is genuine or spam
It can be divided into more than two
—for example, Train a model to classify
an image as a dog, cat, or other animal
It requires a more complex model
22. Otherexamplesfor Classification
Binary Classification
Put a tennis ball into the Color or no-Color bin (color)
(Medical Test) Determine if a patient has certain disease or not
(Quality Control Test) Decide if a product should be sold or discarded
(IR Test) Determine if a document should be in the search results or not
Multi-Class Classification
Put a tennis ball into the Green, Orange, or White ball bin (color)
Decide if an email is advertisement, newsletter, phishing, hack, or
personal.
Classify a document into Yahoo! Categories
(Optical Recognition) Classify a scanned character into digit (0..9)
23. Support Vector Machine
“Support Vector Machine” (SVM) is a supervised
machine learning algorithm which can be used
mostly in classification problems.
In this algorithm, data plots as a points in n-
dimensional space (where n is number of
features) with the value of each feature being
the value of a particular coordinate.
Then, classification perform by finding the
hyper-plane that differentiate the two classes
very well
hyper-plane
24. Margin
Margin
HowSupport vector machine Works
Classifies data by finding the linear decision boundary
(hyperplane) that separates all data points of one class
from those of the other class.
The best hyperplane for an SVM is the one with the
largest margin between the two classes, when the data is
linearly separable.
If the data is not linearly separable, a loss function is used
to penalize points on the wrong side of the hyperplane.
SVMs sometimes use a kernel transform to transform nonlinearly separable data into
higher dimensions where a linear decision boundary can be found.
25. Identifythe right hyper-plane
“Select the hyper-plane which segregates the two classes
better”.
In this scenario, hyper-plane “B” has excellently performed
this job.
Which is the right hyper plane?
Which is the right hyper plane?
Above, you can see that the margin for hyper-plane C is
high as compared to both A and B.
Hence, we name the right hyper-plane as C.
27. Identifythe right hyper-plane
SVM selects the hyper-plane which classifies the classes
accurately prior to maximizing margin.
Here, hyper-plane B has a classification error and A has
classified all correctly.
Therefore, the right hyper-plane is A.
Which is the right hyper plane?
Which is the right hyper plane?
It solves this problem by introducing additional feature.
Here, we will add a new feature z=x^2+y^2. (Kernel
Transformation)
Now, let’s plot the data points on axis x and z:
28. Support vector machine Best used ….
For data that has exactly two classes (you can also use it for multiclass classification with
a technique called error correcting output codes)
For high-dimensional, nonlinearly separable data
29. Pros and Cons associatedwithSVM
Pros:
It works really well with clear margin of separation
It is effective in high dimensionalspaces.
It is effective in cases where number of dimensionsis greater than the number of samples.
It uses a subset of training points in the decisionfunction (called support vectors), so it is also
memory efficient.
Cons:
It doesn’t perform well, when we have large data set because the required training time is higher
It also doesn’tperform very well, when the data set has more noise i.e. target classes are
overlapping
SVM doesn’t directly provide probabilityestimates, these are calculated using an expensive five-fold
cross-validation.
30. Discriminant Analysis
Discriminant analysis (DA) is a technique for analyzing data when the criterion or
dependent variable is categorical and the predictor or independent variables are
interval in nature.
It is a technique to discriminate between two or more mutually exclusive and
exhaustive groups on the basis of some explanatory variables
Types Discriminant Analysis (DA)
1. Linear D A - when the criterion /
dependent variable has two categories
Example: adopters & non-adopters
2. Multiple D A- when three or more
categories are involved
Example: SHG1, SHG2,SHG3
31. Group sizes of the dependent should not be grossly different i.e. 80:20. It should be
at least five times the number of independent variables
How DA Works
Assumptions
1. Sample Size (n)
Each of the independent variable is normally distributed.
2. Normal Distribution
All variables have linear and homoscedastic relationships.
3. Homogeneity of variances / covariances
32. Outliers should not be present in the data.
DA is highly sensitive to the inclusion of
outliers.
4. Outliers
There should NOT BE MULTICOLLINEARITY
among the independent variables.
5. Non - multicolinearity
33. The groups must be mutually exclusive, with every subject or case belonging to
only one group.
6. Mutually exclusive
Each of the allocations for the dependent categories in the initial classification are
correctly classified.
7. Classification
34. Discriminant Analysis Model
The discriminant analysis model involves linear combinations of the following
form
𝐷 = 𝑏0+𝑏1𝑋1 + 𝑏2𝑋2 + 𝑏3𝑋3 + ……….+ 𝑏𝑘𝑋𝑘
where
D = discriminant score
b 's = discriminant coefficient or weight
X 's = predictor or independent variable
The coefficients, or weights (b), are estimated so that the groups differ as much
as possible on the values of the discriminant function.
35. Applications of Discriminant Analysis Model
Discriminant analysis has been success fully used for many applications. As long
as we can transform the problem into a classification problem.
DA can be used for original applications also
1. Identification
TO identify type of customers that is likely to buy certain product in a store.
Using simple questionnaires survey, we can get the features of customers
DA will help to select which features can describe the group membership of
buy or not buy the product
36. 3. Prediction
Question “will it rain to day” can be thought as prediction.
Prediction problem can be thought as assigning “today” to one of the two
possible groups of rain and dry
2. Decision Making
Doctor diagnosing illness may be seen as which disease the patient has.
This problem can be transform into classification problem by assigning the
patient to a number of possible groups of disease based on the Observation on
the symptoms
37. 5. Learning
Scientists want to teach robot to learn to talk can be seen as classification
problem.
It assigns frequency , pitch, tune, and many other measurements of sound into
many groups of words
4. Pattern recognition
To distinguish pedestrian from dogs and cars on capture image sequence of traffic
date is a classification problem
38. Naïve Bayes Model
It is a classification technique based on Bayes theorem with an assumption of
independence among predictors.
It is easy to build and particularly useful for very large datasets.
It learns and predicts very fast and it does not require lots of storage.
I has an Assumption : All features must be independent of each other
It still returns very good accuracy in practice even when the independent
assumption does not hold
39. 1. Real-time Prediction
Applications of Naïve Bayes Model
2. Multi - Class Prediction
3. Text Classification/ Spam Filtering/Sentiment Analysis
4. Recommendation System
40. Probability Basics
• Prior, conditional and joint probability for random variables
– Prior probability: P(x)
– Conditional probability: P(𝑥1|𝑥2), P(𝑥2|𝑥1)
– Relationship: P 𝑥1, 𝑥2 = 𝑃 𝑥2 𝑥1 𝑃 𝑥1 = 𝑃 𝑥1 𝑥2 𝑃 𝑥2
– Independence: )
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Discriminative Generative
Bayesian Rule
41.
42. Event contains 2 boxes. Box 1 Contains 2 white balls and 3 red balls, Box 2
contains 4 white balls and 5 red balls. One ball is drawn at random from one of the
box and is found to be red. Find the probability that It was drawn from second
box.
Example to Understand Baye’s Theorem
Let Assume, Red ball = R, white ball = W, Box1 = A, Box2 = B
Probability for selected one as box1 P(A) =
1
2
Probability for selected box as box 2 P(B) =
1
2
Probability of getting red ball from box1 = P(R|A) =
3
5
Solution
43. Probability of getting red ball from box2 = P(R|B) =
5
9
probability Red ball was drawn from second box = P(B|R) =
𝑃(𝑅|𝐵).𝑃(𝐵)
𝑃(𝑅|𝐵).𝑃 𝐵 +𝑃(𝑅|𝐴).𝑃(𝐴)
This is called baye’s theorem
P(B|R) =
𝑃(𝑅|𝐵).𝑃(𝐵)
𝑃(𝑅|𝐵).𝑃 𝐵 +𝑃(𝑅|𝐴).𝑃(𝐴)
=
5
9
∗
1
2
5
9
∗
1
2
+
3
5
∗
1
2
= 0.487 = 48.7%
44. With below tabulation of the 100 people, what is the conditional probability that a
certain member of the school is a ‘Teacher’ given that he is a ‘Man’?
Example to Understand Baye’s Theorem
Female Male Total
Teacher 8 12 20
Student 32 48 80
Total 40 60 100
45. The Naïve Bayes Model
The Bayes Rule provides the formula for the probability of Y given X. But, in real-
world problems, you typically have multiple X variables
When the features are independent, we can extend the Bayes Rule to what is
called Naive Bayes
It is called ‘Naive’ because of the naive assumption that the X’s are independent
of each other.
46.
47.
48. Naive Bayes Example
Say you have 1000 fruits which could be either ‘banana’, ‘orange’ or ‘other’. These
are the 3 possible classes of the Y variable.
49. For the sake of computing the probabilities, let’s aggregate the
training data to form a counts table like this.
50. Step1: Compute the ‘Prior’ probabilities for each of the class of
fruits.
P(Y=Banana) = 500 / 1000 = 0.50
P(Y=Orange) = 300 / 1000 = 0.30
P(Y=Other) = 200 / 1000 = 0.20.
Step 2: Compute the probability of evidence that goes in the
denominator..
P(x1=Long) = 500 / 1000 = 0.50
P(x2=Sweet) = 650 / 1000 = 0.65
P(x3=Yellow) = 800 / 1000 = 0.80
51. Step 3: Compute the probability of likelihood of evidences that goes
in the numerator..
P(x1=Long | Y=Banana) = 400 / 500 = 0.80
P(x2=Sweet | Y=Banana) = 350 / 500 = 0.70
P(x3=Yellow | Y=Banana) = 450 / 500 = 0.90
So, the overall probability of Likelihood of evidence for Banana =
0.8 * 0.7 * 0.9 = 0.504
52. Step 4: Substitute all the 3 equations into the Naive Bayes formula,
to get the probability that it is a banana.
53. Nearest Neighbor Algorithm
Simple Analogy , Tell me about your friends (Who your neighbors are) , then I
will tell who you are
55. What is KNN (K-Nearest Neighbor)
A powerful classification algorithm used in pattern recognition.
K nearest neighbors stores all available casesand classifies new
cases based on a similarity measure(e.g distance function)
One of the top data mining algorithms used today.
A non-parametric lazy learning algorithm (An Instancebased Learning
method).
56. When do we use KNN
KNN can be used for both classification and regression predictive problems.
However, it is more widely used in classification problems in the industry.
To evaluate any technique we generally look at 3 important aspects
It is commonly used for its easy of interpretation and low calculation time.
63. Training error rate and Validation error rate
.
Segregate the training and validation from the initial dataset. then
Plot the validation error curve to get the optimal value of K. This value of K
should be used for all predictions
66. George to John Distance = Sqrt[(35 − 37)2+(35 − 50)2+(3 − 2)2] = 15.16
Rachel to John Distance = Sqrt[(22 − 37)2+(50 − 50)2+(2 − 2)2] = 15
Steve to John Distance = Sqrt[(63 − 37)2+(200 − 50)2+(1 − 2)2] = 152.23
Tom to John Distance = Sqrt[(59 − 37)2+(170 − 50)2+(1 − 2)2] = 122
Tom to John Distance = Sqrt[(25 − 37)2+(40 − 50)2+(4 − 2)2] = 15.74
Distance Measure from john to others using Euclidean
Distance
69. Types of Regression
1. Simple Linear Regression
2. Polynomial Regression
3. Support Vector Regression
4. Decision Tree Regression
5. Random Forest Regression
70. Form of Linear Regression
𝑌 = 𝑏0+𝑏1𝑋1 + 𝑏2𝑋2 + 𝑏3𝑋3 + ……….+ 𝑏𝑘𝑋𝑘
Y is the response
b values are called the model coefficients. These values are “learned”
during the model fitting/training step.
𝑏0 is the intercept
𝑏1 is the coefficient for X1 (the first feature)
𝑏𝑘 is the coefficient for Xn (the nth feature)
71. Steps for Training Linear regression
1. Model Coefficients/Parameters
When training a linear regression model it’s way to say we are trying to find out a
coefficients for the linear function that best describe the input variables.
2. Cost Function (Loss Function)
When building a linear model it’s said that we are trying to minimize the error an
algorithm does making predictions, and we got that by choosing a function to help
us measure the error also called cost function.
3.Estimate The Coefficients
For that task there’s a mathematical algorithm called Gradient Descent,
72. Model evaluation metrics for regression
It is necessary to evaluate metrics designed for comparing continuous values
Root Mean Squared Error, is on of the best evaluation methods
1
𝑛
𝑖=1
𝑛
(𝑦𝑖 − 𝑦𝑚𝑒𝑎𝑛)2
76. Learn More About machine learning through Online Courses
1. Coursera – Machine Learning- Andrew N.G. – Stanford University
2. Machine Learning for Intelligent Systems – Kilian Weinberger