This presentation about hierarchical clustering will help you understand what is clustering, what is hierarchical clustering, how does hierarchical clustering work, what is distance measure, what is agglomerative clustering, what is divisive clustering and you will also see a demo on how to group states based on their sales using clustering method. Clustering is the method of dividing the objects into clusters which are similar between them and are dissimilar to the objects belonging to another cluster. It is used to find data clusters such that each cluster has the most closely matched data. Prototype-based clustering, hierarchical clustering, and density-based clustering are the three types of clustering algorithms. Lets us discuss hierarchical clustering in this video. In simple terms, Hierarchical clustering is separating data into different groups based on some measure of similarity.
Below topics are explained in this "Hierarchical Clustering" presentation:
1. What is clustering?
2. What is hierarchical clustering
3. How hierarchical clustering works?
4. Distance measure
5. What is agglomerative clustering
6. What is divisive clustering
7. Demo: to group states based on their sales
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at www.simplilearn.com
K Means Clustering Algorithm | K Means Clustering Example | Machine Learning ...Simplilearn
This K-Means clustering algorithm presentation will take you through the machine learning introduction, types of clustering algorithms, k-means clustering, how does K-Means clustering work and at least explains K-Means clustering by taking a real life use case. This Machine Learning algorithm tutorial video is ideal for beginners to learn how K-Means clustering work.
Below topics are covered in this K-Means Clustering Algorithm presentation:
1. Types of Machine Learning?
2. What is K-Means Clustering?
3. Applications of K-Means Clustering
4. Common distance measure
5. How does K-Means Clustering work?
6. K-Means Clustering Algorithm
7. Demo: k-Means Clustering
8. Use case: Color compression
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
K Means Clustering Algorithm | K Means Clustering Example | Machine Learning ...Simplilearn
This K-Means clustering algorithm presentation will take you through the machine learning introduction, types of clustering algorithms, k-means clustering, how does K-Means clustering work and at least explains K-Means clustering by taking a real life use case. This Machine Learning algorithm tutorial video is ideal for beginners to learn how K-Means clustering work.
Below topics are covered in this K-Means Clustering Algorithm presentation:
1. Types of Machine Learning?
2. What is K-Means Clustering?
3. Applications of K-Means Clustering
4. Common distance measure
5. How does K-Means Clustering work?
6. K-Means Clustering Algorithm
7. Demo: k-Means Clustering
8. Use case: Color compression
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
This is very simple introduction to Clustering with some real world example. At the end of lecture I use stackOverflow API to test some clustering. I also wants to try facebook but it has some problem with it's API
This Logistic Regression Presentation will help you understand how a Logistic Regression algorithm works in Machine Learning. In this tutorial video, you will learn what is Supervised Learning, what is Classification problem and some associated algorithms, what is Logistic Regression, how it works with simple examples, the maths behind Logistic Regression, how it is different from Linear Regression and Logistic Regression applications. At the end, you will also see an interesting demo in Python on how to predict the number present in an image using Logistic Regression.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. What is supervised learning?
2. What is classification? what are some of its solutions?
3. What is logistic regression?
4. Comparing linear and logistic regression
5. Logistic regression applications
6. Use case - Predicting the number in an image
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
A tutorial on LDA that first builds on the intuition of the algorithm followed by a numerical example that is solved using MATLAB. This presentation is an audio-slide, which becomes self-explanatory if downloaded and viewed in slideshow mode.
What is an "ensemble learner"? How can we combine different base learners into an ensemble in order to improve the overall classification performance? In this lecture, we are providing some answers to these questions.
This is very simple introduction to Clustering with some real world example. At the end of lecture I use stackOverflow API to test some clustering. I also wants to try facebook but it has some problem with it's API
This Logistic Regression Presentation will help you understand how a Logistic Regression algorithm works in Machine Learning. In this tutorial video, you will learn what is Supervised Learning, what is Classification problem and some associated algorithms, what is Logistic Regression, how it works with simple examples, the maths behind Logistic Regression, how it is different from Linear Regression and Logistic Regression applications. At the end, you will also see an interesting demo in Python on how to predict the number present in an image using Logistic Regression.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. What is supervised learning?
2. What is classification? what are some of its solutions?
3. What is logistic regression?
4. Comparing linear and logistic regression
5. Logistic regression applications
6. Use case - Predicting the number in an image
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
A tutorial on LDA that first builds on the intuition of the algorithm followed by a numerical example that is solved using MATLAB. This presentation is an audio-slide, which becomes self-explanatory if downloaded and viewed in slideshow mode.
What is an "ensemble learner"? How can we combine different base learners into an ensemble in order to improve the overall classification performance? In this lecture, we are providing some answers to these questions.
This slide is all about the Data mining techniques.This slide is all about the Data mining techniques.This slide is all about the Data mining techniques.This slide is all about the Data mining techniques;This slide is all about the Data mining techniques;This slide is all about the Data mining techniques.This slide is all about the Data mining techniques.This slide is all about the Data mining techniques
🔥 Cyber Security Engineer Vs Ethical Hacker: What's The Difference | Cybersec...Simplilearn
In this video on "Cyber Security Engineer Vs Ethical Hacker: What's The Difference," we'll dive deep into the fascinating world of cybersecurity. We'll explore the roles, qualifications, and responsibilities that set Cyber Security Engineers and Ethical Hackers apart. From managing production environments to reporting client usage and tackling complex problem-solving scenarios, we'll dissect the key distinctions between these two vital roles. Not only that, we'll reveal insights into the average salaries in these fields as well.
Top 10 Companies Hiring Machine Learning Engineer | Machine Learning Jobs | A...Simplilearn
This video is based on Top 10 Companies Hiring Machine Learning Engineer, we'll delve into the dynamic realm of Machine Learning Engineering and explore the Top 10 Companies that are currently at the forefront of hiring in 2023. From industry giants like Google, Apple, and Microsoft to other innovative companies, we will cover all of that, join us as we uncover the exciting opportunities that await ML Engineers. Discover how Amazon, Facebook, and others are shaping the landscape of artificial intelligence and machine learning technologies.
How to Become Strategy Manager 2023 ? | Strategic Management | Roadmap | Simp...Simplilearn
In this video on Strategic Manager Roadmap for 2023, we're diving deep into the realm of strategic management and uncovering the path to becoming a skilled strategic manager in 2023. From understanding the fundamentals of strategy management to exploring the career opportunities it offers, we've got you covered. Discover the essential skills that set strategic managers apart and gain insights into their pivotal roles and responsibilities. Follow our step-by-step guide to walk on your journey toward becoming a proficient strategic manager.
Top 20 Devops Engineer Interview Questions And Answers For 2023 | Devops Tuto...Simplilearn
In this video on Top 20 Devops Engineer Interview Questions And Answers For 2023. We will dive into the realm of DevOps interview questions. Gain insights into essential concepts, methodologies, and practices driving modern software development and collaboration between teams. Whether you're new or experienced, these discussions will equip you with valuable knowledge to excel in this dynamic field.
🔥 Big Data Engineer Roadmap 2023 | How To Become A Big Data Engineer In 2023 ...Simplilearn
This video is based on Big Data Engineer Roadmap 2023. In this informative session, we will dive into the fundamentals of Big Data Engineering. Join us as we explore the role and responsibilities of a Big Data Engineer, highlighting the key skills required in this field. Additionally, we provide a step-by-step guide on how to become a proficient Big Data Engineer. Don't miss out on this essential information for aspiring data professionals!
🔥 AI Engineer Resume For 2023 | CV For AI Engineer | AI Engineer CV 2023 | Si...Simplilearn
In this video on AI Engineer Resume For 2023, We delve into the essential components of an AI Engineer Resume for 2023. Learn the intricacies of Resume formatting, structure, and content to craft a compelling application. From resume summaries to objectives, gain insights into creating captivating opening statements. Uncover the key skills demanded in the AI engineering sector. Navigate effectively through presenting your educational background. Elevate your resume and excel in your pursuit of an AI Engineering role with the insights gained from this informative session.
🔥 Top 5 Skills For Data Engineer In 2023 | Data Engineer Skills Required For ...Simplilearn
This video is based on Top 5 Skills For Data Engineer In 2023. In this video, we delve into the role of Data Engineers and the future salary trends. Learn about key skills like Big Data technologies, Data Modeling, and proficiency in programming languages that are crucial for excelling in the field. Stay ahead by mastering the expertise needed to thrive as a Data Engineer in the dynamic landscape of data-driven decision-making.
🔥 6 Reasons To Become A Data Engineer | Why You Should Become A Data Engineer...Simplilearn
🔥Link to watch video: https://youtu.be/m9ViGf3iPHo
🔥 Post Graduate Program In Data Engineering: https://www.simplilearn.com/pgp-data-engineering-certification-training-course?utm_campaign=28July2023ReasonsToBecomeADataEngineer&utm_medium=Descriptionff&utm_source=youtube
This video is based on 6 Reasons To Become A Data Engineer. In this video, we delve into the role of a Data Engineer and present 6 compelling reasons why it's an incredible career choice. From building cutting-edge solutions to unlocking valuable insights, join us as we embark on an exciting journey through the world of Data Engineering. If you're seeking a dynamic and impactful profession, don't miss out on the opportunities that await you as a Data Engineer!
Project Manager vs Program Manager - What’s the Difference ? | Project Manage...Simplilearn
https://www.youtube.com/watch?v=9z0BNicnBjw
In this informative video on Project Manager vs Program Manager - What’s the Difference ?, we will explore the fundamentals of Project Management and Program Management. Discover the definitions of both disciplines, their unique characteristics, and key differences. Learn about the essential skills and competencies required for successful execution in each role. Whether you're a professional seeking career growth or a curious learner, this concise breakdown will provide valuable insights. Stay tuned and expand your knowledge of these crucial management practices!
Deloitte Interview Questions And Answers | Top 45 Deloitte Interview Question...Simplilearn
https://www.youtube.com/watch?v=Cfj0y6xIo48
Deloitte is one of the reputed “Big Four” accounting companies and the largest professional service provider by revenue as well as the number of professionals. With more than 263900 professionals worldwide, the organisation provides financial advising, corporate risk, consulting, tax, and audit services. Deloitte generated revenue of a record USD 38.8 billion in the financial year 2017 and is ranked as the sixth-largest private company in the United States as of 2016. In this video session on Deloitte interview questions and answers, we will go through different interview questions often asked during the interview process at Deloitte.
🔥 Deep Learning Roadmap 2024 | Deep Learning Career Path 2024 | SimplilearnSimplilearn
This video on "Deep Learning Roadmap for 2024" offers a comprehensive guide to becoming a DL engineer. This "deep Learning Career Path 2024" provides valuable knowledge about crucial programming languages and mathematical concepts necessary for attaining proficiency in DL engineering. The field of dL presents captivating career prospects across different industries and sectors. Exciting roles such as DL engineers, ML engineers, data scientists, NLP engineers, AI engineers, and more offer the opportunity to work with advanced technologies and contribute to AI innovation.
In this ChatGPT in Cybersecurity video, we delve into the role of ChatGPT in the realm of cybersecurity. Discover how this powerful language model assists in threat detection, vulnerability assessment, and incident response. Gain insights into the innovative ways ChatGPT is shaping the future of cybersecurity. Join us to explore the fascinating intersection of AI and cybersecurity.
In this SQL Injection video, we delve into the world of SQL Injection attacks, one of the most prevalent threats to databases today. Join us as we explore the inner workings of this malicious technique and understand how hackers exploit vulnerabilities in web applications to gain unauthorized access to sensitive data. With step-by-step examples and demonstrations, we provide comprehensive insights on the various types of SQL Injection attacks and their potential consequences. Moreover, we equip you with essential knowledge and countermeasures to safeguard your database against these attacks, ensuring the security of your valuable information. Don't let your data fall victim to SQL Injection—watch this video now!
Top 5 High Paying Cloud Computing Jobs in 2023 Simplilearn
This video, "Top 5 High Paying Cloud Computing Jobs In 2023" by Simplilearn will take you through 5 different job role which are the highest paid in 2023. In this Cloud Computing Jobs and salary video, we'll talk about the required skills and the average salary of various job profiles in the United States. Below are the topics covered in this Cloud Computing Jobs and Salary 2023 video.
This video, "Types of Cloud Jobs In 2024," by Simplilearn, will take you through the different types of cloud computing jobs available in the field of cloud computing in 2024. In this video, we will take you through the roles and responsibilities along with the career path and salaries of each job role available in this dynamic field. In addition, you will also understand through the video which job role matches your skills and interest in this field. Below are the topics we have covered in this video on Types of Cloud Jobs in 2024.
Top 12 AI Technologies To Learn 2024 | Top AI Technologies in 2024 | AI Trend...Simplilearn
🔥 Become An AI & ML Expert Today: https://taplink.cc/simplilearn_ai_ml
Explore the future of AI in our Top 12 AI Technologies To Learn in 2024 video. We've curated a list of the most significant AI technologies for the coming year. Whether you're new to AI or an experienced pro, these insights are valuable. Discover machine learning, natural language processing, computer vision, and more. Stay ahead of the AI curve, and ensure you're prepared for the evolving landscape. Don't miss out on the opportunity to advance your AI knowledge and career.
Here in this Top 12 AI Technologies To Learn 2024 video, we start with:
What is LSTM ?| Long Short Term Memory Explained with Example | Deep Learning...Simplilearn
In this video on What is LSTM, we will go through what is LSTM, moving forward we will learn what is RNN, and after this, we will see the types of gates in LSTM and some applications of LSTM. At the end of the video, we will see a hands-on lab demo of gold price prediction using the LSTM model in machine learning.
00:00 What is LSTM?
01:51 What is RNN?
02:29 Types of gates in LSTM
03:45 Applications of LSTM
05:40 Hands-on lab demo
Dataset link: https://drive.google.com/drive/folder...
🔥 Get Your Dream Job With Simplilearn's Artificial Intelligence Engineer Master's Program: https://www.simplilearn.com/artificia...
🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688
What is LSTM?
Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) that can capture long-term dependencies in sequential data. LSTMs are able to process and analyze sequential data, such as time series, text, and speech. They use a memory cell and gates to control the flow of information, allowing them to selectively retain or discard information as needed and thus avoid the vanishing gradient problem that plagues traditional RNNs. LSTMs are widely used in various applications such as natural language processing, speech recognition, and time series forecasting.
What is RNN?
RNNs are a type of neural network that are designed to process sequential data. They can analyze data with a temporal dimension, such as time series, speech, and text. RNNs can do this by using a hidden state passed from one timestep to the next. The hidden state is updated at each timestep based on the input and the previous hidden state. RNNs are able to capture short-term dependencies in sequential data, but they struggle with capturing long-term dependencies.
Types of Gates in LSTM
Input gate
Output gate
Forget gate
Applications of LSTM
Language Simulation
Voice Recognition
Sentiment analysis
Time series prediction
Video analysis
Handwriting recognition
✅ Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH
✅ Watch More Videos On Artificial Intelligence By Simplilearn:
• 🔥Artificial Intel...
✅ Best From Simplilearn:
⏩ Top 10 Programming Languages in 2023:
• Top 10 Programmin...
⏩ Top 10 Highest Paying Jobs in 2022:
• Top 10 Highest Pa...
⏩ Top 10 Certifications for 2022:
• Top 10 Certificat...
⏩ Top 10 Technologies to Learn in 2022:
• Top 10 Technologi...
✅ About Artificial Intelligence Engineer Master's Program
The Artificial Intelligence course, created in partnership with IBM, introduces students to blended learning and prepares them to be specialists in AI and Data Science. IBM, located in Armonk, New York, is a significant cognitive services and integrated cloud solution firm that provides many technology and consulting solutions. IBM invests $6 billion in research & development every year and has won five Nobel prizes, nine US Na
Top 10 Chat GPT Use Cases | ChatGPT Applications | ChatGPT Tutorial For Begin...Simplilearn
In this video on ChatGPT Usecases, we will explore ChatGPT by OpenAI, which interacts conversationally. This ChatGPT tutorial for beginners will help you understand what chatGPT is, How it works, and the Different Usecases of chatGPT to make your life easier.
00:00 Chat GPT Usecases
01:04 What is Chat GPT?
01:25 How does Chat GPT work?
01:58 Demo -Usecases
02:21 Explain complex subjects
04:12 Write any code
06:36 Audit/Debug any code
08:26 Create custom plans for marketing strategy
11:54 Write articles and blogs
16:00 Summarize book or article
17:35 Answer interview questions
19:38 Develop apps
21:45 Create diet plan and exercise plan
24:00 Answer general knowledge questions
What is ChatGPT
ChatGPT is a conversational language model created by OpenAI. It is a form of the Generative Pre-trained Transformer (GPT) model, which was trained on a dataset of conversational prompts, such as dialogue snippets and chat logs. The model is capable of generating human-like responses to text inputs.
How does chatGPT work?
This model is pre-trained on a large text dataset and then fine-tuned on a smaller dataset specific to the everyday task.
When the model receives input from a user, it uses the patterns it learned during fine-tuning to generate a response
🔥Enroll for Free Introduction to Artificial Intelligence Course & Get Your Completion Certificate: https://www.simplilearn.com/learn-ai-...
#ChatGPT #ChatGPTUseCases #ChatGPTApplications #ApplicationsOfChatGPT #ChatGPTForCoding #ChatGPTForContentCreation #ChatGPTExamples #AutomationUsingChatGPT #ChatGPTTutorial #ArtificialIntelligence #AI #Simplilearn
🔥 Watch Top Trending Videos From Simplilearn:
⏩ Top 10 Programming Languages in 2023:
• Top 10 Programmin...
⏩ Top 10 Certifications for 2023:
• Top 10 Certificat...
⏩ Top 10 Highest Paying Jobs in 2023:
• Top 10 Highest Pa...
⏩ Top 10 Dying Programming Languages in 2023:
• Top 10 Dying Prog...
✅ Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH
⏩ Check out the Artificial Intelligence Training videos:
• 🔥Artificial Intel...
✅ About Artificial Intelligence Engineer Master's Program:
Accelerate your career with this Artificial Intelligence Course in conjunction with IBM. Industry-relevant AI courses, including Data Science with Python, Machine Learning, Deep Learning, and NLP, feature unique hackathons and Ask Me Anything sessions hosted by IBM. Get job-ready AI certification training with Capstone projects, practical labs, live sessions, and hands-on projects.
✅ What skills are covered in this Artificial Intelligence course?
You will be able to demonstrate the following ability after completing this Artificial Intelligence certification training:
- Learn Artificial Intelligence's concept, purpose, domain breadth, phases, implementations, and impacts.
- Create real-world projects, games, prediction models, logic constraint
React JS Vs Next JS - What's The Difference | Next JS Tutorial For Beginners ...Simplilearn
In this video on "React JS Vs Next JS - What's The Difference," we will start with what is React js and Next js. After that, we will see the difference between React and Next js regarding performance, development cost, community, and many more. Moving ahead, we will talk about the features of React and Next js, and then we will see where we can use react and next js.
00:00 - React JS Vs Next JS - What's The Difference
01:55 - What is React js?
02:53 - What is Next js?
03:18 - Differnce between React and Next js
03:48 - React vs Next js : Performance
04:38 - React vs Next js : Documentation
05:05 - React vs Next js : Server side rendering
05:35 - React vs Next js : Community
06:40 - React vs Next js : Configuration
07:18 - React vs Next js : Maintance
07:43 - React vs Next js : Development Cost
08:00 - React vs Next js : Features
08:37 - Where React js and Next js is used?
What is React JS?
1. React is a JavaScript library that builds fast, interactive mobile and web applications.
2. It is an open-source, reusable component-based front-end library of JavaScript.
3. React is a combination of HTML and JavaScript.
4. It provides a robust and opinionated way to build modern applications Interface.
What Next js is.
1.Next js is an open-source web framework created by Vercel.
2. Next js enables React-based web applications with server-side rendering and generating static websites.
3. In addition, next js offers additional structure, features, and optimizations for your application.
4. Next.js takes care of the tooling and settings required for React Js.
🔥 Explore Free Certification Course to Learn React JS Basics: https://www.simplilearn.com/learn-rea...
#ReactvsNextJS #ReactJSvsNextJS #ReactJS #NextJS #NextJSTutorial #NextJSForBeginners #JavaScript #WebDevelopment #FrontendDevelopment #JavaScriptLibraries #React #Coding #Programming #Simplilearn
⏩ Check out ReactJS Tutorial Videos:
• ReactJS Tutorial ...
✅Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH
✅ About Full Stack Web Developer - MEAN Stack Program:
This program will advance your career as a MEAN stack developer. You’ll learn top skills such as MongoDB, Express.js, Angular, and Node.js (“MEAN”), plus GIT, HTML, CSS, and JavaScript to build and deploy interactive applications and services throughout this Full Stack MEAN Developer program. Full Stack Web Developer Mean Stack course provides complete knowledge of software development and testing technologies such as JavaScript, Node.js, Angular, Docker, and Protractor. You'll build an end-to-end application, test and deploy code, and store data using MongoDB.
✅ What is MEAN Stack?
The term MEAN stack refers to a collection of JavaScript-based technologies used to develop web applications. MEAN is an acronym for MongoDB, Express, Angular, and Node.js. MongoDB is a database system, Express is a back-end web framework, Angular.js is a front-end framework, and Node.js
Backpropagation in Neural Networks | Back Propagation Algorithm with Examples...Simplilearn
This video covers What is Backpropagation in Neural Networks? Neural Network Tutorial for Beginners includes a definition of backpropagation, working of backpropagation, benefits of backpropagation, and applications.
00:00 - What is Backpropagation?
This phase contains the definition of backpropagation with diagrammatic representation.
01:41 - What is Backpropagation in neural networks?
This phase of the video has specifically explained the role of backpropagation in neural networks.
02:23 - How does Backpropagation in neural networks work?
This phase of the video explains functioning, activation, and loss function in simple words.
05:28 - Benefits of Backpropagation
This content highlights the importance of backpropagation and gives you a reason to choose the same.
05:54 - Applications of Backpropagation
This phase covers applications of backpropagation in different fields.
🔥 Enroll for Free Data Science Course & Get Your Completion Certificate: https://www.simplilearn.com/data-scie...
✅Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH
⏩ Check out the Deep Learning Tutorial videos:
• Deep Learning Tut...
#WhatisBackpropagationinNeuralNetworks #Backpropagation #NeuralNetworks #BackpropagationInNeuralNetworks #BackpropagationAlgorithm #BackpropagationAlgorithmExplained #NeuralNetworksTutorialForBeginners #DeepLearningTutorial #ArtificialIntelligence #DeepLearning #MachineLearning #DataScience #DifferenceBetween #LearnDataScience #DataScience #DataScienceTutorial #DataScienceCourse #DataScienceCareers #Simplilearn
Backpropagation is an algorithm that is created to test errors that will travel back from input nodes to output nodes. It is applied to improve accuracy in data mining and machine learning.The concept of backpropagation in neural networks was first introduced in the 1960s. An artificial neural network is made up of bunches of connected input/output units, each of which is connected by a software program and has a certain weight. This kind of network is based on biological neural networks, which contain neurons coupled to one another across different network levels. In this instance, neurons are shown as nodes.
▶️ Understand Neural Networks in 1 Minute :
• Neural Network In...
🔴 Watch Top Trending Videos From Simplilearn:
⏩ Top 10 Programming Languages in 2023:
• Top 10 Programmin...
⏩ Top 10 Certifications for 2023:
• Top 10 Certificat...
⏩ Top 10 Highest Paying Jobs in 2023:
• Top 10 Highest Pa...
⏩ Top 10 Technologies to Learn in 2022:
• Top 10 Technologi...
About Data Science with Python Certification Course:
Ranked #1 Data Science program by Economic Times
The Data Science with Python certification course provides a complete overview of Python's Data Analytics tools and techniques. Learning Python is a crucial skill for many Data Science roles. Acquiring knowledge in Python will be the key to unlock your career as a Da
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
MATATAG CURRICULUM: ASSESSING THE READINESS OF ELEM. PUBLIC SCHOOL TEACHERS I...NelTorrente
In this research, it concludes that while the readiness of teachers in Caloocan City to implement the MATATAG Curriculum is generally positive, targeted efforts in professional development, resource distribution, support networks, and comprehensive preparation can address the existing gaps and ensure successful curriculum implementation.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Delivering Micro-Credentials in Technical and Vocational Education and TrainingAG2 Design
Explore how micro-credentials are transforming Technical and Vocational Education and Training (TVET) with this comprehensive slide deck. Discover what micro-credentials are, their importance in TVET, the advantages they offer, and the insights from industry experts. Additionally, learn about the top software applications available for creating and managing micro-credentials. This presentation also includes valuable resources and a discussion on the future of these specialised certifications.
For more detailed information on delivering micro-credentials in TVET, visit this https://tvettrainer.com/delivering-micro-credentials-in-tvet/
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
2. What’s in it for you?
What is Clustering?
What is Hierarchical Clustering?
How Hierarchical Clustering works?
Distance Measure
What is Agglomerative Clustering?
What is Divisive Clustering?
10. What is Clustering?
It will group places with least distance
The method of dividing the objects into clusters which are similar between them and are dissimilar
to the objects belonging to another cluster
11. What is Clustering?
It will group places with least distance
The method of dividing the objects into clusters which are similar between them and are dissimilar
to the objects belonging to another cluster
Partial
Clustering
Hierarchical
Clustering
12. What is Clustering?
It will group places with least distance
The method of dividing the objects into clusters which are similar between them and are dissimilar
to the objects belonging to another cluster
Partial
Clustering
Hierarchical
Clustering
Agglomerative Divisive
13. What is Clustering?
It will group places with least distance
The method of dividing the objects into clusters which are similar between them and are dissimilar
to the objects belonging to another cluster
Partial
Clustering
Hierarchical
Clustering
Agglomerative Divisive K-means Fuzzy C-Means
18. What is Hierarchical Clustering?
It will group places with least distance
Let’s consider that we have a set of cars and we have to group similar ones together
19. What is Hierarchical Clustering?
It will group places with least distance
Hierarchical Clustering creates a tree like structure and group similar objects together
20. What is Hierarchical Clustering?
It will group places with least distance
The grouping is done till we reach the last cluster
21. What is Hierarchical Clustering?
It will group places with least distance
Hierarchical Clustering is separating data into different groups based on some measure of similarity
22. Types of Hierarchical Clustering
It will group places with least distance
Agglomerative
It is known as Bottom-up approach
23. Types of Hierarchical Clustering
It will group places with least distance
Agglomerative Divisive
It is known as Top Down approach
25. What is Hierarchical Clustering?
Convergence
0
1
2
3
4
5
6
0 2 4 6 8
Y-Values
P1P2
P3
P4
P5 P6
Termination
Grouping
Measure the
distance
• Let’s consider we have few points on a plane
26. What is Hierarchical Clustering?
Convergence
0
1
2
3
4
5
6
0 2 4 6 8
Y-Values
P1P2
P3
P4
P5 P6
Termination
Grouping
Measure the
distance
• Each data point is a cluster of its own
27. What is Hierarchical Clustering?
Convergence
0
1
2
3
4
5
6
0 2 4 6 8
Y-Values
P1P2
P3
P4
P5 P6
Termination
Grouping
Measure the
distance
• Each data point is a cluster of its own
• We try to find the least distance between two data points/cluster
28. What is Hierarchical Clustering?
0
1
2
3
4
5
6
0 2 4 6 8
Y-Values
P1P2
P3
P4
P5 P6
0
1
2
3
4
5
6
0 2 4 6 8
Y-Values
P1P2
P5 P6
P3
P4
• The two nearest clusters/datapoints are merged together
Termination
Grouping
Measure the
distance
29. What is Hierarchical Clustering?
0
1
2
3
4
5
6
0 2 4 6 8
Y-Values
P1P2
P3
P4
P5 P6
0
1
2
3
4
5
6
0 2 4 6 8
Y-Values
P1P2
P5 P6
P3
P4
• The two nearest clusters/datapoints are merged together
Termination
Grouping
Measure the
distance
P2 P1
• This is represented in a tree like structure called Dendrogram
30. What is Hierarchical Clustering?
0
1
2
3
4
5
6
0 2 4 6 8
Y-Values
P1P2
P3
P4
P5 P6
0
1
2
3
4
5
6
0 2 4 6 8
Y-Values
P1P2
P5 P6
P3
P4
• The two nearest clusters/datapoints are merged together
Termination
Grouping
Measure the
distance
• This is represented in a tree like structure called Dendrogram
P3P2 P1 P4
31. What is Hierarchical Clustering?
0
1
2
3
4
5
6
0 2 4 6 8
Y-Values
P1P2
P3
P4
P5 P6
0
1
2
3
4
5
6
0 2 4 6 8
Y-Values
P1P2
P5 P6
P3
P4
P5 P6
• The two nearest clusters/datapoints are merged together
Termination
Grouping
Measure the
distance
• This is represented in a tree like structure called Dendrogram
P5 P6P3 P4P2 P1
32. What is Hierarchical Clustering?
0
1
2
3
4
5
6
0 2 4 6 8
Y-Values
P1P2
P3
P4
P5 P6
0
1
2
3
4
5
6
0 2 4 6 8
Y-Values
P1P2
P5 P6
P3
P4
P5 P6
• The two nearest clusters/datapoints are merged together
Termination
Grouping
Measure the
distance
• This is represented in a tree like structure called Dendrogram
P5 P6P3 P4P2 P1
33. What is Hierarchical Clustering?
0
1
2
3
4
5
6
0 2 4 6 8
Y-Values
P1P2
P3
P4
P5 P6
0
0.2
0.4
0.6
0.8
1
1.2
0 0.5 1 1.5
Y-Values
P6
P3
P4
P6
• We terminate when we are left with only one clusters
Termination
Grouping
Measure the
distance
P6P3P2 P1
P
P5P4
34. What is Hierarchical Clustering?
It will group places with least distance
An algorithm that builds hierarchy of clusters
0
1
2
3
4
5
6
0 2 4 6 8
Y-Values
P1P2
P5 P6
P3
P4
P5 P6 P2 P1 P3 P4
?
How do we measure the distance
between the data points?
41. Euclidean Distance Measure
• The Euclidean distance is the "ordinary" straight line
• It is the distance between two points in Euclidean space
d=√ 𝑖=1
𝑛
( 𝑞𝑖− )2
p
q
Euclidian
Distance
𝑝𝑖
Option 02
Euclidean distance
measure
01
Squared euclidean
distance measure
02
Manhattan distance
measure
03
Cosine distance
measure
04
42. Squared Euclidean Distance Measure
The Euclidean squared distance metric uses the same equation as the
Euclidean distance metric, but does not take the square root.
d= 𝑖=1
𝑛
( 𝑞𝑖− )2
𝑝𝑖
Option 02
Euclidean distance
measure
01
Squared euclidean
distance measure
02
Manhattan distance
measure
03
Cosine distance
measure
04
43. Manhattan Distance Measure
Option 02
Euclidean distance
measure
01
Squared euclidean
distance measure
02
Manhattan distance
measure
03
Cosine distance
measure
04
The Manhattan distance is the simple sum of the horizontal and vertical
components or the distance between two points measured along axes at right angles
d= 𝑖=1
𝑛
| 𝑞 𝑥− |
p
q
Manhattan
Distance
𝑝 𝑥 +|𝑞 𝑦− |𝑝 𝑦
(x,y)
(x,y)
44. Cosine Distance Measure
Option 02
Euclidean distance
measure
01
Squared euclidean
distance measure
02
Manhattan distance
measure
03
Cosine distance
measure
04
The cosine distance similarity measures the angle between the two vectors
p
q
Cosine
Distance
𝑖=0
𝑛−1
𝑞𝑖−
𝑖=0
𝑛−1
(𝑞𝑖)2
× 𝑖=0
𝑛−1
(𝑝𝑖)2
d=
𝑝 𝑥
46. What is Agglomerative Clustering?
It will group places with least distance
Agglomerative Clustering begins with each element as a separate cluster and merge them into larger clusters
47. What is Agglomerative Clustering?
It will group places with least distance
There are three key questions that needs to be answered
How do we represent a cluster of more than one point?
48. What is Agglomerative Clustering?
It will group places with least distance
There are three key questions that needs to be answered
How do we determine the nearness of clusters?
How do we represent a cluster of more than one point?
49. What is Agglomerative Clustering?
It will group places with least distance
There are three key questions that needs to be answered
How do we represent a cluster of more than one point?
How do we determine the nearness of clusters?
When to stop combining clusters?
50. What is Agglomerative Clustering?
It will group places with least distance
(1,2)
(2,1)
(0,0)
(4,1)
(5,3)
(5,0)
Let’s assume that we have 6 points in a Euclidean space
51. What is Agglomerative Clustering?
It will group places with least distance
(1,2)
(2,1)
(0,0)
(4,1)
(5,3)
(5,0)
Let’s assume that we have 6 points in a Euclidean space
?
How do we
represent a cluster
of more than one
point?
52. What is Agglomerative Clustering?
It will group places with least distance
(1,2)
(2,1)
(0,0)
(4,1)
(5,3)
(5,0)
Let’s assume that we have 6 points in a Euclidean space
We make use of
centroids which is
the average of it’s
points
53. What is Agglomerative Clustering?
It will group places with least distance
(1,2)
(2,1)
(0,0)
(4,1)
(5,3)
(5,0)
Let’s assume that we have 6 points in a Euclidean space
54. What is Agglomerative Clustering?
It will group places with least distance
(1,2)
(2,1)
(0,0)
(4,1)
(5,3)
(5,0)
Let’s assume that we have 6 points in a Euclidean space
(1.5,1.5)
55. What is Agglomerative Clustering?
It will group places with least distance
(1,2)
(2,1)
(0,0)
(4,1)
(5,3)
(5,0)
Let’s assume that we have 6 points in a Euclidean space
(1.5,1.5)
56. What is Agglomerative Clustering?
It will group places with least distance
(1,2)
(2,1)
(0,0)
(4,1)
(5,3)
(5,0)
Let’s assume that we have 6 points in a Euclidean space
(1.5,1.5)
(4.5,0.5)
57. What is Agglomerative Clustering?
It will group places with least distance
(1,2)
(2,1)
(0,0)
(4,1)
(5,3)
(5,0)
Let’s assume that we have 6 points in a Euclidean space
(1.5,1.5)
(4.5,0.5)
(1,1)
58. What is Agglomerative Clustering?
It will group places with least distance
(1,2)
(2,1)
(0,0)
(4,1)
(5,3)
(5,0)
Let’s assume that we have 6 points in a Euclidean space
(1.5,1.5)
(4.5,0.5)
(4.7,1.3)
(1,1)
59. What is Agglomerative Clustering?
It will group places with least distance
(1,2)
(2,1)
(0,0)
(4,1)
(5,3)
(5,0)
Let’s assume that we have 6 points in a Euclidean space
(1.5,1.5)
(4.5,0.5)
(4.7,1.3)
(1,1)
60. What is Agglomerative Clustering?
It will group places with least distance
(1,2)
(2,1)
(0,0)
(4,1)
(5,3)
(5,0)
Let’s assume that we have 6 points in a Euclidean space
(1.5,1.5)
(4.5,0.5)
(4.7,1.3)
(1,1)
?
When to stop
combining clusters?
61. What is Agglomerative Clustering?
It will group places with least distance
There are many approaches to it
62. What is Agglomerative Clustering?
It will group places with least distance
There are many approaches to it
Approach 1: Pick a number of clusters(k) upfront
We decide the number of clusters required in the beginning and we terminate when we
reach the value(k)
63. What is Agglomerative Clustering?
It will group places with least distance
There are many approaches to it
Possible Challenges
This only makes sense when we know about the data
Approach 1: Pick a number of clusters(k) upfront
We decide the number of clusters required in the beginning and we terminate when we
reach the value(k)
64. What is Agglomerative Clustering?
It will group places with least distance
There are many approaches to it
Approach 2: Stop when the next merge would create a cluster with low “cohesion”
65. What is Agglomerative Clustering?
It will group places with least distance
There are many approaches to it
Approach 2: Stop when the next merge would create a cluster with low “cohesion”
We keep clustering till the next merge of clusters creates a bad cluster/low cohesion
66. What is Agglomerative Clustering?
It will group places with least distance
There are many approaches to it
Approach 2: Stop when the next merge would create a cluster with low “cohesion”
We keep clustering till the next merge of clusters creates a bad cluster/low cohesion
?
But, how is cohesion
defined?
67. What is Agglomerative Clustering?
It will group places with least distance
There are many approaches to it
Approach 2: Stop when the next merge would create a cluster with low “cohesion”
We keep clustering till the next merge of clusters creates a bad cluster/low cohesion
?
Approach 3.1: Diameter of a cluster
• Diameter is the maximum distance between any pair of points in cluster
68. What is Agglomerative Clustering?
It will group places with least distance
There are many approaches to it
Approach 2: Stop when the next merge would create a cluster with low “cohesion”
We keep clustering till the next merge of clusters creates a bad cluster/low cohesion
?
Approach 3.1: Diameter of a cluster
• Diameter is the maximum distance between any pair of points in cluster
• We terminate when the diameter of a new cluster exceeds the threshold
69. What is Agglomerative Clustering?
It will group places with least distance
There are many approaches to it
Approach 2: Stop when the next merge would create a cluster with low “cohesion”
We keep clustering till the next merge of clusters creates a bad cluster/low cohesion
?
Approach 3.1: Radius of a cluster
70. What is Agglomerative Clustering?
It will group places with least distance
There are many approaches to it
Approach 2: Stop when the next merge would create a cluster with low “cohesion”
We keep clustering till the next merge of clusters creates a bad cluster/low cohesion
?
Approach 3.1: Radius of a cluster
• Radius is the maximum distance of a point from centroid
71. What is Agglomerative Clustering?
It will group places with least distance
There are many approaches to it
Approach 2: Stop when the next merge would create a cluster with low “cohesion”
We keep clustering till the next merge of clusters creates a bad cluster/low cohesion
?
Approach 3.1: Radius of a cluster
• Radius is the maximum distance of a point from centroid
• We terminate when the diameter of a new cluster exceeds the threshold
73. What is Divisive Clustering?
It will group places with least distance
Divisive Clustering approach begins with the whole set and proceeds to divide it into smaller clusters
74. What is Divisive Clustering?
It will group places with least distance
Convergence
Step 1
• Start with a single cluster composed of all the data points
Step 2
Divisive Clustering approach begins with the whole set and proceeds to divide it into smaller clusters
75. What is Divisive Clustering?
It will group places with least distance
Convergence
Step 1
• Start with a single cluster composed of all the data points
• Split it into different clustersStep 2
Divisive Clustering approach begins with the whole set and proceeds to divide it into smaller clusters
76. What is Divisive Clustering?
It will group places with least distance
Convergence
Step 2
Step 1
• Start with a single cluster composed of all the data points
• This can be done using Monothethic divisive methods
• Split it into different clusters
Divisive Clustering approach begins with the whole set and proceeds to divide it into smaller clusters
77. What is Divisive Clustering?
It will group places with least distance
Convergence
Step 1
• Start with a single cluster composed of all the data points
• Split this into different clusters
• This can be done using Monothethic divisive methods
Step 2
?
What is monothetic divisive method?
Divisive Clustering approach begins with the whole set and proceeds to divide it into smaller clusters
78. What is Divisive Clustering?
It will group places with least distance
Convergence
Step 1
• Start with a single cluster composed of all the data points
• There are two ways to do this
1. Monothethic divisive methods
2. Polythetic divisive methods
?
A,B,C,D,E,F
• Obtain all possible splits into two clusters
Divisive Clustering approach begins with the whole set and proceeds to divide it into smaller clusters
79. What is Divisive Clustering?
It will group places with least distance
Convergence
Step 1
• Start with a single cluster composed of all the data points
?
• Obtain all possible splits into two clusters
A,B,C,D,E,F
C,D,E,F
A,B
Divisive Clustering approach begins with the whole set and proceeds to divide it into smaller clusters
80. What is Divisive Clustering?
It will group places with least distance
Convergence
Step 1
• Start with a single cluster composed of all the data points
• Split this into different clusters
• There are two ways to do this
?
• Obtain all possible splits into two clusters
A,B,C,D,E,F
A,D,F
C,D,E,F
A,B
B,C,E
Divisive Clustering approach begins with the whole set and proceeds to divide it into smaller clusters
81. What is Divisive Clustering?
It will group places with least distance
Convergence
Step 1
• Start with a single cluster composed of all the data points
• Split this into different clusters
• There are two ways to do this
1. Monothethic divisive methods
2. Polythetic divisive methods
?
• Obtain all possible splits into two clusters
A,B,C,D,E,F
A,D,F
C,D,E,F
A,B
B,C,E
Divisive Clustering approach begins with the whole set and proceeds to divide it into smaller clusters
A,B,C
D,E,F
82. What is Divisive Clustering?
It will group places with least distance
Convergence
Step 1
• Start with a single cluster composed of all the data points
• There are two ways to do this
1. Monothethic divisive methods
2. Polythetic divisive methods
?
• For each split compute cluster sum of squares
Divisive Clustering approach begins with the whole set and proceeds to divide it into smaller clusters
83. What is Divisive Clustering?
It will group places with least distance
Convergence
Step 1
• Start with a single cluster composed of all the data points
• There are two ways to do this
1. Monothethic divisive methods
2. Polythetic divisive methods
?
• For each split compute cluster sum of squares
• We select the cluster with largest sum of squares
Divisive Clustering approach begins with the whole set and proceeds to divide it into smaller clusters
84. What is Divisive Clustering?
It will group places with least distance
Convergence
Step 1
• Start with a single cluster composed of all the data points
?
• Let’s assume that the sum of squared distance is largest for 3rd split
A,B,C,D,E,F
Divisive Clustering approach begins with the whole set and proceeds to divide it into smaller clusters
85. What is Divisive Clustering?
It will group places with least distance
Convergence
Step 1
• Start with a single cluster composed of all the data points
?
• We divide it into two clusters
A,B,C
A,B,C,D,E,F
Divisive Clustering approach begins with the whole set and proceeds to divide it into smaller clusters
86. What is Divisive Clustering?
It will group places with least distance
Convergence
Step 1
• Start with a single cluster composed of all the data points
?
A,B,C D,E,F
A,B,C,D,E,F
Divisive Clustering approach begins with the whole set and proceeds to divide it into smaller clusters
• We divide it into two clusters
87. What is Divisive Clustering?
It will group places with least distance
Convergence
Step 1
• Start with a single cluster composed of all the data points
?
A,B,C D,E,F
A B,C
A,B,C,D,E,F
Divisive Clustering approach begins with the whole set and proceeds to divide it into smaller clusters
• We divide it into two clusters
88. What is Divisive Clustering?
It will group places with least distance
Convergence
Step 1
• Start with a single cluster composed of all the data points
?
A,B,C D,E,F
A B,C
A,B,C,D,E,F
A,B,C D,E,F
A B,C D E,F
A,B,C,D,E,F
Divisive Clustering approach begins with the whole set and proceeds to divide it into smaller clusters
• We divide it into two clusters
89. What is Divisive Clustering?
It will group places with least distance
Convergence
Step 1
• Start with a single cluster composed of all the data points
?
A,B,C D,E,F
A B,C D E,F
A B C
A,B,C,D,E,F
Divisive Clustering approach begins with the whole set and proceeds to divide it into smaller clusters
• We divide it into two clusters
90. What is Divisive Clustering?
It will group places with least distance
Convergence
Step 1
• Start with a single cluster composed of all the data points
?
• We terminate when every data point is it’s own cluster
A,B,C D,E,F
A B,C D E,F
A B C D E F
A,B,C,D,E,F
Divisive Clustering approach begins with the whole set and proceeds to divide it into smaller clusters
95. Demo: Hierarchical Clustering
Problem Statement
• To group petroleum companies based on their sales
Steps?
• Create a scatter plot
• Import the dataset
96. Demo: Hierarchical Clustering
Problem Statement
• To group petroleum companies based on their sales
Steps?
• Create a scatter plot
• Import the dataset
• Normalize the data
97. Demo: Hierarchical Clustering
Problem Statement
• To group petroleum companies based on their sales
Steps?
• Create a scatter plot
• Import the dataset
• Normalize the data
• Calculate Euclidean Distance
98. Demo: Hierarchical Clustering
Problem Statement
• To group petroleum companies based on their sales
Steps?
• Create a scatter plot
• Import the dataset
• Normalize the data
• Calculate Euclidean Distance
• Create a dendogram
99. Demo: Hierarchical Clustering
Problem Statement
• To group petroleum companies based on their sales
Steps?
• Create a scatter plot
• Import the dataset
• Normalize the data
• Calculate Euclidean Distance
• Create a dendogram
• Cluster into groups