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
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.
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.
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.
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.
For this plenary talk at the Charlotte AI Institute for Smarter Learning, Dr. Cori Faklaris introduces her fellow college educators to the exciting world of generative AI tools. She gives a high-level overview of the generative AI landscape and how these tools use machine learning algorithms to generate creative content such as music, art, and text. She then shares some examples of generative AI tools and demonstrate how she has used some of these tools to enhance teaching and learning in the classroom and to boost her productivity in other areas of academic life.
Generative AI: Past, Present, and Future – A Practitioner's PerspectiveHuahai Yang
Generative AI: Past, Present, and Future – A Practitioner's Perspective
As the academic realm grapples with the profound implications of generative AI
and related applications like ChatGPT, I will present a grounded view from my
experience as a practitioner. Starting with the origins of neural networks in
the fields of logic, psychology, and computer science, I trace its history and
align it within the wider context of the pursuit of artificial intelligence.
This perspective will also draw parallels with historical developments in
psychology. Against this backdrop, I chart a proposed trajectory for the future.
Finally, I provide actionable insights for both academics and enterprising
individuals in the field.
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
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.
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.
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.
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.
For this plenary talk at the Charlotte AI Institute for Smarter Learning, Dr. Cori Faklaris introduces her fellow college educators to the exciting world of generative AI tools. She gives a high-level overview of the generative AI landscape and how these tools use machine learning algorithms to generate creative content such as music, art, and text. She then shares some examples of generative AI tools and demonstrate how she has used some of these tools to enhance teaching and learning in the classroom and to boost her productivity in other areas of academic life.
Generative AI: Past, Present, and Future – A Practitioner's PerspectiveHuahai Yang
Generative AI: Past, Present, and Future – A Practitioner's Perspective
As the academic realm grapples with the profound implications of generative AI
and related applications like ChatGPT, I will present a grounded view from my
experience as a practitioner. Starting with the origins of neural networks in
the fields of logic, psychology, and computer science, I trace its history and
align it within the wider context of the pursuit of artificial intelligence.
This perspective will also draw parallels with historical developments in
psychology. Against this backdrop, I chart a proposed trajectory for the future.
Finally, I provide actionable insights for both academics and enterprising
individuals in the field.
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.
AI and ML Series - Introduction to Generative AI and LLMs - Session 1DianaGray10
Session 1
👉This first session will cover an introduction to Generative AI & harnessing the power of large language models. The following topics will be discussed:
Introduction to Generative AI & harnessing the power of large language models.
What’s generative AI & what’s LLM.
How are we using it in our document understanding & communication mining models?
How to develop a trustworthy and unbiased AI model using LLM & GenAI.
Personal Intelligent Assistant
Speakers:
📌George Roth - AI Evangelist at UiPath
📌Sharon Palawandram - Senior Machine Learning Consultant @ Ashling Partners & UiPath MVP
📌Russel Alfeche - Technology Leader RPA @qBotica & UiPath MVP
Unlocking the Power of Generative AI An Executive's Guide.pdfPremNaraindas1
Generative AI is here, and it can revolutionize your business. With its powerful capabilities, this technology can help companies create more efficient processes, unlock new insights from data, and drive innovation. But how do you make the most of these opportunities?
This guide will provide you with the information and resources needed to understand the ins and outs of Generative AI, so you can make informed decisions and capitalize on the potential. It covers important topics such as strategies for leveraging large language models, optimizing MLOps processes, and best practices for building with Generative AI.
How AI is going to change the world _M.Mujeeb Riaz.pdfMujeeb Riaz
How AI is going to change the world?
"AI: The Future of Our World“
"AI and its Transformative Impact on the World: Understanding the Potential of Chatbots and Conversational AI"
What is Artificial Intelligence and how it works?
What are Chatbots?
What Is ChatGPT?
Difference between chatGPT 3 and chatGPT 4?
Is Jasper artificial intelligence?
What is Character AI and how it works?
How chatGPT is going to change the world?
Why we are calling ChatGPT the future?
The presentation is about the career path in the field of Data Science. Data Science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
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
Using AI chatbots for deep learning and teaching with specific examples to en...Nigel Daly
This talk was given to Senior high School teachers in Taiwan to help them better understand (1) what AI chatbot technology like ChatGPT and Bing Chat is, and (2) how to use it to enhance their own teaching and also their students' learning. Also discussed was how to make prompts and several examples. The examples specifically focused on language learning since the school will soon become a bilingual school. AI technology use was also described in terms of Bloom's taxonomy of learning objectives and connected to ideas of deep learning as advocated by the movement "New Pedagogies for Deep Learning", which the school has become a part of.
Presented at Social Media Breakfast Red Deer. Website and social analytics give you lots of data to look at, but what should you do with it? Learn how to make analytics work for you and understand how your various marketing and communication efforts are having an impact.
(1) The document discusses several computer science topics including data science, artificial intelligence, and cloud computing. (2) It notes that data science has grown in popularity from 2012-2017 due to an ability to better process large volumes of data using statistics, specialized hardware, and contributions from companies. (3) Artificial intelligence aims to develop machines that can think and learn like humans, and this field has accelerated in recent years with improved data processing and hardware.
How to Use AI (Like ChatGPT & Bard) in your SEO & Content - A Comprehensive S...Volume Nine
**Free SEO Prompt Cheat Sheet** - www.V9digital.com/ai
Artificial Intelligence (A.I.) is revolutionizing the digital marketing landscape, and SEO is no exception. Cutting-edge tools like ChatGPT and Bard are changing the way businesses approach their online presence. This 3,000-character SlideShare presentation delves into how to effectively incorporate A.I. into your SEO workflows, ensuring your marketing campaigns remain innovative, efficient, and effective.
In this engaging and insightful presentation, we will explore:
A Brief History of A.I. in SEO:
Discover the origins and evolution of A.I. in the realm of digital marketing. Learn how it has transformed from a niche concept to a mainstream tool that has become indispensable for SEO professionals worldwide.
Pros and Cons of A.I. in SEO:
While A.I. has undoubtedly revolutionized SEO, it is essential to understand the potential advantages and drawbacks of using such technology. We will discuss the strengths and weaknesses of A.I. in SEO, including cost efficiency, scalability, accuracy, and the potential for bias.
Tactical Ideas and Applications for ChatGPT and Bard:
Explore practical and innovative ways to incorporate ChatGPT and Bard into your SEO workflows. From content generation and optimization to keyword research and analysis, we will provide you with actionable insights and best practices for leveraging these powerful A.I. tools to enhance your SEO strategies.
Speculation on the Future of A.I. in SEO:
As A.I. continues to evolve, it is crucial to stay ahead of the curve and anticipate the future developments in the industry. We will speculate on potential advancements and trends, exploring how A.I. might reshape the SEO landscape in the coming years.
Our Favorite A.I. Tools and Resources:
With so many A.I. tools and resources available, it can be challenging to know where to start. We will share our top recommendations for A.I. tools, platforms, and resources that can help you elevate your SEO game and stay ahead of the competition.
By the end of this SlideShare presentation, you will have gained valuable knowledge and practical tips for incorporating A.I. technology like ChatGPT and Bard into your SEO workflows. With a keen understanding of the potential benefits and challenges of using A.I. in SEO, you will be better equipped to navigate the rapidly evolving digital marketing landscape and drive your business to new heights of success.
This document provides an overview of Chat GPT, an AI tool launched in November 2022 by OpenAI. It discusses that Chat GPT allows for conversational dialogues and aims to give accurate answers while admitting mistakes. The document notes that Chat GPT was trained on huge amounts of online text data to generate human-like responses. Potential uses of Chat GPT discussed include powering virtual customer service agents, personal assistants, social media moderation, and improving machine translation.
The document discusses artificial intelligence, including its history, applications, and languages. It provides an overview of AI, noting that it aims to recreate human intelligence through machine learning and problem solving. The document then covers key topics like the philosophy of AI, limits on machine intelligence, and comparisons between human and artificial brains. It also gives brief histories of AI and machine learning. The document concludes by discussing popular AI programming languages like Lisp and Prolog, as well as various applications of AI technologies.
Fight for Yourself: How to Sell Your Ideas and Crush PresentationsDigital Surgeons
Don't let your blood, sweat, and pixels be overlooked, great creative doesn't sell itself.
Every presentation is a story, an opportunity to sell not just your work, but what people actually buy — YOU.
This presentation will walk viewers through three core aspects of winning at any presentation, Confidence, Comprehension, and Conviction.
These concepts, central to your work as a creative professional, are backed by science and bolstered by thoughts from some of the world’s leading creative professionals.
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.
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.
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.
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.
How to get things done - Lessons from Yahoo, Google, Netflix and Meta Ido Green
How can you make your software teams better?
What are the values and processes that you wish to embrace?
In these slides, we will share some stories from leading companies (e.g., Google, Meta, and Netflix), and we will see what is working for them.
This talk overviews my background as a female data scientist, introduces many types of generative AI, discusses potential use cases, highlights the need for representation in generative AI, and showcases a few tools that currently exist.
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
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.
AI and ML Series - Introduction to Generative AI and LLMs - Session 1DianaGray10
Session 1
👉This first session will cover an introduction to Generative AI & harnessing the power of large language models. The following topics will be discussed:
Introduction to Generative AI & harnessing the power of large language models.
What’s generative AI & what’s LLM.
How are we using it in our document understanding & communication mining models?
How to develop a trustworthy and unbiased AI model using LLM & GenAI.
Personal Intelligent Assistant
Speakers:
📌George Roth - AI Evangelist at UiPath
📌Sharon Palawandram - Senior Machine Learning Consultant @ Ashling Partners & UiPath MVP
📌Russel Alfeche - Technology Leader RPA @qBotica & UiPath MVP
Unlocking the Power of Generative AI An Executive's Guide.pdfPremNaraindas1
Generative AI is here, and it can revolutionize your business. With its powerful capabilities, this technology can help companies create more efficient processes, unlock new insights from data, and drive innovation. But how do you make the most of these opportunities?
This guide will provide you with the information and resources needed to understand the ins and outs of Generative AI, so you can make informed decisions and capitalize on the potential. It covers important topics such as strategies for leveraging large language models, optimizing MLOps processes, and best practices for building with Generative AI.
How AI is going to change the world _M.Mujeeb Riaz.pdfMujeeb Riaz
How AI is going to change the world?
"AI: The Future of Our World“
"AI and its Transformative Impact on the World: Understanding the Potential of Chatbots and Conversational AI"
What is Artificial Intelligence and how it works?
What are Chatbots?
What Is ChatGPT?
Difference between chatGPT 3 and chatGPT 4?
Is Jasper artificial intelligence?
What is Character AI and how it works?
How chatGPT is going to change the world?
Why we are calling ChatGPT the future?
The presentation is about the career path in the field of Data Science. Data Science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data.
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
Using AI chatbots for deep learning and teaching with specific examples to en...Nigel Daly
This talk was given to Senior high School teachers in Taiwan to help them better understand (1) what AI chatbot technology like ChatGPT and Bing Chat is, and (2) how to use it to enhance their own teaching and also their students' learning. Also discussed was how to make prompts and several examples. The examples specifically focused on language learning since the school will soon become a bilingual school. AI technology use was also described in terms of Bloom's taxonomy of learning objectives and connected to ideas of deep learning as advocated by the movement "New Pedagogies for Deep Learning", which the school has become a part of.
Presented at Social Media Breakfast Red Deer. Website and social analytics give you lots of data to look at, but what should you do with it? Learn how to make analytics work for you and understand how your various marketing and communication efforts are having an impact.
(1) The document discusses several computer science topics including data science, artificial intelligence, and cloud computing. (2) It notes that data science has grown in popularity from 2012-2017 due to an ability to better process large volumes of data using statistics, specialized hardware, and contributions from companies. (3) Artificial intelligence aims to develop machines that can think and learn like humans, and this field has accelerated in recent years with improved data processing and hardware.
How to Use AI (Like ChatGPT & Bard) in your SEO & Content - A Comprehensive S...Volume Nine
**Free SEO Prompt Cheat Sheet** - www.V9digital.com/ai
Artificial Intelligence (A.I.) is revolutionizing the digital marketing landscape, and SEO is no exception. Cutting-edge tools like ChatGPT and Bard are changing the way businesses approach their online presence. This 3,000-character SlideShare presentation delves into how to effectively incorporate A.I. into your SEO workflows, ensuring your marketing campaigns remain innovative, efficient, and effective.
In this engaging and insightful presentation, we will explore:
A Brief History of A.I. in SEO:
Discover the origins and evolution of A.I. in the realm of digital marketing. Learn how it has transformed from a niche concept to a mainstream tool that has become indispensable for SEO professionals worldwide.
Pros and Cons of A.I. in SEO:
While A.I. has undoubtedly revolutionized SEO, it is essential to understand the potential advantages and drawbacks of using such technology. We will discuss the strengths and weaknesses of A.I. in SEO, including cost efficiency, scalability, accuracy, and the potential for bias.
Tactical Ideas and Applications for ChatGPT and Bard:
Explore practical and innovative ways to incorporate ChatGPT and Bard into your SEO workflows. From content generation and optimization to keyword research and analysis, we will provide you with actionable insights and best practices for leveraging these powerful A.I. tools to enhance your SEO strategies.
Speculation on the Future of A.I. in SEO:
As A.I. continues to evolve, it is crucial to stay ahead of the curve and anticipate the future developments in the industry. We will speculate on potential advancements and trends, exploring how A.I. might reshape the SEO landscape in the coming years.
Our Favorite A.I. Tools and Resources:
With so many A.I. tools and resources available, it can be challenging to know where to start. We will share our top recommendations for A.I. tools, platforms, and resources that can help you elevate your SEO game and stay ahead of the competition.
By the end of this SlideShare presentation, you will have gained valuable knowledge and practical tips for incorporating A.I. technology like ChatGPT and Bard into your SEO workflows. With a keen understanding of the potential benefits and challenges of using A.I. in SEO, you will be better equipped to navigate the rapidly evolving digital marketing landscape and drive your business to new heights of success.
This document provides an overview of Chat GPT, an AI tool launched in November 2022 by OpenAI. It discusses that Chat GPT allows for conversational dialogues and aims to give accurate answers while admitting mistakes. The document notes that Chat GPT was trained on huge amounts of online text data to generate human-like responses. Potential uses of Chat GPT discussed include powering virtual customer service agents, personal assistants, social media moderation, and improving machine translation.
The document discusses artificial intelligence, including its history, applications, and languages. It provides an overview of AI, noting that it aims to recreate human intelligence through machine learning and problem solving. The document then covers key topics like the philosophy of AI, limits on machine intelligence, and comparisons between human and artificial brains. It also gives brief histories of AI and machine learning. The document concludes by discussing popular AI programming languages like Lisp and Prolog, as well as various applications of AI technologies.
Fight for Yourself: How to Sell Your Ideas and Crush PresentationsDigital Surgeons
Don't let your blood, sweat, and pixels be overlooked, great creative doesn't sell itself.
Every presentation is a story, an opportunity to sell not just your work, but what people actually buy — YOU.
This presentation will walk viewers through three core aspects of winning at any presentation, Confidence, Comprehension, and Conviction.
These concepts, central to your work as a creative professional, are backed by science and bolstered by thoughts from some of the world’s leading creative professionals.
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.
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.
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.
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.
How to get things done - Lessons from Yahoo, Google, Netflix and Meta Ido Green
How can you make your software teams better?
What are the values and processes that you wish to embrace?
In these slides, we will share some stories from leading companies (e.g., Google, Meta, and Netflix), and we will see what is working for them.
This talk overviews my background as a female data scientist, introduces many types of generative AI, discusses potential use cases, highlights the need for representation in generative AI, and showcases a few tools that currently exist.
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.
This document discusses machine learning and artificial intelligence. It begins by defining AI and machine learning, noting that ML allows systems to learn tasks without being explicitly programmed. Machine learning is a subset of AI that uses data to learn, allowing systems to recognize patterns and make predictions. Three main types of machine learning are discussed: supervised learning, unsupervised learning, and reinforcement learning. Examples of applications are given for areas like banking, healthcare, and retail. Sources of errors in machine learning models are also explained, including bias, variance, and the bias-variance tradeoff. Overall, the document provides a high-level overview of key concepts in machine learning and AI.
Machine learning algorithms can learn from data to make predictions without being explicitly programmed. They are used in applications like medical diagnosis, financial trading, and product recommendations. There are two main types - supervised learning uses labeled input/output data to build predictive models, while unsupervised learning finds hidden patterns in unlabeled data. Examples show how machine learning optimizes HVAC systems, detects car crashes, and analyzes artistic styles.
Applied Artificial Intelligence Unit 3 Semester 3 MSc IT Part 2 Mumbai Univer...Madhav Mishra
The document discusses machine learning paradigms including supervised learning, unsupervised learning, clustering, artificial neural networks, and more. It then discusses how supervised machine learning works using labeled training data for tasks like classification and regression. Unsupervised learning is described as using unlabeled data to find patterns and group data. Semi-supervised learning uses some labeled and some unlabeled data. Reinforcement learning provides rewards or punishments to achieve goals. Inductive learning infers functions from examples to make predictions for new examples.
This document summarizes a 15-day practical training undertaken by Kirti Sharma from August 11-25, 2022 at Udemy on the topic of "Data Science and Machine Learning with Python Bootcamp". The training was undertaken to fulfill partial requirements for a Bachelor of Technology degree in Computer Science Engineering. The training covered topics such as Python programming, machine learning libraries and algorithms, and their applications.
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.
How to build machine learning apps.pdfJamieDornan2
The document provides an overview of machine learning, including key concepts like supervised vs unsupervised learning, common algorithms like decision trees and neural networks, and how machine learning is used to build applications. It discusses how machine learning models are trained on large datasets to identify patterns and make predictions. Examples of machine learning in apps include predictive text, speech recognition, and personalized recommendations based on user behavior data. The document also outlines the steps involved in building a machine learning application.
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.
The document provides an overview of machine learning, including key concepts such as data, models, algorithms, and different machine learning methods. It discusses how machine learning uses large amounts of data to develop models that can make predictions without being explicitly programmed. The document also outlines several common machine learning algorithms like decision trees, k-nearest neighbors, support vector machines, neural networks, and reinforcement learning. Overall, the summary provides a high-level introduction to fundamental machine learning concepts and techniques.
Supervised Machine Learning Techniques common algorithms and its applicationTara ram Goyal
The document provides an introduction to supervised machine learning, including definitions, techniques, and applications. It discusses how supervised machine learning involves training algorithms using labeled input data to make predictions on unlabeled data. Some common supervised learning algorithms mentioned are naive Bayes, decision trees, linear regression, support vector machines, and neural networks. Applications discussed include self-driving cars, online recommendations, fraud detection, and spam filtering. The key difference between supervised and unsupervised learning is that supervised learning uses labeled training data while unsupervised learning does not have pre-existing labels.
06-18-2024-Princeton Meetup-Introduction to MilvusTimothy Spann
06-18-2024-Princeton Meetup-Introduction to Milvus
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://github.com/milvus-io/milvus
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/142-17June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
Expand LLMs' knowledge by incorporating external data sources into LLMs and your AI applications.
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
06-20-2024-AI Camp Meetup-Unstructured Data and Vector DatabasesTimothy Spann
Tech Talk: Unstructured Data and Vector Databases
Speaker: Tim Spann (Zilliz)
Abstract: In this session, I will discuss the unstructured data and the world of vector databases, we will see how they different from traditional databases. In which cases you need one and in which you probably don’t. I will also go over Similarity Search, where do you get vectors from and an example of a Vector Database Architecture. Wrapping up with an overview of Milvus.
Introduction
Unstructured data, vector databases, traditional databases, similarity search
Vectors
Where, What, How, Why Vectors? We’ll cover a Vector Database Architecture
Introducing Milvus
What drives Milvus' Emergence as the most widely adopted vector database
Hi Unstructured Data Friends!
I hope this video had all the unstructured data processing, AI and Vector Database demo you needed for now. If not, there’s a ton more linked below.
My source code is available here
https://github.com/tspannhw/
Let me know in the comments if you liked what you saw, how I can improve and what should I show next? Thanks, hope to see you soon at a Meetup in Princeton, Philadelphia, New York City or here in the Youtube Matrix.
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/141-10June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
https://www.meetup.com/unstructured-data-meetup-new-york/events/301383476/?slug=unstructured-data-meetup-new-york&eventId=301383476
https://www.aicamp.ai/event/eventdetails/W2024062014
PyData London 2024: Mistakes were made (Dr. Rebecca Bilbro)Rebecca Bilbro
To honor ten years of PyData London, join Dr. Rebecca Bilbro as she takes us back in time to reflect on a little over ten years working as a data scientist. One of the many renegade PhDs who joined the fledgling field of data science of the 2010's, Rebecca will share lessons learned the hard way, often from watching data science projects go sideways and learning to fix broken things. Through the lens of these canon events, she'll identify some of the anti-patterns and red flags she's learned to steer around.
Did you know that drowning is a leading cause of unintentional death among young children? According to recent data, children aged 1-4 years are at the highest risk. Let's raise awareness and take steps to prevent these tragic incidents. Supervision, barriers around pools, and learning CPR can make a difference. Stay safe this summer!
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