A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
GENERATIVE AI, THE FUTURE OF PRODUCTIVITYAndre Muscat
Discuss the impact and opportunity of using Generative AI to support your development and creative teams
* Explore business challenges in content creation
* Cost-per-unit of different types of content
* Use AI to reduce cost-per-unit
* New partnerships being formed that will have a material impact on the way we search and engage with content
Part 4 of a 9 Part Research Series named "What matters in AI" published on www.andremuscat.com
Introduction To Artificial Intelligence PowerPoint Presentation SlidesSlideTeam
Introduction to Artificial Intelligence is for the mid level managers giving information about what is AI, AI levels, types of AI, where AI is used. You can also know the difference between AI vs Machine learning vs Deep learning to understand expert system in a better way for business growth. https://bit.ly/3er7KWI
A journey into the business world of artificial intelligence. Explore at a high-level ongoing business experiments in creating new value.
* Review AI as a priority for value generation
* Explore ongoing experimentation
* Touch on how businesses are monetising AI
* Understand the intent of adoption by industries
* Discuss on the state of customer trust in AI
Part 1 of a 9 Part Research Series named "What matters in AI" published on https://www.andremuscat.com
ChatGPT What It Is and How Writers Can Use It.pdfAdsy
Have you heard of ChatGPT? This smart model seems to change the way we work in the content marketing field.
We've investigated what this AI tool can do regarding content writing and ready to share the results.
Check this presentation to learn how this chatbot can assist you with content creation.
ChatGPT is a natural language processing model created by OpenAI that can generate human-like responses to text-based conversations. It uses deep learning and was pre-trained on vast amounts of text to understand language. Performance is evaluated using metrics like perplexity, accuracy, fluency and human evaluation. There are ethical concerns around copyright, personal data, bias and how the training data was obtained. OpenAI has introduced a paid ChatGPT Plus subscription with additional features while maintaining the free version.
The document discusses various AI tools from OpenAI like GPT-3 and DALL-E 2, as well as ChatGPT. It explores how search engines are using AI and things to consider around AI-generated content. Potential SEO uses of ChatGPT are also presented, such as generating content at scale, conducting topic research, and automating basic coding tasks. The document encourages further reading on using ChatGPT for SEO purposes.
Hello guys . im yuvraj . recently in my collage they gave a task to make pptx of topic : ChatGPT . So i would like to share with you guys ! i hope someone will be get help from this ..and dont forget to Rate my PPTX ..THANK YOU
ChatGPT is an AI chatbot created by OpenAI to conduct natural conversations. It is based on GPT-3, an AI language model trained on vast amounts of online text. To access ChatGPT, users create an OpenAI account and can then ask questions in a chat interface. ChatGPT aims to be helpful, harmless, and honest but has some limitations, as it cannot experience the world in the same way humans can.
GENERATIVE AI, THE FUTURE OF PRODUCTIVITYAndre Muscat
Discuss the impact and opportunity of using Generative AI to support your development and creative teams
* Explore business challenges in content creation
* Cost-per-unit of different types of content
* Use AI to reduce cost-per-unit
* New partnerships being formed that will have a material impact on the way we search and engage with content
Part 4 of a 9 Part Research Series named "What matters in AI" published on www.andremuscat.com
Introduction To Artificial Intelligence PowerPoint Presentation SlidesSlideTeam
Introduction to Artificial Intelligence is for the mid level managers giving information about what is AI, AI levels, types of AI, where AI is used. You can also know the difference between AI vs Machine learning vs Deep learning to understand expert system in a better way for business growth. https://bit.ly/3er7KWI
A journey into the business world of artificial intelligence. Explore at a high-level ongoing business experiments in creating new value.
* Review AI as a priority for value generation
* Explore ongoing experimentation
* Touch on how businesses are monetising AI
* Understand the intent of adoption by industries
* Discuss on the state of customer trust in AI
Part 1 of a 9 Part Research Series named "What matters in AI" published on https://www.andremuscat.com
ChatGPT What It Is and How Writers Can Use It.pdfAdsy
Have you heard of ChatGPT? This smart model seems to change the way we work in the content marketing field.
We've investigated what this AI tool can do regarding content writing and ready to share the results.
Check this presentation to learn how this chatbot can assist you with content creation.
ChatGPT is a natural language processing model created by OpenAI that can generate human-like responses to text-based conversations. It uses deep learning and was pre-trained on vast amounts of text to understand language. Performance is evaluated using metrics like perplexity, accuracy, fluency and human evaluation. There are ethical concerns around copyright, personal data, bias and how the training data was obtained. OpenAI has introduced a paid ChatGPT Plus subscription with additional features while maintaining the free version.
The document discusses various AI tools from OpenAI like GPT-3 and DALL-E 2, as well as ChatGPT. It explores how search engines are using AI and things to consider around AI-generated content. Potential SEO uses of ChatGPT are also presented, such as generating content at scale, conducting topic research, and automating basic coding tasks. The document encourages further reading on using ChatGPT for SEO purposes.
Hello guys . im yuvraj . recently in my collage they gave a task to make pptx of topic : ChatGPT . So i would like to share with you guys ! i hope someone will be get help from this ..and dont forget to Rate my PPTX ..THANK YOU
ChatGPT is an AI chatbot created by OpenAI to conduct natural conversations. It is based on GPT-3, an AI language model trained on vast amounts of online text. To access ChatGPT, users create an OpenAI account and can then ask questions in a chat interface. ChatGPT aims to be helpful, harmless, and honest but has some limitations, as it cannot experience the world in the same way humans can.
What is AI and how it works? What is early history of AI. what are risks and benefits of AI? Current status and future of AI. General perceptions about AI. Achievement of AI. Will AI be more beneficent or more destructive?
Launching a startup isn't easy. At each stage of scaling - from founding to product-market fit, from product-market fit to hyper growth, and from hyper growth to maturity - entrepreneurs face unique challenges. Greylock Partners hosted an event, called Greyscale, focused on these challenges at each stage. In the opening keynote, Jerry Chen of Greylock Partners discusses the state of enterprise software after the first quarter of 2016. He summarizes the private and public markets, M&A activity, and explains how this climate affects the startup environment.
Love reading comics? You're not the only one. What about these stories about super-beings keep our eyes glued to the pages and our minds salivating for more? We explore in this deck how comic writers use these storytelling techniques and how you can apply it in your presentation.
One thing to keep in mind is that ChatGPT, like all language models, is not perfect and may not always produce the desired results. Therefore, there are several things that businesses should consider before using ChatGPT. Here is a detailed explanation of some of the key limitations of ChatGPT. To know all problems of ChatGPT then visit blog post at https://windzoon.com/blog/chatgpt-for-small-businesses/
How to Become a Data Scientist
SF Data Science Meetup, June 30, 2014
Video of this talk is available here: https://www.youtube.com/watch?v=c52IOlnPw08
More information at: http://www.zipfianacademy.com
Zipfian Academy @ Crowdflower
In the US, people are already implementing the use of converstaionl AI, ChatGPT in everydy mundane tasks. Implementation is not only limited to that. Various industries are also using this revolutionary technology for maintaining a superior customer experience. People are also criticizing ChatGPT for creating employment threats and also being unethical in it's answers. The technology is being widely applauded but everything has certain pain points associated with it.
What is ChatGPT and how can we use it? This is a talk given at Affiliate Summit West -- January 2023 to explain what ChatGPT is and isn't and how we can use it in Search.
All images were created using Dall-e.
What Is Data Science? | Introduction to Data Science | Data Science For Begin...Simplilearn
This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
The Future Of Work & The Work Of The FutureArturo Pelayo
What Happens When Robots And Machines Learn On Their Own?
This slide deck is an introduction to exponential technologies for an audience of designers and developers of workforce training materials.
The Blended Learning And Technologies Forum (BLAT Forum) is a quarterly event in Auckland, New Zealand that welcomes practitioners, designers and developers of blended learning instructional deliverables across different industries of the New Zealand economy.
Welcome to my SlideShare presentation on ChatGPT, a powerful language model based on the GPT-3.5 architecture.
In this presentation, I will introduce you to ChatGPT and explore its features and capabilities. ChatGPT is a state-of-the-art language model that can understand natural language and generate responses that are highly relevant and accurate.
I will discuss the underlying technology behind ChatGPT, including its neural network architecture and training process. I will also highlight the benefits of using ChatGPT, such as its ability to understand complex language and its potential applications in various industries.
Additionally, I will share examples of how ChatGPT can be used to improve customer service, create conversational interfaces, and generate human-like responses in various applications.
In conclusion, ChatGPT is a powerful tool for businesses and individuals looking to enhance their communication capabilities. Its advanced language understanding and generation capabilities make it an ideal solution for a variety of use cases. I hope this presentation has been informative and has given you a better understanding of the capabilities of ChatGPT.
This slide is "How To Chat Gpt Works?". Here you will learn more things about chat Gpt. Here I discussed what is chat gpt, history of chat gpt, Algorithms & , chat gpt uses technology, advantage & disadvantage of chat gpt, limitations of chat gpt & most important point future works of chat gpt. And all reference link is here. Open & visit the link and learn more about chat Gpt. So , This slide will help for your learning .
N.B: If any information wrong then contact with us and please let me know, I will update the slide.
So Please download the slide and put here your review.
ChatGPT is an AI chatbot created by OpenAI that can understand questions and provide answers in natural language. It was trained using reinforcement learning from human feedback on massive text datasets. In its initial release, ChatGPT is free to use but OpenAI may later monetize it due to high operating costs. While very capable, ChatGPT has limitations like an inability to gather new information or think critically.
Generative artificial intelligence (AI) models are reinventing communication, content creation, and information access. In this roadmap, presented at Bessemer's annual Seed Summit, Partner Talia Goldberg explores the technological advancements driving AI solutions and how these changes are opening up new promising area of investment.
Learn more about Generative AI:
https://www.bvp.com/atlas/is-ai-gener...
https://www.bvp.com/atlas/roadmap-the...
https://www.bvp.com/atlas/entering-th...
Subscribe for venture insights: https://bessemervp.team/subscribe
About Bessemer Venture Partners —
We help entrepreneurs lay strong foundations to build and forge long-standing companies. With more than 135 IPOs and 200 portfolio companies in the enterprise, consumer and healthcare spaces, Bessemer supports founders and CEOs from their early days through every stage of growth. Our global portfolio includes Pinterest, Shopify, Twilio, Yelp, LinkedIn, PagerDuty, DocuSign, Wix, Fiverr and Toast, and has more than $20 billion of assets under management.
Connect with us —
Subscribe to our channel: https://bit.ly/3oVeW4k
Visit our website bvp.com: https://bit.ly/3bzFXaE
Sign up for our newsletter: https://bit.ly/3SoVY3D
Find Bessemer on LinkedIn: https://bit.ly/3zZpGoS
Find Bessemer on Twitter: https://bit.ly/3JsVJAF
Find Bessemer on Instagram: https://bit.ly/3BH8but
ChatGPT and the Future of Work - Clark Boyd Clark Boyd
Everyone is in agreement that ChatGPT (and other generative AI tools) will shape the future of work. Yet there is little consensus on exactly how, when, and to what extent this technology will change our world.
Businesses that extract maximum value from ChatGPT will use it as a collaborative tool for everything from brainstorming to technical maintenance.
For individuals, now is the time to pinpoint the skills the future professional will need to thrive in the AI age.
Check out this presentation to understand what ChatGPT is, how it will shape the future of work, and how you can prepare to take advantage.
The document describes a 10 module data science course covering topics such as introduction to data science, machine learning techniques using R, Hadoop architecture, and Mahout algorithms. The course includes live online classes, recorded lectures, quizzes, projects, and a certificate. Each module covers specific data science topics and techniques. The document provides details on the course content, objectives, and topics covered in module 1 which includes an introduction to data science, its components, use cases, and how to integrate R and Hadoop. Examples of data science applications in various domains like healthcare, retail, and social media are also presented.
1. The document discusses machine learning and provides an overview of the seven steps of machine learning including gathering data, preparing data, choosing a model, training the model, evaluating the model, tuning hyperparameters, and making predictions.
2. It describes tips for data preparation such as exploring data for trends and issues, formatting data consistently, and handling missing values, outliers, and imbalanced data.
3. Techniques for outlier removal are discussed including clustering-based, nearest-neighbor based, density-based, graphical, and statistical approaches. Limitations and challenges of outlier removal are noted.
This document discusses AI and ChatGPT. It begins with an introduction to David Cieslak and his company RKL eSolutions, which provides ERP sales and consulting. It then provides definitions for key AI concepts like artificial intelligence, generative AI, large language models, and ChatGPT. The document discusses OpenAI's ChatGPT tool and how it works. It covers prompts, commands, and potential uses and impacts of generative AI technologies. Finally, it discusses concerns regarding generative AI and the future of life institute's call for more oversight of advanced AI.
ChatGPT is a chatbot developed by OpenAI that was launched in November 2022 and can answer questions across many domains using its training on 570GB of internet text data. It works by taking user questions and searching its database to provide quick answers, and allows users to provide feedback to continuously update its knowledge. While ChatGPT can give personalized responses and detailed answers freely, limitations include its potential to reduce human creativity, the risk of believing incorrect answers, its current support only for English, and its inability to answer all questions due to ending its training in March 2022.
Gartner provides webinars on various topics related to technology. This webinar discusses generative AI, which refers to AI techniques that can generate new unique artifacts like text, images, code, and more based on training data. The webinar covers several topics related to generative AI, including its use in novel molecule discovery, AI avatars, and automated content generation. It provides examples of how generative AI can benefit various industries and recommendations for organizations looking to utilize this emerging technology.
This knolx is about an introduction to machine learning, wherein we see the basics of various different algorithms. This knolx isn't a complete intro to ML but can be a good starting point for anyone who wants to start in ML. In the end, we will take a look at the demo wherein we will analyze the FIFA dataset going through the understanding of various data analysis techniques and use an ML algorithm to derive 5 players that are similar to each other.
what-is-machine-learning-and-its-importance-in-todays-world.pdfTemok IT Services
Machine Learning is an AI method for teaching computers to learn from their mistakes. Machine learning algorithms can “learn” data directly from data without using an equation as a model by employing computational methods.
https://bit.ly/RightContactDataSpecialists
What is AI and how it works? What is early history of AI. what are risks and benefits of AI? Current status and future of AI. General perceptions about AI. Achievement of AI. Will AI be more beneficent or more destructive?
Launching a startup isn't easy. At each stage of scaling - from founding to product-market fit, from product-market fit to hyper growth, and from hyper growth to maturity - entrepreneurs face unique challenges. Greylock Partners hosted an event, called Greyscale, focused on these challenges at each stage. In the opening keynote, Jerry Chen of Greylock Partners discusses the state of enterprise software after the first quarter of 2016. He summarizes the private and public markets, M&A activity, and explains how this climate affects the startup environment.
Love reading comics? You're not the only one. What about these stories about super-beings keep our eyes glued to the pages and our minds salivating for more? We explore in this deck how comic writers use these storytelling techniques and how you can apply it in your presentation.
One thing to keep in mind is that ChatGPT, like all language models, is not perfect and may not always produce the desired results. Therefore, there are several things that businesses should consider before using ChatGPT. Here is a detailed explanation of some of the key limitations of ChatGPT. To know all problems of ChatGPT then visit blog post at https://windzoon.com/blog/chatgpt-for-small-businesses/
How to Become a Data Scientist
SF Data Science Meetup, June 30, 2014
Video of this talk is available here: https://www.youtube.com/watch?v=c52IOlnPw08
More information at: http://www.zipfianacademy.com
Zipfian Academy @ Crowdflower
In the US, people are already implementing the use of converstaionl AI, ChatGPT in everydy mundane tasks. Implementation is not only limited to that. Various industries are also using this revolutionary technology for maintaining a superior customer experience. People are also criticizing ChatGPT for creating employment threats and also being unethical in it's answers. The technology is being widely applauded but everything has certain pain points associated with it.
What is ChatGPT and how can we use it? This is a talk given at Affiliate Summit West -- January 2023 to explain what ChatGPT is and isn't and how we can use it in Search.
All images were created using Dall-e.
What Is Data Science? | Introduction to Data Science | Data Science For Begin...Simplilearn
This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
The Future Of Work & The Work Of The FutureArturo Pelayo
What Happens When Robots And Machines Learn On Their Own?
This slide deck is an introduction to exponential technologies for an audience of designers and developers of workforce training materials.
The Blended Learning And Technologies Forum (BLAT Forum) is a quarterly event in Auckland, New Zealand that welcomes practitioners, designers and developers of blended learning instructional deliverables across different industries of the New Zealand economy.
Welcome to my SlideShare presentation on ChatGPT, a powerful language model based on the GPT-3.5 architecture.
In this presentation, I will introduce you to ChatGPT and explore its features and capabilities. ChatGPT is a state-of-the-art language model that can understand natural language and generate responses that are highly relevant and accurate.
I will discuss the underlying technology behind ChatGPT, including its neural network architecture and training process. I will also highlight the benefits of using ChatGPT, such as its ability to understand complex language and its potential applications in various industries.
Additionally, I will share examples of how ChatGPT can be used to improve customer service, create conversational interfaces, and generate human-like responses in various applications.
In conclusion, ChatGPT is a powerful tool for businesses and individuals looking to enhance their communication capabilities. Its advanced language understanding and generation capabilities make it an ideal solution for a variety of use cases. I hope this presentation has been informative and has given you a better understanding of the capabilities of ChatGPT.
This slide is "How To Chat Gpt Works?". Here you will learn more things about chat Gpt. Here I discussed what is chat gpt, history of chat gpt, Algorithms & , chat gpt uses technology, advantage & disadvantage of chat gpt, limitations of chat gpt & most important point future works of chat gpt. And all reference link is here. Open & visit the link and learn more about chat Gpt. So , This slide will help for your learning .
N.B: If any information wrong then contact with us and please let me know, I will update the slide.
So Please download the slide and put here your review.
ChatGPT is an AI chatbot created by OpenAI that can understand questions and provide answers in natural language. It was trained using reinforcement learning from human feedback on massive text datasets. In its initial release, ChatGPT is free to use but OpenAI may later monetize it due to high operating costs. While very capable, ChatGPT has limitations like an inability to gather new information or think critically.
Generative artificial intelligence (AI) models are reinventing communication, content creation, and information access. In this roadmap, presented at Bessemer's annual Seed Summit, Partner Talia Goldberg explores the technological advancements driving AI solutions and how these changes are opening up new promising area of investment.
Learn more about Generative AI:
https://www.bvp.com/atlas/is-ai-gener...
https://www.bvp.com/atlas/roadmap-the...
https://www.bvp.com/atlas/entering-th...
Subscribe for venture insights: https://bessemervp.team/subscribe
About Bessemer Venture Partners —
We help entrepreneurs lay strong foundations to build and forge long-standing companies. With more than 135 IPOs and 200 portfolio companies in the enterprise, consumer and healthcare spaces, Bessemer supports founders and CEOs from their early days through every stage of growth. Our global portfolio includes Pinterest, Shopify, Twilio, Yelp, LinkedIn, PagerDuty, DocuSign, Wix, Fiverr and Toast, and has more than $20 billion of assets under management.
Connect with us —
Subscribe to our channel: https://bit.ly/3oVeW4k
Visit our website bvp.com: https://bit.ly/3bzFXaE
Sign up for our newsletter: https://bit.ly/3SoVY3D
Find Bessemer on LinkedIn: https://bit.ly/3zZpGoS
Find Bessemer on Twitter: https://bit.ly/3JsVJAF
Find Bessemer on Instagram: https://bit.ly/3BH8but
ChatGPT and the Future of Work - Clark Boyd Clark Boyd
Everyone is in agreement that ChatGPT (and other generative AI tools) will shape the future of work. Yet there is little consensus on exactly how, when, and to what extent this technology will change our world.
Businesses that extract maximum value from ChatGPT will use it as a collaborative tool for everything from brainstorming to technical maintenance.
For individuals, now is the time to pinpoint the skills the future professional will need to thrive in the AI age.
Check out this presentation to understand what ChatGPT is, how it will shape the future of work, and how you can prepare to take advantage.
The document describes a 10 module data science course covering topics such as introduction to data science, machine learning techniques using R, Hadoop architecture, and Mahout algorithms. The course includes live online classes, recorded lectures, quizzes, projects, and a certificate. Each module covers specific data science topics and techniques. The document provides details on the course content, objectives, and topics covered in module 1 which includes an introduction to data science, its components, use cases, and how to integrate R and Hadoop. Examples of data science applications in various domains like healthcare, retail, and social media are also presented.
1. The document discusses machine learning and provides an overview of the seven steps of machine learning including gathering data, preparing data, choosing a model, training the model, evaluating the model, tuning hyperparameters, and making predictions.
2. It describes tips for data preparation such as exploring data for trends and issues, formatting data consistently, and handling missing values, outliers, and imbalanced data.
3. Techniques for outlier removal are discussed including clustering-based, nearest-neighbor based, density-based, graphical, and statistical approaches. Limitations and challenges of outlier removal are noted.
This document discusses AI and ChatGPT. It begins with an introduction to David Cieslak and his company RKL eSolutions, which provides ERP sales and consulting. It then provides definitions for key AI concepts like artificial intelligence, generative AI, large language models, and ChatGPT. The document discusses OpenAI's ChatGPT tool and how it works. It covers prompts, commands, and potential uses and impacts of generative AI technologies. Finally, it discusses concerns regarding generative AI and the future of life institute's call for more oversight of advanced AI.
ChatGPT is a chatbot developed by OpenAI that was launched in November 2022 and can answer questions across many domains using its training on 570GB of internet text data. It works by taking user questions and searching its database to provide quick answers, and allows users to provide feedback to continuously update its knowledge. While ChatGPT can give personalized responses and detailed answers freely, limitations include its potential to reduce human creativity, the risk of believing incorrect answers, its current support only for English, and its inability to answer all questions due to ending its training in March 2022.
Gartner provides webinars on various topics related to technology. This webinar discusses generative AI, which refers to AI techniques that can generate new unique artifacts like text, images, code, and more based on training data. The webinar covers several topics related to generative AI, including its use in novel molecule discovery, AI avatars, and automated content generation. It provides examples of how generative AI can benefit various industries and recommendations for organizations looking to utilize this emerging technology.
This knolx is about an introduction to machine learning, wherein we see the basics of various different algorithms. This knolx isn't a complete intro to ML but can be a good starting point for anyone who wants to start in ML. In the end, we will take a look at the demo wherein we will analyze the FIFA dataset going through the understanding of various data analysis techniques and use an ML algorithm to derive 5 players that are similar to each other.
what-is-machine-learning-and-its-importance-in-todays-world.pdfTemok IT Services
Machine Learning is an AI method for teaching computers to learn from their mistakes. Machine learning algorithms can “learn” data directly from data without using an equation as a model by employing computational methods.
https://bit.ly/RightContactDataSpecialists
The document discusses machine learning methods including supervised learning, unsupervised learning, and reinforcement learning. It provides examples of how each method is used, such as using historical data for prediction in supervised learning and organizing unlabeled data in unsupervised learning. Random forest, an ensemble supervised learning algorithm, is also summarized. It states random forest combines decision trees for improved performance and discusses its use in sectors like banking, medicine, land use, and marketing.
Machine learning applications nurturing growth of various business domainsShrutika Oswal
Machine learning is a science in which machines are becoming smarter and helping humans to make the best decisions based on previous data recommended practices. This technique is not new but is occupying fresh momentum. Machine Learning Algorithm learns from the previous records and analyses the data. Without any human interrupt, it will generate its own recommendation. A machine will add that recommendation as experience in its database and use it for further processing. In short, the machine learns from its own experience and gives you better and better output.
Machine learning is an iterative process as the more data added to machines learn from fresh feeds of data and then independently adapt new features to handle new data without constant human intervention. Machine learning was earlier used to predict what’s happing with the business but now the machine learning algorithm will suggest what action needs be taken by moving our business forward.
This PowerPoint presentation presents the results of a literature survey of machine learning applications nurturing the growth of various business domains. More specifically, it gives a brief introduction of Machine Learning, four major types of Machine Learning, enhancement in various business domains by the use of various machine learning algorithms.
This document provides an introduction to machine learning, covering various topics. It defines machine learning as a branch of artificial intelligence that uses algorithms and data to enable machines to learn. It discusses different types of machine learning, including supervised, unsupervised, and reinforcement learning. It also covers important machine learning concepts like overfitting, evaluation metrics, and well-posed learning problems. The history of machine learning is reviewed, from early work in the 1950s to recent advances in deep learning.
1. The document summarizes a seminar on machine learning presented by Amit Kumar to the Rajkiya Engineering College.
2. It discusses key machine learning concepts like supervised learning techniques of classification and regression, as well as unsupervised learning techniques like clustering.
3. Applications of machine learning discussed include virtual assistants, social media services, image recognition, and medical diagnosis.
BIG DATA AND MACHINE LEARNING
Big Data is a collection of data that is huge in volume, yet growing exponentially with time. It is a data with so large size and complexity that none of traditional data management tools can store it or process it efficiently. Big data is also a data but with huge size.
This document provides an introduction to machine learning and data science. It discusses key concepts like supervised vs. unsupervised learning, classification algorithms, overfitting and underfitting data. It also addresses challenges like having bad quality or insufficient training data. Python and MATLAB are introduced as suitable software for machine learning projects.
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Machine learning is a sub-field of artificial intelligence (AI) that focuses on creating statistical models and algorithms that allow computers to learn and become more proficient at performing particular tasks. Machine learning algorithms create a mathematical model with the help of historical sample data, or “training data,” that assists in making predictions or judgments without being explicitly programmed.
Machine learning is a sub-field of artificial intelligence (AI) that focuses on creating statistical models and algorithms that allow computers to learn and become more proficient at performing particular tasks. Machine learning algorithms create a mathematical model with the help of historical sample data, or “training data,” that assists in making predictions or judgments without being explicitly programmed.
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Machine learning is a sub-field of artificial intelligence (AI) that focuses on creating statistical models and algorithms that allow computers to learn and become more proficient at performing particular tasks. Machine learning algorithms create a mathematical model with the help of historical sample data, or “training data,” that assists in making predictions or judgments without being explicitly programmed.
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https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
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https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
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5. Challenges deep-dive
Why the Hype Around
Data Science?
● The demand for data scientists will soar by 28% by 2023
● Data scientist roles have grown over 650% since 2012, but
currently, 35,000 people in the US have data science skills,
while hundreds of companies are hiring for those roles.
● Software engineering is a common starting point for
professionals who are in the top five fasting growing jobs today.
● Data Science gives you career flexibility
8. Challenges deep-dive
What is Machine
Learning ?
Machine learning teaches computers to do what comes naturally to
humans and animals: learn from experience. Machine learning
algorithms use computational methods to “learn” information directly
from data without relying on a predetermined equation as a model.
The algorithms adaptively improve their performance as the number
of samples available for learning increases.
9. Challenges deep-dive
A Definition
A computer program is said to learn from experience E with
respect to some task T and some performance measure P if its
performance on T, as measured by P, improves with experience E.
-Tom Mitchell
10. Challenges deep-dive
A Small Question
Suppose we feed a learning algorithm a lot of historical weather
data, and have it learn to predict weather. In this setting, what is
T,P,E?
13. Challenges deep-dive
Machine learning teaches computers to do what comes naturally to
humans and animals: learn from experience. Machine learning
algorithms use computational methods to “learn” information directly
from data without relying on a predetermined equation as a model.
The algorithms adaptively improve their performance as the number
of samples available for learning increases.
Real World
Applications
With the rise in big data, machine learning has become particularly
important for solving problems in areas like these:
● Image processing and computer vision,for face recognition,
motion detection, and object detection
● Computational biology, for tumor detection, drug discovery, and
DNA sequencing
● Energy production, for price and load forecasting
● Automotive, aerospace, and manufacturing, for predictive
maintenance
● Natural language processing
14. Challenges deep-dive
How Machine
Learning Works
Machine learning uses two types of techniques:
● Supervised learning, which trains a model on known input and
output data so that it can predict future outputs
● Unsupervised learning, which finds hidden patterns or intrinsic
structures in input data.
16. Challenges deep-dive
Supervised
Learning
The aim of supervised machine learning is to build a model that
makes predictions based on evidence in the presence of
uncertainty. A supervised learning algorithm takes a known set of
input data and known responses to the data (output) and trains a
model to generate reasonable predictions for the response to new
data
17. Classification - predict discrete responses
Classification models classify input data into categories.for
example, whether an email is genuine or spam, or whether a tumor
is cancerous or benign.
Regression - predict continuous responses
for example, changes in temperature or fluctuations in power
demand. Typical applications include electricity load forecasting and
algorithmic trading.
19. Clustering is the most common unsupervised learning technique. It
is used for exploratory data analysis to find hidden patterns or
groupings in data.Applications for clustering include gene sequence
analysis,market research, and object recognition
20. Knowledge Test
Which of the following would you apply supervised learning to?
1. Given genetic (DNA) data from a person, predict the odds of him/her developing
diabetes over the next 10 years.
2. Given a large dataset of medical records from patients suffering from heart
disease, try to learn whether there might be different clusters of such patients for
which we might tailor separate treatments.
3. Given data on how 1000 medical patients respond to an experimental drug (such
as effectiveness of the treatment, side effects, etc.), discover whether there are
different categories or "types" of patients in terms of how they respond to the
drug, and if so what these categories are.
4. Have a computer examine an audio clip of a piece of music, and classify whether
or not there are vocals (i.e., a human voice singing) in that audio clip, or if it is a
clip of only musical instruments (and no vocals).
21. Knowledge Test
Which of the following questions can be answered using a
classification algorithm?
1. How does the exchange rate depend on the GDP?
2. Does a document contain the handwritten letter S?
3. How can I group supermarket products using purchase
frequency?
22. Knowledge Test
1. Suppose you are working on weather prediction, and you
would like to predict whether or not it will be raining at 5pm
tomorrow. You want to use a learning algorithm for this.Would
you treat this as a classification or a regression problem?
2. Suppose you are working on stock market prediction. You
would like to predict whether or not a certain company will
declare bankruptcy within the next 7 days (by training on data
of similar companies that had previously been at risk of
bankruptcy). Would you treat this as a classification or a
regression problem?
24. Choosing the right algorithm can seem overwhelming
There are dozens of supervised and unsupervised machine
learning algorithms, and each takes a different approach to
learning.
25. There is no best method or one size fits all. Finding the right
algorithm is partly just trial and error
But algorithm selection also depends on the size and type of data
you’re working with, the insights you want to get from the data, and
how those insights will be used.
31. Challenges deep-dive
When should we use
Machine Learning
Consider using machine learning when you have a complex task or
problem involving a large amount of data and lots of variables, but
no existing formula or equation.
32.
33. Knowledge Test
Have a look at the statements below and identify the one which
is not a machine learning problem
1. Given a viewer's shopping habits, recommend a product to
purchase the next time she visits your website.
2. Given the symptoms of a patient, identify her illness.
3. Predict the USD/EUR exchange rate for February 2023.
4. Compute the mean wage of 10 employees for your company.
34. Knowledge Test
Which of the following statements uses a machine learning
model?
1. Determine whether an incoming email is spam or not
2. Obtain the name of last year's FIFIA Ballon d’Or champion
3. Automatically tagging your new Facebook photos
4. Select the student with the highest grade on a statistics course
36. Challenges deep-dive
There is NO
Straight Line
With machine learning there’s rarely a straight line from start to
finish. You’ll find yourself constantly iterating and trying different
ideas and approaches
37. Challenges deep-dive
Machine learning teaches computers to do what comes naturally to
humans and animals: learn from experience. Machine learning
algorithms use computational methods to “learn” information directly
from data without relying on a predetermined equation as a model.
The algorithms adaptively improve their performance as the number
of samples available for learning increases.
Machine Learning
Challenges
● Data comes in all shapes and sizes
● Preprocessing your data might require specialized knowledge
and tools
● It takes time to find the best model to fit the data.
38. Challenges deep-dive
Machine learning teaches computers to do what comes naturally to
humans and animals: learn from experience. Machine learning
algorithms use computational methods to “learn” information directly
from data without relying on a predetermined equation as a model.
The algorithms adaptively improve their performance as the number
of samples available for learning increases.
Questions to Ask
Before Starting
Every machine learning workflow begins with three questions:
● What kind of data are you working with?
● What insights do you want to get from it?
● How and where will those insights be applied?
Your answers to these questions help you decide whether to use
supervised or unsupervised learning.
39. Challenges deep-dive
Machine learning teaches computers to do what comes naturally to
humans and animals: learn from experience. Machine learning
algorithms use computational methods to “learn” information directly
from data without relying on a predetermined equation as a model.
The algorithms adaptively improve their performance as the number
of samples available for learning increases.
Data Science -
Five Questions
There are only five questions that data science answers:
● Is this A or B?
● Is this weird?
● How much – or – How many?
● How is this organized?
● What should I do next?
40. Knowledge Test
Which of the following questions can be answered using a
classification algorithm?
1. How does the exchange rate depend on the GDP?
2. Does a document contain the handwritten letter S?
3. How can I group supermarket products using purchase
frequency?
43. Challenges deep-dive
Machine learning teaches computers to do what comes naturally to
humans and animals: learn from experience. Machine learning
algorithms use computational methods to “learn” information directly
from data without relying on a predetermined equation as a model.
The algorithms adaptively improve their performance as the number
of samples available for learning increases.
Step 1 -
Load the Data
We store the labeled data sets in a text file. A flat file format such as
text or CSV is easy to work with and makes it straightforward to
import data.
Machine learning algorithms aren’t smart enough to tell the
difference between noise and valuable information. Before using the
data for training, we need to make sure it’s clean and complete
44. Challenges deep-dive
Machine learning teaches computers to do what comes naturally to
humans and animals: learn from experience. Machine learning
algorithms use computational methods to “learn” information directly
from data without relying on a predetermined equation as a model.
The algorithms adaptively improve their performance as the number
of samples available for learning increases.
Step 2 -
Preprocess the Data
To preprocess the data we do the following:
● Look for outliers–data points that lie outside the rest of the data
● Check for missing values
● Divide the data into two sets
○ We save part of the data for testing (the test set) and use
the rest (the training set) to build models. This is referred
to as holdout, and is a useful cross-validation technique
45. Challenges deep-dive
Machine learning teaches computers to do what comes naturally to
humans and animals: learn from experience. Machine learning
algorithms use computational methods to “learn” information directly
from data without relying on a predetermined equation as a model.
The algorithms adaptively improve their performance as the number
of samples available for learning increases.
Step 3 -
Derive Features
Deriving features (also known as feature engineering or feature
extraction) turns raw data into information that a machine learning
algorithm can use.
Use feature selection to:
• Improve the accuracy of a machine learning algorithm
• Boost model performance for high-dimensional data sets
• Improve model interpretability
• Prevent overfitting
46. Challenges deep-dive
Machine learning teaches computers to do what comes naturally to
humans and animals: learn from experience. Machine learning
algorithms use computational methods to “learn” information directly
from data without relying on a predetermined equation as a model.
The algorithms adaptively improve their performance as the number
of samples available for learning increases.
Step 4 -
Build and Train Model
● The predefined algorithms and the test data are used for
building the model.
● The training data is used to train and evaluate the model
47. Challenges deep-dive
Machine learning teaches computers to do what comes naturally to
humans and animals: learn from experience. Machine learning
algorithms use computational methods to “learn” information directly
from data without relying on a predetermined equation as a model.
The algorithms adaptively improve their performance as the number
of samples available for learning increases.
Step 5 -
Improve the Model
Improving a model can take two different directions: make the
model simpler or add complexity.
Simplify - reduce the number of features
Add Complexity - make it more fine-tuned
48. Simplify
Popular feature reduction techniques include:
● Correlation matrix – shows the relationship between
variables, so that variables (or features) that are not highly
correlated can be removed.
● Principal component analysis (PCA) - eliminates redundancy
by finding a combination of features that captures key
distinctions between the original features and brings out strong
patterns in the dataset.
● Sequential feature reduction – reduces features iteratively on
the model until there is no improvement in performance
49. Add Complexity
● Use model combination – merge multiple simpler models into
a larger model that is better able to represent the trends in the
data than any of the simpler models could on their own.
● Add more data sources
50. TO DO
● Getting Started
● Familiarize with Maths and
Algorithms
● Select the Infrastructure or
Tool
● Create your profile and
participate in competition