Choose our Artificial Intelligence Machine Learning Deep Learning PPT PowerPoint Presentation Slide Templates to understand this popular branch of computer science. Acquaint your audience with the process of building smart, capable machines that can perform intelligent tasks with the help of this neural network PPT presentation. Exhibit the difference between AI, machine learning, and deep learning through this informative robotics PPT design. Elaborate on the wide range of areas that can benefit from artificial intelligence like supply chain, customer experience, human resources, fraud detection, research, and development by taking the aid of this computer science PPT slideshow. Highlight the booming rate of AI business and its future revenue forecast by downloading this thought-provoking and indulging information technology PowerPoint graphics. Save your time and efforts with these pre-ready and professionally crafted content-specific slides. It will educate your audience about this complex process in an easy yet efficient way. Download this AI functioning PowerPoint deck to create a roadmap for the growth and expansion of your business. https://bit.ly/3x135nD
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck is loaded with easy-to-follow content, and intuitive design. Introduce the types and levels of artificial intelligence using the highly-effective visuals featured in this PPT slide deck. Showcase the AI-subfield of machine learning, as well as deep learning through our comprehensive PowerPoint theme. Represent the differences, and interrelationship between AI, ML, and DL. Elaborate on the scope and use case of machine intelligence in healthcare, HR, banking, supply chain, or any other industry. Take advantage of the infographic-style layout to describe why AI is flourishing in today’s day and age. Elucidate AI trends such as robotic process automation, advanced cybersecurity, AI-powered chatbots, and more. Cover all the essentials of machine learning and deep learning with the help of this PPT slideshow. Outline the application, algorithms, use cases, significance, and selection criteria for machine learning. Highlight the deep learning process, types, limitations, and significance. Describe reinforcement training, neural network classifications, and a lot more. Hit download and begin personalization. Our AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro. https://bit.ly/3ngJCKf
Artificial Intelligence And Machine Learning PowerPoint Presentation Slides C...SlideTeam
Artificial Intelligence And Machine Learning PowerPoint Presentation Slides arrange insightful data using industry-best design practices. Highlight the differences between machine intelligence, machine learning, and deep learning through our PPT format. Utilize this PowerPoint slideshow to present advantages, disadvantages, learning techniques, and types of supervised machine learning. Further, cover the merits, demerits, and types of unsupervised machine learning. Communicate important details concerning reinforcement learning. Familiarize your viewers with the expert system in artificial intelligence. Outline examples, characteristics, constituents, uses, advantages, drawbacks, and other aspects of the expert system. Compile the deep learning process, recurrent neural networks, and convolutional neural networks through this PowerPoint theme. Present an impactful introduction to artificial intelligence. Introduce kinds, algorithms, trends, and use cases of artificial intelligence. This presentation is not only easy-to-follow but also very convenient to edit, even if you have no prior design experience. Smash the download button and start instant personalization. Our Artificial Intelligence And Machine Learning PowerPoint Presentation Slides Complete Deck are explicit and effective. They combine clarity and concise expression. https://bit.ly/3hKg7PV
Artificial Intelligence Machine Learning Deep Learning PPT PowerPoint Present...SlideTeam
This PPT is for the mid level managers giving information about AI Artificial Intelligence, Machine Learning ML, Deep Learning DL, Supervised Machine Learning, Unsupervised Machine Learning, Reinforcement Learning. You can also learn the difference between Artificial Intelligence and Machine Learning and deciding which out of AI or DL or ML will be better for your business. You will also get to know about the Expert System, its examples, characteristics, components, etc. https://bit.ly/2ApMbXB
Machine learning and artificial intelligence are explained. Machine learning uses algorithms and past data to allow computers to optimize performance and develop behaviors without being explicitly programmed. It is a branch of artificial intelligence that uses supervised and unsupervised algorithms to apply past information to new data or draw conclusions from datasets. Case studies show how machine learning reveals influences and predicts user preferences. Artificial intelligence aims to simulate human intelligence through computer science, psychology, and other fields. Industries like healthcare and finance will benefit from machine learning and artificial intelligence applications like disease prediction and financial recommendations.
This document presents an overview of machine learning. It defines machine learning as a field that allows computers to learn without being explicitly programmed, and discusses how machine learning enables computers to automatically analyze large datasets to make predictions. The document then summarizes different types of machine learning techniques including supervised learning, unsupervised learning, reinforcement learning, and more. It provides examples of applications of machine learning like face recognition, speech recognition, and self-driving cars. In conclusion, it states that machine learning is already used across many industries and can improve lives in numerous ways.
This document summarizes a seminar presentation on machine learning. It defines machine learning as applications of artificial intelligence that allow computers to learn automatically from data without being explicitly programmed. It discusses three main algorithms of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning uses labelled training data, unsupervised learning finds patterns in unlabelled data, and reinforcement learning involves learning through rewards and punishments. Examples applications discussed include data mining, natural language processing, image recognition, and expert systems.
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.
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck is loaded with easy-to-follow content, and intuitive design. Introduce the types and levels of artificial intelligence using the highly-effective visuals featured in this PPT slide deck. Showcase the AI-subfield of machine learning, as well as deep learning through our comprehensive PowerPoint theme. Represent the differences, and interrelationship between AI, ML, and DL. Elaborate on the scope and use case of machine intelligence in healthcare, HR, banking, supply chain, or any other industry. Take advantage of the infographic-style layout to describe why AI is flourishing in today’s day and age. Elucidate AI trends such as robotic process automation, advanced cybersecurity, AI-powered chatbots, and more. Cover all the essentials of machine learning and deep learning with the help of this PPT slideshow. Outline the application, algorithms, use cases, significance, and selection criteria for machine learning. Highlight the deep learning process, types, limitations, and significance. Describe reinforcement training, neural network classifications, and a lot more. Hit download and begin personalization. Our AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro. https://bit.ly/3ngJCKf
Artificial Intelligence And Machine Learning PowerPoint Presentation Slides C...SlideTeam
Artificial Intelligence And Machine Learning PowerPoint Presentation Slides arrange insightful data using industry-best design practices. Highlight the differences between machine intelligence, machine learning, and deep learning through our PPT format. Utilize this PowerPoint slideshow to present advantages, disadvantages, learning techniques, and types of supervised machine learning. Further, cover the merits, demerits, and types of unsupervised machine learning. Communicate important details concerning reinforcement learning. Familiarize your viewers with the expert system in artificial intelligence. Outline examples, characteristics, constituents, uses, advantages, drawbacks, and other aspects of the expert system. Compile the deep learning process, recurrent neural networks, and convolutional neural networks through this PowerPoint theme. Present an impactful introduction to artificial intelligence. Introduce kinds, algorithms, trends, and use cases of artificial intelligence. This presentation is not only easy-to-follow but also very convenient to edit, even if you have no prior design experience. Smash the download button and start instant personalization. Our Artificial Intelligence And Machine Learning PowerPoint Presentation Slides Complete Deck are explicit and effective. They combine clarity and concise expression. https://bit.ly/3hKg7PV
Artificial Intelligence Machine Learning Deep Learning PPT PowerPoint Present...SlideTeam
This PPT is for the mid level managers giving information about AI Artificial Intelligence, Machine Learning ML, Deep Learning DL, Supervised Machine Learning, Unsupervised Machine Learning, Reinforcement Learning. You can also learn the difference between Artificial Intelligence and Machine Learning and deciding which out of AI or DL or ML will be better for your business. You will also get to know about the Expert System, its examples, characteristics, components, etc. https://bit.ly/2ApMbXB
Machine learning and artificial intelligence are explained. Machine learning uses algorithms and past data to allow computers to optimize performance and develop behaviors without being explicitly programmed. It is a branch of artificial intelligence that uses supervised and unsupervised algorithms to apply past information to new data or draw conclusions from datasets. Case studies show how machine learning reveals influences and predicts user preferences. Artificial intelligence aims to simulate human intelligence through computer science, psychology, and other fields. Industries like healthcare and finance will benefit from machine learning and artificial intelligence applications like disease prediction and financial recommendations.
This document presents an overview of machine learning. It defines machine learning as a field that allows computers to learn without being explicitly programmed, and discusses how machine learning enables computers to automatically analyze large datasets to make predictions. The document then summarizes different types of machine learning techniques including supervised learning, unsupervised learning, reinforcement learning, and more. It provides examples of applications of machine learning like face recognition, speech recognition, and self-driving cars. In conclusion, it states that machine learning is already used across many industries and can improve lives in numerous ways.
This document summarizes a seminar presentation on machine learning. It defines machine learning as applications of artificial intelligence that allow computers to learn automatically from data without being explicitly programmed. It discusses three main algorithms of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning uses labelled training data, unsupervised learning finds patterns in unlabelled data, and reinforcement learning involves learning through rewards and punishments. Examples applications discussed include data mining, natural language processing, image recognition, and expert systems.
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.
The slide helps to get an insight on the concepts of Artificial Intelligence.
The topics covered are as follows,
* Concept of AI
* Meaning of AI
* History of AI
* Levels of AI
* Types of AI
* Applications of AI - Agriculture, Health, Business (Emerging market), Education
* AI Tools and Platforms
Machine learning is a method of data analysis that uses algorithms to iteratively learn from data without being explicitly programmed. It allows computers to find hidden insights in data and become better at tasks via experience. Machine learning has many practical applications and is important due to growing data availability, cheaper and more powerful computation, and affordable storage. It is used in fields like finance, healthcare, marketing and transportation. The main approaches are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Each has real-world examples like loan prediction, market basket analysis, webpage classification, and marketing campaign optimization.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
Machine learning helps predict behavior and recognize patterns that humans cannot by learning from data without relying on programmed rules. It is an algorithmic approach that differs from statistical modeling which formalizes relationships through mathematical equations. Machine learning is a part of the broader field of artificial intelligence which aims to develop systems that can act and respond intelligently like humans. The machine learning workflow involves collecting and preprocessing data, selecting algorithms, training models, and evaluating performance. Common machine learning algorithms include supervised learning, unsupervised learning, reinforcement learning, and deep learning. Popular tools for machine learning include Python, R, TensorFlow, and Spark.
The document introduces artificial intelligence, machine learning, and deep learning. It discusses supervised, unsupervised, and reinforced learning techniques. Examples of applications discussed include image recognition, natural language processing, and virtual assistants. The document also notes that some AI systems have developed their own internal languages when interacting without human supervision.
Branch of computer science that develops machines and software with human-like intelligence
top 5 artificial intelligence stocks
artificial intelligence technology
artificial intelligence articles
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artificial intelligence robots
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artificial intelligence wikipedia
Artificial intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. There are four main schools of thought in AI: thinking humanly, thinking rationally, acting humanly, and acting rationally. Popular techniques used in AI include machine learning, deep learning, and natural language processing. The document then discusses the growth of AI and its applications in various domains like healthcare, law, education, and more. It also lists the top companies leading the development of AI like DeepMind, Google, Facebook, Microsoft, and others. Finally, it provides perspectives on the future impact and adoption of AI.
Machine learning involves programming computers to optimize performance using example data or past experience. It is used when human expertise does not exist, humans cannot explain their expertise, solutions change over time, or solutions need to be adapted to particular cases. Learning builds general models from data to approximate real-world examples. There are several types of machine learning including supervised learning (classification, regression), unsupervised learning (clustering), and reinforcement learning. Machine learning has applications in many domains including retail, finance, manufacturing, medicine, web mining, and more.
This document provides an overview of machine learning. It begins with an introduction and definitions, explaining that machine learning allows computers to learn without being explicitly programmed by exploring algorithms that can learn from data. The document then discusses the different types of machine learning problems including supervised learning, unsupervised learning, and reinforcement learning. It provides examples and applications of each type. The document also covers popular machine learning techniques like decision trees, artificial neural networks, and frameworks/tools used for machine learning.
The document describes a machine learning certification training offered by Edureka. It covers topics like introduction to data science, machine learning applications, types of machine learning including supervised, unsupervised and reinforcement learning. For supervised learning, it discusses algorithms like linear regression, logistic regression, decision trees, random forest and Naive Bayes classifier. It also explains machine learning life cycle and concepts like model fitting, clustering and applications of machine learning.
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.
The document provides an introduction to data analytics, including defining key terms like data, information, and analytics. It outlines the learning outcomes which are the basic definition of data analytics concepts, different variable types, types of analytics, and the analytics life cycle. The analytics life cycle is described in detail and involves problem identification, hypothesis formulation, data collection, data exploration, model building, and model validation/evaluation. Different variable types like numerical, categorical, and ordinal variables are also defined.
This document provides an overview of artificial intelligence (AI). It defines AI as making computers do things that require human intelligence. AI works using artificial neurons that mimic biological neurons. Neural networks are composed of interconnected artificial neurons. The document also discusses expert systems, machine learning, comparisons between human and artificial intelligence, and applications of AI in areas like medicine, archaeology, and geology.
Machine learning and its applications was a gentle introduction to machine learning presented by Dr. Ganesh Neelakanta Iyer. The presentation covered an introduction to machine learning, different types of machine learning problems including classification, regression, and clustering. It also provided examples of applications of machine learning at companies like Facebook, Google, and McDonald's. The presentation concluded with discussing the general machine learning framework and steps involved in working with machine learning problems.
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
This Machine Learning Algorithms presentation will help you learn you what machine learning is, and the various ways in which you can use machine learning to solve a problem. At the end, you will see a demo on linear regression, logistic regression, decision tree and random forest. This Machine Learning Algorithms presentation is designed for beginners to make them understand how to implement the different Machine Learning Algorithms.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. Real world applications of Machine Learning
2. What is Machine Learning?
3. Processes involved in Machine Learning
4. Type of Machine Learning Algorithms
5. Popular Algorithms with a hands-on demo
- Linear regression
- Logistic regression
- Decision tree and Random forest
- N Nearest neighbor
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Data Science is a wonderful technology that has applications in almost every field. Let's learn the basics of this domain on 16th March at (time).
Agenda
1. What is Data Science? How is it different from ML, DL, and AI
2. Why is this skill in demand?
3. What are some popular applications of Data Science
4. Popular tools and frameworks used in Data Science
Principles of Artificial Intelligence & Machine LearningJerry Lu
Artificial intelligence has captivated me since I worked on projects at Google that ranged from detecting fraud on Google Cloud to predicting subscriber retention on YouTube Red. Looking to broaden my professional experience, I then entered the world of venture capital by joining Baidu Ventures as its first summer investment associate where I got to work with amazingly talented founders building AI-focused startups.
Now at the Wharton School at the University of Pennsylvania, I am looking for opportunities to meet people with interesting AI-related ideas and learn about the newest innovations within the AI ecosystem. Within the first two months of business school, I connected with Nicholas Lind, a second-year Wharton MBA student who interned at IBM Watson as a data scientist. Immediately recognizing our common passion for AI, we produced a lunch-and-learn about AI and machine learning (ML) for our fellow classmates.
Using the following deck, we sought to:
- define artificial intelligence and describe its applications in business
- decode buzzwords such as “deep learning” and “cognitive computing”
- highlight analytical techniques and best practices used in AI / ML
- ultimately, educate future AI leaders
The lunch-and-learn was well received. When it became apparent that it was the topic at hand and not so much the free pizzas that attracted the overflowing audience, I was amazed at the level of interest. It was reassuring to hear that classmates were interested in learning more about the technology and its practical applications in solving everyday business challenges. Nick and I are now laying a foundation to make these workshops an ongoing effort so that more people across the various schools of engineering, design, and Penn at large can benefit.
With its focus on quantitative rigor, Wharton already feels like a perfect fit for me. In the next two years, I look forward to engaging with like-minded people, both in and out of the classroom, sharing my knowledge about AI with my peers, and learning from them in turn. By working together to expand Penn’s reach and reputation with respect to this new frontier, I’m confident that we can all grow into next-generation leaders who help drive companies forward in an era of artificial intelligence.
I’d love to hear what you think. If you found this post or the deck useful, please recommend them to your friends and colleagues!
ppt on machine learning to deep learning (1).pptxAnweshaGarima
The document provides an overview of machine learning, deep learning, and artificial intelligence. It begins with definitions of AI, machine learning, and deep learning. It then covers key topics like the levels of AI, types of AI, where AI is used, and why AI is booming. Sections are dedicated to machine learning, deep learning, the differences between AI, ML, and DL, and various machine learning and deep learning algorithms and applications.
Artificial intelligence (AI) is a branch of computer science focused on building intelligent machines. The document discusses various topics related to AI including different types (narrow, general, super artificial intelligence), subfields (machine learning, deep learning), common uses (healthcare, banking, supply chain), and trends (robotic process automation, AI in retail). Rapid advances in deep learning and machine learning are transforming industries as AI is applied to tasks like predictive analytics, computer vision, and natural language processing.
The slide helps to get an insight on the concepts of Artificial Intelligence.
The topics covered are as follows,
* Concept of AI
* Meaning of AI
* History of AI
* Levels of AI
* Types of AI
* Applications of AI - Agriculture, Health, Business (Emerging market), Education
* AI Tools and Platforms
Machine learning is a method of data analysis that uses algorithms to iteratively learn from data without being explicitly programmed. It allows computers to find hidden insights in data and become better at tasks via experience. Machine learning has many practical applications and is important due to growing data availability, cheaper and more powerful computation, and affordable storage. It is used in fields like finance, healthcare, marketing and transportation. The main approaches are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Each has real-world examples like loan prediction, market basket analysis, webpage classification, and marketing campaign optimization.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
Machine learning helps predict behavior and recognize patterns that humans cannot by learning from data without relying on programmed rules. It is an algorithmic approach that differs from statistical modeling which formalizes relationships through mathematical equations. Machine learning is a part of the broader field of artificial intelligence which aims to develop systems that can act and respond intelligently like humans. The machine learning workflow involves collecting and preprocessing data, selecting algorithms, training models, and evaluating performance. Common machine learning algorithms include supervised learning, unsupervised learning, reinforcement learning, and deep learning. Popular tools for machine learning include Python, R, TensorFlow, and Spark.
The document introduces artificial intelligence, machine learning, and deep learning. It discusses supervised, unsupervised, and reinforced learning techniques. Examples of applications discussed include image recognition, natural language processing, and virtual assistants. The document also notes that some AI systems have developed their own internal languages when interacting without human supervision.
Branch of computer science that develops machines and software with human-like intelligence
top 5 artificial intelligence stocks
artificial intelligence technology
artificial intelligence articles
artificial intelligence companies
artificial intelligence stocks to buy
artificial intelligence robots
artificial intelligence in medicine
artificial intelligence wikipedia
Artificial intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. There are four main schools of thought in AI: thinking humanly, thinking rationally, acting humanly, and acting rationally. Popular techniques used in AI include machine learning, deep learning, and natural language processing. The document then discusses the growth of AI and its applications in various domains like healthcare, law, education, and more. It also lists the top companies leading the development of AI like DeepMind, Google, Facebook, Microsoft, and others. Finally, it provides perspectives on the future impact and adoption of AI.
Machine learning involves programming computers to optimize performance using example data or past experience. It is used when human expertise does not exist, humans cannot explain their expertise, solutions change over time, or solutions need to be adapted to particular cases. Learning builds general models from data to approximate real-world examples. There are several types of machine learning including supervised learning (classification, regression), unsupervised learning (clustering), and reinforcement learning. Machine learning has applications in many domains including retail, finance, manufacturing, medicine, web mining, and more.
This document provides an overview of machine learning. It begins with an introduction and definitions, explaining that machine learning allows computers to learn without being explicitly programmed by exploring algorithms that can learn from data. The document then discusses the different types of machine learning problems including supervised learning, unsupervised learning, and reinforcement learning. It provides examples and applications of each type. The document also covers popular machine learning techniques like decision trees, artificial neural networks, and frameworks/tools used for machine learning.
The document describes a machine learning certification training offered by Edureka. It covers topics like introduction to data science, machine learning applications, types of machine learning including supervised, unsupervised and reinforcement learning. For supervised learning, it discusses algorithms like linear regression, logistic regression, decision trees, random forest and Naive Bayes classifier. It also explains machine learning life cycle and concepts like model fitting, clustering and applications of machine learning.
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.
The document provides an introduction to data analytics, including defining key terms like data, information, and analytics. It outlines the learning outcomes which are the basic definition of data analytics concepts, different variable types, types of analytics, and the analytics life cycle. The analytics life cycle is described in detail and involves problem identification, hypothesis formulation, data collection, data exploration, model building, and model validation/evaluation. Different variable types like numerical, categorical, and ordinal variables are also defined.
This document provides an overview of artificial intelligence (AI). It defines AI as making computers do things that require human intelligence. AI works using artificial neurons that mimic biological neurons. Neural networks are composed of interconnected artificial neurons. The document also discusses expert systems, machine learning, comparisons between human and artificial intelligence, and applications of AI in areas like medicine, archaeology, and geology.
Machine learning and its applications was a gentle introduction to machine learning presented by Dr. Ganesh Neelakanta Iyer. The presentation covered an introduction to machine learning, different types of machine learning problems including classification, regression, and clustering. It also provided examples of applications of machine learning at companies like Facebook, Google, and McDonald's. The presentation concluded with discussing the general machine learning framework and steps involved in working with machine learning problems.
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
This Machine Learning Algorithms presentation will help you learn you what machine learning is, and the various ways in which you can use machine learning to solve a problem. At the end, you will see a demo on linear regression, logistic regression, decision tree and random forest. This Machine Learning Algorithms presentation is designed for beginners to make them understand how to implement the different Machine Learning Algorithms.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. Real world applications of Machine Learning
2. What is Machine Learning?
3. Processes involved in Machine Learning
4. Type of Machine Learning Algorithms
5. Popular Algorithms with a hands-on demo
- Linear regression
- Logistic regression
- Decision tree and Random forest
- N Nearest neighbor
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Data Science is a wonderful technology that has applications in almost every field. Let's learn the basics of this domain on 16th March at (time).
Agenda
1. What is Data Science? How is it different from ML, DL, and AI
2. Why is this skill in demand?
3. What are some popular applications of Data Science
4. Popular tools and frameworks used in Data Science
Principles of Artificial Intelligence & Machine LearningJerry Lu
Artificial intelligence has captivated me since I worked on projects at Google that ranged from detecting fraud on Google Cloud to predicting subscriber retention on YouTube Red. Looking to broaden my professional experience, I then entered the world of venture capital by joining Baidu Ventures as its first summer investment associate where I got to work with amazingly talented founders building AI-focused startups.
Now at the Wharton School at the University of Pennsylvania, I am looking for opportunities to meet people with interesting AI-related ideas and learn about the newest innovations within the AI ecosystem. Within the first two months of business school, I connected with Nicholas Lind, a second-year Wharton MBA student who interned at IBM Watson as a data scientist. Immediately recognizing our common passion for AI, we produced a lunch-and-learn about AI and machine learning (ML) for our fellow classmates.
Using the following deck, we sought to:
- define artificial intelligence and describe its applications in business
- decode buzzwords such as “deep learning” and “cognitive computing”
- highlight analytical techniques and best practices used in AI / ML
- ultimately, educate future AI leaders
The lunch-and-learn was well received. When it became apparent that it was the topic at hand and not so much the free pizzas that attracted the overflowing audience, I was amazed at the level of interest. It was reassuring to hear that classmates were interested in learning more about the technology and its practical applications in solving everyday business challenges. Nick and I are now laying a foundation to make these workshops an ongoing effort so that more people across the various schools of engineering, design, and Penn at large can benefit.
With its focus on quantitative rigor, Wharton already feels like a perfect fit for me. In the next two years, I look forward to engaging with like-minded people, both in and out of the classroom, sharing my knowledge about AI with my peers, and learning from them in turn. By working together to expand Penn’s reach and reputation with respect to this new frontier, I’m confident that we can all grow into next-generation leaders who help drive companies forward in an era of artificial intelligence.
I’d love to hear what you think. If you found this post or the deck useful, please recommend them to your friends and colleagues!
ppt on machine learning to deep learning (1).pptxAnweshaGarima
The document provides an overview of machine learning, deep learning, and artificial intelligence. It begins with definitions of AI, machine learning, and deep learning. It then covers key topics like the levels of AI, types of AI, where AI is used, and why AI is booming. Sections are dedicated to machine learning, deep learning, the differences between AI, ML, and DL, and various machine learning and deep learning algorithms and applications.
Artificial intelligence (AI) is a branch of computer science focused on building intelligent machines. The document discusses various topics related to AI including different types (narrow, general, super artificial intelligence), subfields (machine learning, deep learning), common uses (healthcare, banking, supply chain), and trends (robotic process automation, AI in retail). Rapid advances in deep learning and machine learning are transforming industries as AI is applied to tasks like predictive analytics, computer vision, and natural language processing.
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/2V0reNa
Reinforcement Learning In AI Powerpoint Presentation Slide Templates Complete...SlideTeam
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What is Artificial Intelligence? by Simplilearn is an introduction to artificial intelligence that deals with the fundamentals of artificial intelligence and its critical technicalities. Artificial Intelligence In five Minutes tutorial video will help you enhance your knowledge of Artificial Intelligence. This video on AI Explained by Simplilearn covers the most Critical AI Concepts in the simplest ways.
00:00:00 Introduction to What is AI?
This video segment on what is AI? will cover the briefing on Artificial Intelligence.
00:00:30 What is AI?
This section of the What is AI? video will give the technical definition of AI.
00:00:57 Types of AI
This section of the What is AI? video will cover the types of AI available in the industry.
00:01:46 Machine learning and Deep learning
This section of the What is AI? video will cover the aspects of machine learning and deep learning in detail.
00:02:30 Deep Learning and NLP
This section of the What is AI? video will cover the aspects of Deep Learning and NLP in detail.
00:02:45 AI in different fields
This section of the What is AI? video will cover the various fields where AI is currently operational.
00:03:30 Salary of AI engineer
This section of the What is AI? video will cover the salary details of AI engineers.
00:03:48 Quiz on What is AI?
This section of the What is AI? video will have a quick and simple quiz question on AI.
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Learn Where Artificial Intelligence Is Used NowadaysRobert Smith
The document discusses the rise of artificial intelligence and its growing applications in everyday life. It describes how AI is being used in domains like self-driving cars, healthcare for disease prediction, chatbots, supply chain management, and more. The document also provides examples of AI in systems like Alexa, Facebook feeds, Netflix recommendations, and Snapchat filters. It notes that while AI technology has been around since the 1940s, it is only recently that it has become widely used and a popular career option, with many companies now offering AI certification courses to produce experts.
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The document provides an overview of an artificial intelligence course taught at the Nadimpalli Satyanarayana Raju Institute of Technology. The course instructor is D Hema Sri Chowdary and covers topics such as the definition of AI, its importance and applications, the differences between AI, machine learning and deep learning, and the pros and cons of AI. A brief history of AI development is also presented, outlining major advancements from 1956 to the present.
Presenting this set of slides with name - Artificial Intelligence Overview Powerpoint Presentation Slides. This complete deck is oriented to make sure you do not lag in your presentations. Our creatively crafted slides come with apt research and planning. This exclusive deck with thirtyseven slides is here to help you to strategize, plan, analyse, or segment the topic with clear understanding and apprehension. Utilize ready to use presentation slides on Artificial Intelligence Overview Powerpoint Presentation Slides with all sorts of editable templates, charts and graphs, overviews, analysis templates. It is usable for marking important decisions and covering critical issues. Display and present all possible kinds of underlying nuances, progress factors for an all inclusive presentation for the teams. This presentation deck can be used by all professionals, managers, individuals, internal external teams involved in any company organization.
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Artificial intelligence (AI) is a branch of computer science concerned with building intelligent machines that can perform tasks requiring human intelligence. AI is advancing rapidly through machine learning and deep learning techniques. Developers use AI to automate tasks and solve problems. AI systems can learn with or without human supervision. While strong AI that matches human intelligence does not yet exist, weak AI is used for applications like smart assistants, self-driving cars, and spam filters. The future of AI is uncertain but it has potential to transform many industries through automation and improved decision making. Challenges include the costs of development and potential job disruption.
Artificial intelligence (AI) is a branch of computer science concerned with building intelligent machines that can perform tasks requiring human intelligence. AI is advancing rapidly through machine learning and deep learning techniques. Developers use AI to automate tasks and solve problems. AI systems can learn with or without human supervision. While strong AI that matches human intelligence does not yet exist, weak AI is used for applications like smart assistants, self-driving cars, and spam filters. The future of AI is uncertain but it has potential to transform many industries through automation and improved decision making. Challenges include the costs of development and potential job disruption.
[DSC MENA 24] Nezar_El_Kady_-_From_Turing_to_Transformers__Navigating_the_AI_...DataScienceConferenc1
In this insightful talk, we'll embark on a journey from the origins of programming in 1883 and the conceptualization of AI in the 1950s, to the current explosion of AI applications reshaping our world. We'll unravel why AI has surged to prominence in the last decade, driven by unprecedented data generation and significant hardware advancements. With examples ranging from individual email filtering to complex supply chain optimizations, we'll explore AI's pervasive impact across various sectors including finance, manufacturing, healthcare, and media. The talk will address the challenges of AI implementation, such as the high cost of AI teams and the quest for universally applicable models, while highlighting the promising horizon of no-code AI platforms democratizing access. Furthermore, we'll delve into the ethical dimensions of AI, from biases to privacy concerns, and the pressing question of AI's potential to replace human roles. Lastly, we'll discuss the transformative potential of language models and generative AI, underscoring the importance of understanding and integrating AI into our lives and businesses for a future that's both scalable and sustainable.
How to gain a business advantage with the AI superpower, the solution for Banking and Financial Services Industry. Giving you an edge on your competition while increasing customer retention.
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This presentation by OECD, OECD Secretariat, was made during the discussion “Competition and Regulation in Professions and Occupations” held at the 77th meeting of the OECD Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found at oe.cd/crps.
This presentation was uploaded with the author’s consent.
This presentation by Professor Alex Robson, Deputy Chair of Australia’s Productivity Commission, was made during the discussion “Competition and Regulation in Professions and Occupations” held at the 77th meeting of the OECD Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found at oe.cd/crps.
This presentation was uploaded with the author’s consent.
2. Table of Contents
2
Introduction
o What is AI?
o Introduction to AI Levels?
o Types of Artificial Intelligence
o AI VS machine learning vs deep learning
o Where is AI used?
o AI use cases
o Why is AI booming now?
o AI trend in 2020
Machine Learning
o What is Machine Learning?
o 7 Steps of Machine learning
o Machine Learning vs. Traditional Programming
o How does machine learning work?
o Machine learning Algorithms
o Machine learning use cases
o How to choose Machine Learning Algorithm
o Why to use decision tree algorithm learning
o Challenges and Limitations of Machine learning
o Application of Machine learning
o Why is machine learning important?
02
Deep Learning
o What is Deep Learning?
o Deep learning Process
o Classification of Neural Networks
o Types of Deep Learning Networks
o Feed-forward neural networks
o Recurrent neural networks (RNNs)
o Convolutional neural networks (CNN)
o Reinforcement Learning
o Examples of deep learning applications
o Why is Deep Learning Important?
o Limitations of deep learning
03
Difference between AI vs ML vs DL
o What is AI?
o What is ML?
o What is Deep Learning?
o Machine Learning Process
o Deep Learning Process
o Difference between Machine Learning and Deep
Learning
o Which is better to start AI,ML or Deep learning
04
01
3. Table of Contents
3
Unsupervised Machine Learning
o What is Unsupervised Learning?
o How Unsupervised Machine Learning works
o Types of Unsupervised Learning
o Disadvantages of Unsupervised Learning
06
Reinforcement Learning
o What is reinforcement learning?
o How reinforcement learning works
o Types of reinforcement learning
o Advantage of reinforcement learning
o Disadvantage of reinforcement learning
07
Back Propagation Neural Network in AI
o Back Propagation Neural Network in AI
o What is Artificial Neural Networks?
o What is Backpropagation?
o Why We Need Backpropagation?
o What is a Feed Forward Network?
o Types of Backpropagation Networks
o Best practice Backpropagation
08
Supervised Machine Learning
o Types of Machine Learning
o What is Supervised Machine Learning?
o How Supervised Learning Works
o Types of Supervised Machine Learning Algorithms
o Supervised vs. Unsupervised Machine learning
techniques
o Advantages of Supervised Learning
o Disadvantages of Supervised Learning
05
Expert System in Artificial Intelligence
o What is an Expert System?
o Examples of Expert Systems
o Characteristic of Expert System
o Components of the expert system
o Conventional System vs. Expert system
o Human expert vs. expert system
o Benefits of expert systems
o Limitations of the expert system
o Applications of expert systems
09
4. 4
Introduction
01
o What is AI?
o Introduction to AI Levels?
o Types of Artificial Intelligence
o AI VS machine learning vs deep learning
o Where is AI used?
o AI use cases
o Why is AI booming now?
o AI trend in 2020
5. Artificial intelligence (AI) is a popular branch of computer science that concerns with building
“intelligent” smart machines capable of performing intelligent tasks.
With rapid advancements in deep learning and machine learning, the tech industry is transforming
radically.
Artificial Intelligence
Transforming the Nature of Work, Learning, and Learning to Work
5
Artificial Intelligence
Machine Learning
Deep Learning
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6. Introduction to AI Levels?
6
Artificial Narrow Intelligence
Artificial General Intelligence
Artificial Super Intelligence
Types of
Artificial
Intelligence
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7. Types of Artificial Intelligence
7
Deep Learning Machine Learning Artificial Intelligence
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8. Artificial Intelligence
8
Artificial intelligence (AI) is a popular
branch of computer science that concerns
with building “intelligent” smart machines
capable of performing intelligent tasks.
With rapid advancements in deep learning
and machine learning, tech industry is
transforming radically.
2018
2019
2020
2017 2016 2015
AI
2013 2014 2015 2016 2017
AI
Text Here
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editable. Adapt it to
your needs and
capture your
audience's attention.
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editable. Adapt it to
your needs and
capture your
audience's attention.
65%
35%
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editable. Adapt it to
your needs and
capture your
audience's attention.
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editable. Adapt it to
your needs and
capture your
audience's attention.
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to your needs and capture your
audience's attention.
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editable. Adapt it to
your needs and
capture your
audience's attention.
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editable. Adapt it to
your needs and
capture your
audience's attention.
9. Machine Learning
9
Machine learning is a type of AI that enables machines to
learn from data and deliver predictive models.
The machine learning is not dependent on any explicit
programming but the data fed into it. It is a complicated
process.
Based on the data you feed into machine learning
algorithm and the training given to it, an output is
delivered.
A predictive algorithm will create a predictive model.
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editable. Adapt it to your
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audience's attention.
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needs and capture your
audience's attention.
Information
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10. Deep Learning
10
Deep Learning is a subfield of machine learning that is
concerned with algorithms inspired by the brain's structure
A computer model can be taught using Deep Learning to
run classification actions using pictures, texts or sounds
as input
&
functions known as artificial neural
networks
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11. AI VS Machine Learning VS Deep Learning
11
o Artificial Intelligence originated around
1950s
o AI represents simulate intelligence in
machines
o AI is a subset of data science
o Aim is to build machines which are
capable of thinking like humans
o Machine Learning originated around
1960s
o Machine learning is the practice of
getting machines to make decisions
without being programmed
o Machine learning is a subset of AI &
Data Science
o Aim is to make machines learn through
data so that they can solve problems
Artificial Intelligence Machine Learning Deep Learning
o Deep Learning originated around 1970s
o Deep Learning is the process of using
artificial neural networks to solve complex
problems
o Deep Learning is a subset of Machine
Learning, AI & Data Science
o Aim is to build neural networks that
au6tonetically discover patterns for
feature detection
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12. Where is AI used?
12
Customer Experience
Supply Chain
Human Resources
Knowledge Creation
Research & Development
Fraud Detection
Real-time Operations Management
Customer Services
Risk Management & Analytics
Customer Insight
Pricing & Promotion
Predictive Analytics
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13. AI Usecase in HealthCare
13
Research
Training Keeping Well
Early Detection
Diagnosis
Decision Making
Treatment
End of Life Care
AI and
Robotics
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14. AI Use Cases in Human Resource
14
LEARNING
o Curated Training
o Skill Development
RECRUITING
o Dynamic Career Sites
o Smart Sourcing
ENGAGEMENT
o HR Chatbot
o Engagement Surveys
ONBOARDING
o Automated Messages
o Curated Videos
Employee Life Cycle
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15. AI in Banking for Fraud Detection
15
Authorization System
Neural
Network Engine
Scoring Engine
Case Creation Module
Case
Management
Database
Expert Authorization
Response Module
Expert Rules Base
Configuration
Workstation
Expert Rules Execute
Rules Definition
Cardholder
Profiles
Postings
Payment System
Nonmonetary System
Analyst
Workstation
Auth Request
1
3
Case Creation Rules Execute
6
Payment and Non-Monetary Transactions
8
Auth Recommendation
4
Auth Request & Score
2
Transaction & Score
5
Case Information
7
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16. AI in Supply Chain
16
Structured & Unstructured
Information
Regulatory
Data
B2B Transaction
Data
Inventory
Data
Multimedia
Data
Sensor
Data
Logistics
Data
Trading
Partner
Data
Social
Media
Data
Procurement Manufacturing Customers Service
Logistics
Pervasive Visibility
Proactive
Replenishment
Predictive Maintenance
Secure Device
Maintenance
Ecosystem
Integration
Unified
Messaging
Actionable
Insights
IIOT – Securely Provisionally People, System and Things
Secure Access Via Identity Management for Transient Users
Digital Ecosystem Data Lake
17. Ai Chatbots in Healthcare
17
Search Engine
Users learn to search
for information
Social Platforms
Like Facebook
connect users online
Smartphones
Bring the internet online
App Eco-system
Lets users download
and use apps easily
Messenger Apps
Lets users chat
anywhere, anytime
Artificial Intelligence
Self Learning machines
becomes smarter the
more they are used
Healthbots
Bring all of the above
together for
healthcare use cases
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18. Why is AI booming now?
18
Global AI Revenue Forecast by 2025, Ranked by Use Case in millions US Dollar
8,097 7,540 7,336
4,680
4,201
3,714 3,655 3,564 3,169
0
2,000
4,000
6,000
8,000
Static Image Recognition,
Classification and Tagging
Algorithm Trading
Strategy Performance
Management
Efficient, Scalable
Processing of Patient Data
Predictive Maintenance Object Identification,
detection, classification,
tracking
Text Query of Images Automated Geophysical
Feature Detection
Content Distribution on
Social Media
Object detection &
classification, avoidance,
navigation
100% 92% 84%
45%
54%
United
States
China India
Germany
Israel
Penetration of Artificial Intelligence Skills, by Country
22%
24%
27%
36%
37%
51%
60%
60%
66%
71%
0 20 40 60 80 100
Fleet Mobile
Expediting Transactions
Production Floor Systems
Logistics & Supply Chain
Monitoring Through External Devices/Systems
Operational Environment
HR/Workforce Management
Security/Fraud
Customer Relation/Interaction (i.e., chatbots)
External Communication
Organizations deploying AI, by Functional Areas
This graph/chart is linked to excel, and changes automatically based on data. Just left click on it and select “Edit Data”.
19. 10 AI Trend in 2020
19
Robotic Process Automation
AI will make Healthcare more Accurate
Data Modeling will move to the Edge
AI will Come for B2B
Ai-powered Chatbots
AI In Retail
Aerospace and Flight Operations
Controlled by AI
AI Mediated Media and Entertainment
Advanced Cybersecurity
Automated Business Process
AI Trends”
“2020
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20. 20
Machine Learning
02
o What is Machine Learning?
o 7 Steps of Machine learning
o Machine Learning vs. Traditional Programming
o How does machine learning work?
o Machine learning Algorithms
o Machine learning use cases
o How to choose Machine Learning Algorithm
o Why to use decision tree algorithm learning
o Challenges and Limitations of Machine learning
o Application of Machine learning
o Why is machine learning important?
21. Machine Learning
21
Machine Learning is the result of General AI that involves developing machines
that can deliver results better than humans
Traditional
Programming
Data
(Input)
Program
Output
Data
(Input)
Output
Machine
Learning
Program
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“Learning”
Machine
Learning System
Input Data:
Feed Learner
Various Data
Output Data:
Present Rules
22. 7 Steps of Machine Learning
22
Gathering Data
Preparing that Data Choosing a Model
Training Evaluation
Hyperparameter Tuning Prediction
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23. Machine Learning vs. Traditional Programming
23
Traditional Modelling
Prediction
Result
Machine Learning
Learning
Model
Prediction
Result
Computer
New Data
Model
Sample Data
Expected
Result
Data
Handcrafted
Model
Computer
Computer
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24. How does Machine Learning Work?
24
Identify, the problem to
be solved and create a
clear objective.
Define Objectives
Preparing data is a crucial
step and involves building
workflows to clean, match and
blend the data.
Prepare Data
Data is fed as input and the
algorithm configured with the
required parameters. A
percent of the data can be
utilized to train the model.
Train Model
Publish the prepared
experiment as a web
service, so applications can
use the model.
Integrate Model
Collect data from hospitals,
health insurance companies,
social service agencies, police
and fire dept.
Collect Data
Depend on the problem to be solved
and the type of data an appropriate
algorithm will be chosen.
Select Algorithm
The remaining data is utilized to test the
model, for accuracy. Depending on the
results, improvements, can be performed in
the “Train model’ and/or “Select Algorithm”
phases, iteratively.
Test Model
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25. Machine Learning Algorithms
25
Machine Learning
o Linear
o Polynomial
o KNN
o Trees
o Logistic Regression
o Naïve-Bayes
o SVM
Regression
Decision Tree
Random Forest
Clustering
o SVD
o PCA
o K-means
o Apriori
o FP-Growth
Continuous
Categorical
Reinforcement
Classification
Association
Anlaysis
Hidden Markov
Model
Supervised Unsupervised
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26. Machine Learning Use Cases
26
Energy Feedstock &
Utilities
o Power Usage Analytics
o Seismic Data
Processing
o Your Text Here
o Smart Grid
Management
o Energy Demand &
Supply Optimization
Financial
Services
o Risk Analytics &
Regulation
o Customer Segmentation
o Your Text Here
o Credit Worthiness
Evaluation
Travel &
Hospitality
o Aircraft Scheduling
o Dynamic Pricing
o Your Text Here
o Traffic Patterns &
Congestion
Management
Manufacturing
o Predictive
Maintenance or
Condition Monitoring
o Your Text Here
o Demand Forecasting
o Process Optimization
o Telematics
Retail
o Predictive Inventory
Planning
o Recommendation
Engines
o Your Text Here
o Customer ROI &
Lifetime Value
Healthcare &
Life Sciences
o Alerts & Diagnostics
from Real-time Patient
Data
o Your Text Here
o Predictive Health
Management
o Healthcare Provider
Sentiment Analysis
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27. How to Choose Machine Learning Algorithm
27
How to Select Machine Learning Algorithms
What do you want to
do with your Data?
How to Select Machine Learning Algorithms
Algorithm Cheat Sheet
Additional
Requirements
Accuracy Linearity Number of
Parameters
Training
Time
Number of
Features
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28. Why use Decision Tree Machine Learning Algorithm?
28
Decision Trees
To Classify
Decision Tree
Non-linear Relationship
between Predictors &
Response
Linear Relationship
between Predictors
& Response
Use c4.5
Implementation
Use Standard
Regression Tree
Responsible
Variable has only
2 Categories
Response Variable has
Multiple Categories
Use Standard
Classification here
Use c4.5
Implementation
To Predict
Responsible
variable is
Continuous
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29. Challenges and Limitations of Machine learning
29
Advantages Disadvantages
Handling multi-dimensional & multi-
variety Data
Data
Acquisition
High error-
Susceptibility
Time and
Resources
Interpretation
Results
Easily Identifies Trends and Patterns
No Human Intervention needed
Continuous Improvement
Wide Applications
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30. Application of Machine Learning
30
Automatic Language
Translation
Medical Diagnosis
Stock Market Trading
Online Fraud Detection
Virtual Personal Assistant
Email Spam and Malware Filtering
Self Driving Cars
Product Recommendations
Traffic Prediction
Speech Recognition
Image Recognition
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31. Why is Machine Learning Important?
31
Phase 1 : Learning
Training
Data
o Normalization
o Dimension Reduction
o Image Processing, etc.
Pre-Processing
o Supervised
o Unsupervised
o Minimization, etc.
o Precision/recall
o Over fitting
o Test/cross Validation
data, etc.
Error Analysis
Model
New Data
Learning
Predicted Data
Prediction
Phase 2: Prediction
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32. 32
Deep Learning
o What is Deep Learning?
o Deep learning Process
o Classification of Neural Networks
o Types of Deep Learning Networks
o Feed-forward neural networks
o Recurrent neural networks (RNNs)
o Convolutional neural networks (CNN)
o Reinforcement Learning
o Examples of deep learning applications
o Why is Deep Learning Important?
o Limitations of deep learning
03
33. What is Deep Learning?
33
Deep Learning is a subfield of machine learning that is concerned with
algorithms inspired by the brain's structure and functions known as
artificial neural networks.
A computer model can be taught using Deep Learning to run
classification actions using pictures, texts or sounds as input.
Feature Extraction + Classification
Input Output
Car
Not Car
What is Deep Learning?
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34. Deep Learning Process
34
Understand
the Problem
Identify
Data
Select Deep
Learning
Algorithms
Training
the Model
Test the
Model
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35. Classification of Neural Networks
35
x1
x2
xn
v11
v12
vpn
w11
w22
wmp ym
y2
y1
1
2
p
1
2
m
V1n
w1p
Input Layer Hidden Layer Output Layer
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36. Types of Deep Learning Networks
36
Supervised
Artificial Neural Networks Used for Regression & Classification
Convolutional Neural Networks Used for Computer Vision
Recurrent Neutral Networks Used for Time Series Analysis
Unsupervised
Self-Organizing Maps Used for Feature Detection
Deep Boltzmann Machines Used for Recommendation Systems
AutoEncoders Used for Recommendation Systems
o Artificial Neural Networks (ANN)
o Convolutional Neural Networks (CNN)
o Recurrent Neural Networks (RNN)
o Self Organizing Maps (SOM)
o Boltzmann Machines (BM)
o AutoEncoders (AE)
Supervised Unsupervised
Deep Learning
Models
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37. Feed-forward Neural Networks
37
Input Layer Hidden Layer Output Layer
Variable- #1
Variable- #2
Variable- #3
Variable- # 4
Output
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38. Recurrent Neural Networks (RNNs)
38
x1
x2
y
Input Layer
Recurrent Network
Output Layer
Hidden Layers
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39. Convolutional Neural Networks (CNN)
39
Take Car = A1 Truck = B1 VAN = C1 Bicycle = D1 Rest all be Same
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40. Reinforcement Learning
40
Reinforcement Learning
uses rewards and punishment to train computing
models to perform a sequence of selections. Here
computing faces a game-like scenario where
it employs trial and error to answer. Based on the
action it performs, computing gets either rewards
or penalties. Its goal is to maximize the rewards.
Exploration Policy Neural Networks
Filters Memory
Algorithm
Agent Environment
Action
State, Reward
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41. Examples of Deep Learning Applications
41
Image
Recognition
Portfolio
Management &
Prediction of Stock
Price Movements
Speech
Recognition
Natural Language
Processing
Drug Discovery &
Better Diagnostics of
Diseases in Healthcare
Robots and Self
- Driving Cars
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42. Why is Deep Learning Important?
42
Deep Learning
Other Learning
Algorithms
Performance
Data
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43. Limitations of Deep Learning
43
Limitations of
Deep Learning
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Interpretability
Statistical Reasoning
Amount of Data
44. 44
Difference between AI vs ML vs DL
04
o What is AI?
o What is ML?
o What is Deep Learning?
o Machine Learning Process
o Deep Learning Process
o Difference between Machine Learning and Deep Learning
o Which is better to start AI,ML or Deep learning
45. Difference between AI vs ML vs DL
45
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Engineering of
making intelligent
machines and
programs
Artificial
Intelligence
Ability to learn without
being explicitly
programmed
Machine
Learning
Learning
based on deep
neural network
Deep
Learning
46. 46
What is AI?
Artificial
Intelligence
(AI) is a popular branch of computer science that
concerns with building “intelligent” smart
machines capable of performing intelligent tasks.
With rapid advancements in deep learning and
machine learning, the tech industry is
transforming radically.
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47. What is ML?
47
Ordinary System With AI Machine Learning
Machine Learning
is a type of AI that enables machines to learn from data and deliver predictive models. The
machine learning is not dependent on any explicit programming but the data fed into it. It is a
complicated process. Based on the data you feed into machine learning algorithm and the training
given to it, an output is delivered. A predictive algorithm will create a predictive model.
Introduction to Machine learning
Learns
Predicts
Improves
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48. What is Deep Learning?
48
Artificial intelligence (AI) is a popular branch of
computer science that concerns with building
“intelligent” smart machines capable of
performing intelligent tasks.
With rapid advancements in deep learning and
machine learning, the tech industry is
transforming radically.
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49. Modelling
Candidate & Final
Visualisation
Predictions & Strategy
Data
Raw & Training Data
Machine Learning Process
49
Data
Gathering
Data
Cleaning
Selecting
Right Algorithms
Building
Model & Finalising
Data Transformation
into Predictions
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50. Deep Learning Process
50
Understand
the Problem
Identify
Data
Select Deep
Learning
Algorithm Training
the Model
Test
the Model
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51. Difference between Machine Learning and Deep Learning
51
Machine Learning
Input Feature Extraction Classification Output
Car
Not Car
Deep Learning
Input Output
Feature Extraction + Classification
Car
Not Car
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52. Which is better to start AI,ML or DL?
52
Any Technique which enables computers to mimic human
behavior.
Artificial Intelligence
Subset of ML which make the Computation of Multi-layer
Neural Networks Feasible.
Deep Learning
Subset of AI Techniques which use Statistical Methods to
Enable Machines to Improve with Experiences.
Machine Learning
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53. 53
Supervised Machine Learning
05
o Types of Machine Learning
o What is Supervised Machine Learning?
o How Supervised Learning Works
o Types of Supervised Machine Learning Algorithms
o Supervised vs. Unsupervised Machine learning techniques
o Advantages of Supervised Learning:
o Disadvantages of Supervised Learning
54. Types of Machine Learning
54
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Supervised
Learning
Unsupervised
Learning
Reinforcement
Learning
Inputs Outputs
Rewards
Inputs Outputs
Inputs Outputs
Training
Makes Machine Learn Explicitly
Data with Clearly defined Output is
given
Direct feedback is given
Predicts outcome/future
Resolves Classification and
Regression Problems
Machine Understands the data
(Identifies Patterns/ Structures)
Evaluation is Qualitative or Indirect
Does not Predict/Find anything
Specific
An approach to AI
Reward Based Learning
Learning form +ve & +ve
Reinforcement
Machine Learns how to act in a
Certain Environment
To Maximize Rewards
55. What is Supervised Machine Learning?
55
Input Raw Data
Processing
Output
Algorithm
Training
Data set
Desired
Output
Supervised Learning
Supervisor
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56. How Supervised Machine Learning works
56
Step 1
Provide the Machine Learning Algorithm Categorized or
“labeled” Input and Output Data from to Learn
Step 2
Feed the Machine New, Unlabeled Information to
See if it Tags New Data Appropriately. If not, Continue
Refining the Algorithm
Machine Machine
Group 1
Group 2
Types of Problems to which it’s Suited
Classification
Sorting Items into Categories
Regression
Identifying Real Values
(Dollars, Weight, etc.)
Label
“Group 1”
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57. Types of Supervised Machine Learning Algorithms
57
Classification
o Fraud Detection
o Email Spam Detection
o Diagnostics
o Image Classification
Regression
o Risk Assessment
o Score Prediction
Supervised
Learning
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58. Supervised vs. Unsupervised Machine Learning Techniques
58
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Supervised
Learning
o Classification
o Regression
Input & Output Data
Unsupervised
Learning
o Clustering
o Association
Input Data
Predictions &
Predictive Models
Patterns / Structure
Discovery
59. Advantages of Supervised Learning
59
It allows you to be very specific about the definition of the labels. In other words, you'll train
the algorithm to differentiate different classes where you'll set a perfect decision boundary.
You are ready to determine the amount of classes you would like to possess.
The input file is extremely documented and is labeled.
The results produced by the supervised method are more accurate and reliable as
compared to the results produced by the unsupervised techniques of machine learning. this
is often mainly because the input file within the supervised algorithm is documented and
labeled. this is often a key difference between supervised and unsupervised learning.
The answers within the analysis and therefore the output of your algorithm are likely to be
known thanks to that each one the classes used are known.
Advantages
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60. Disadvantages of Supervised Learning
60
o Supervised learning are often a
posh method as compared with
the unsupervised method. The
key reason is that you
simply need
to understand alright and label the
inputs in supervised learning.
o It doesn’t happen in real time while the
unsupervised learning is about the
important time. this is often also a
serious difference between supervised
and unsupervised learning. Supervised
machine learning uses of-line analysis.
o It is needed tons of computation
time for training.
o If you've got a dynamic big and
growing data, you're unsure of the
labels to predefine the principles.
this will be a true challenge.
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61. 61
Unsupervised Machine Learning
o What is Unsupervised Learning?
o How Unsupervised Machine Learning works
o Types of Unsupervised Learning
o Disadvantages of Unsupervised Learning
06
62. What is Unsupervised Learning?
62
Unsupervised Learning
Input Raw Data Output
Algorithm
Interpretation Processing
o Unknown output
o No Training Data Set
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63. How Unsupervised Machine Learning works
63
Step 1
Provide the machine learning algorithm uncategorized,
unlabeled input data to see what patterns it finds
Step 2
Observe and learn from the patterns the
machine identifies
Machine Machine
Similar Group 1
Similar Group 2
Types of Problems to Which it’s Suited
Clustering
Identifying similarities in groups
For Example: Are there patterns in the data to
indicate certain patients will respond better to
this treatment than others?
Anomaly Detection
Identifying abnormalities in data
For Example: Is a hacker intruding in our
network?
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64. Types of Unsupervised Learning
64
Dimensionality
Reduction
o Text Mining
o Face Recognition
o Big Data Visualization
o Image Recognition
Clustering
o Biology
o City Planning
o Targeted Marketing
Unsupervised
Learning
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65. Disadvantages of Unsupervised Learning
65
You cannot get very specific about the definition of the
info sorting and therefore the output. This is
often because the info utilized in unsupervised learning is
labeled and not known. It's employment of the machine to
label and group the data before determining the hidden
patterns.
Less accuracy of the results. This is often also because
the input file isn't known and not labeled by
people beforehand , which suggests that the machine will got
to do that alone.
The results of the analysis can't be ascertained. there's no
prior knowledge within the unsupervised method of machine
learning. Additionally, the numbers of classes also are not
known. It results in the lack to determine the results
generated by the analysis.
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66. 66
Reinforcement learning
07
o What is reinforcement learning?
o How reinforcement learning works
o Types of reinforcement learning
o Advantage of reinforcement learning
o Disadvantage of reinforcement learning
67. What is Reinforcement Learning?
67
Input Response Feedback Learns
It’s a
mango
Wrong!
It’s an
apple
Noted
It’s an
Apple
Reinforced
Response
Input
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68. How Reinforcement Learning Works?
68
Reinforcement Learning
Input Raw Data Output
Reward
State
Selection of
Algorithm
Best Action
Environment
Agent
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69. Types of Reinforcement Learning
69
Gaming
Finance Sector
Inventory Management
Manufacturing
Robot Navigation
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70. Disadvantage of Reinforcement Learning
70
Less accuracy of the results. this is often also because the input file isn't known and not labeled by
people beforehand , which suggests that the machine will got to do that alone.
The results of the analysis can't be ascertained. There's no prior knowledge within the unsupervised
method of machine learning. Additionally, the numbers of classes also are not known. It results
in the lack to determine the results generated by the analysis Reinforcement learning as a
framework is wrong in many various ways, but it's precisely this quality that creates it useful.
You cannot get very specific about the definition of the info sorting and therefore the output. this is
often because the info utilized in unsupervised learning is labeled and not known. it's employment of
the machine to label and group the data before determining the hidden patterns.
Too much reinforcement learning can cause an overload of states which may diminish the results.
Reinforcement learning isn't preferable to use for solving simple problems.
Reinforcement learning needs tons of knowledge and tons of computation. it's data-
hungry. that's why it works rather well in video games because one can play the sport again and
again and again, so getting many data seems feasible.
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71. 71
Back Propagation Neural Network in AI
08
o Back Propagation Neural Network in AI
o What is Artificial Neural Networks?
o What is Backpropagation?
o Why We Need Backpropagation?
o What is a Feed Forward Network?
o Types of Backpropagation Networks
o Best practice Backpropagation
72. Back Propagation Neural Network in AI
72
1 2
i1
i2 h2
w1
b1 b2
net out
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73. What is Artificial Neural Networks?
73
Feed-Forward
Network Output
Input Layer
Network Inputs
Hidden Layer
Back Propagation
Output Layer
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74. What is Backpropagation Neural Networking?
74
x
x
x
w
w
w
w
Difference in
Desired Values
Backprop Output Layer
Input Layer
1
1
Hidden Layer(s)
3
Output Layer
5
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75. Why We Need Backpropagation?
75
Backpropagation is fast, simple and
straightforward to program.
It has no parameters to tune aside
from the numbers of input.
It is a versatile method because it doesn't require prior
knowledge about the network.
It doesn't need any special mention
of the features of the function to
be learned.
It is a typical method that generally
works well.
Most prominent
advantages of
Backpropagation
Are:
76. What is a Feed Forward Network?
76
Input Layer
Hidden Layer
Output Layer
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77. Types of Backpropagation Networks
77
It is one quite backpropagation network which
produces a mapping of a static input for static
output. it's useful to unravel static classification
issues like optical character recognition.
Static Back-propagation
Recurrent backpropagation is fed forward until a
hard and fast value is achieved. Then, the error is
computed and propagated backward.
Recurrent Backpropagation
The main difference between both of those methods is: that the mapping is rapid in static back-propagation
while it's nonstatic in recurrent backpropagation
o Static Back-propagation
o Recurrent Backpropagation
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78. Best Practice Backpropagation
78
A neural network is a group of connected it I/O
units where each connection features
a weight related to its computer programs.
Backpropagation may be a short form for
"backward propagation of errors." it's a
typical method of coaching artificial neural
networks.
Backpropagation is fast, simple and
straightforward to program.
A feedforward neural network is a man-
made neural network.
BACKPROPAGATION
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79. 79
Expert System in Artificial Intelligence
09
o What is an Expert System?
o Examples of Expert Systems
o Characteristic of Expert System
o Components of the expert system
o Conventional System vs. Expert system
o Human expert vs. expert system
o Benefits of expert systems
o Limitations of the expert system
o Applications of expert systems
80. Expert System in Artificial Intelligence
80
Knowledge
Base
Inference
Engine
User
Interface
User
(May not be an expert)
Knowledge
Engineer
Human
Expert
The Expert System in AI are computer
applications. Also, with the assistance of
this development, we will solve complex
problems. it's level of human intelligence
and expertise
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81. Examples of Expert Systems
81
Expert System
User
Interface
Knowledge
Base
Inference
Engine
Non-expert
User
Knowledge from
an expert
Query
Advice
The Highest Level of Expertise
o The expert system offers the very best level of experience. It provides
efficiency, accuracy and imaginative problem-solving.
Right on Time Reaction
o An Expert System interacts during a very reasonable period of your time with the
user. the entire time must be but the time taken by an expert to urge the
foremost accurate solution for an equivalent problem.
Good Reliability
o The expert system must be reliable, and it must not make any an error.
Flexible
o It is significant that it remains flexible because it the is possessed by an
Expert system.
Effective Mechanism
o Expert System must have an efficient mechanism to administer the
compilation of the prevailing knowledge in it.
Capable of Handling Challenging Decision & Problems
o An expert system is capable of handling challenging decision problems and
delivering solutions.
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82. Characteristic of Expert System
o The system must be capable of responding at A
level of competency adequate to or better than
an expert system within the field. the
standard of the recommendation given by the
system should be during a high level integrity
and that the performance ratio should be also
very high
o The system should be designed in
such how that it's ready to perform
within alittle amount of your time , like or
better than the time taken by a
person's expert to succeed in at a
choice point. An expert system that takes
a year to succeed in a choice compared
to a person's expert’s time of
1 hour wouldn't be useful
o Expert systems use symbolic
representations for knowledge
(rules, networks or frames) and
perform their inference through
symbolic computations that
closely resemble manipulations
of tongue
Use Symbolic
Representations
Adequate Response Time
High level Performance
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o Expert systems are typically very domain specific. For ex.,
a diagnostic expert system for troubleshooting computers
must actually perform all the required data manipulation
as a person's expert would. The developer of such a
system must limit his or her scope of the system to
only what's needed to unravel the target problem. Special
tools or programming languages are often needed to
accomplish the precise objectives of the system
Domain Specificity
o The expert system must be as reliable as a
person's expert
Good Reliability
o The system should be understandable
i.e. be ready to explain the steps of
reasoning while executing. The expert
system should have an
evidence capability almost like the
reasoning ability of human experts
Understandable
83. Components of the Expert System
83
Explanation
Inference
Engine
Knowledge
Base
Acquisition
Facility
User
Interface
Experts and
Knowledge
Engineers
Users
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84. Conventional System vs. Expert System
84
Knowledge domain break away the mechanism processing
The program could have made an error
Not necessarily need all the input/data
Changes within the rule are often made with ease
The system can work only with the rule as a tittle
Information and processing combined during a sequential file
The program isn't wrong
Need all the input file
Changes to the program are inconvenient
The system works if it's complete
01
02
03
04
05 05
04
03
02
01
vs
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85. Human Expert vs. Expert System
85
Perishable
Difficult to Transfer
Difficult to Document
Expensive, especially top notch
Add Your Text Here
Permanent
Easy to Transfer
Easy to Document
Affordable, costly to develop, but cheap to operate
Add Your Text Here
Human Experts (Artificial ) Expert Systems
86. Benefits of Expert Systems
86
Fast Response
Easy to Develop and
Modify the System
Low Accessibility
Cost
Humans
Emotions are not
Affected
Error Rate are
Very Low
Data Warehousing
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87. Limitations of the Expert System
87
Don’t Have Decision Making
Power Like Humans
Its Difficult to Maintain
Its Developed for a Specific Domain
Expert System is not Widely
used or Tested
Not Able to Explain the Logic
Behind the Decision
It cant Deal with the
Mixed Knowledge
Development Cost is High There are Chances of Errors
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88. Knowledge domain (Finding out the faults in vehicles, computer)
Finance/Commerce (Stock market trading, airline
scheduling cargo scheduling)
Process Control System
Repairing
Monitoring system
Medical domain (Diagnosis
system, medical operations)
Warehousing
Optimization
Shipping
Design domain (Camera lens
design,automobile design)
Applications of Expert Systems
88
91. Bar Chart
91
0
10
20
30
40
50
60
70
80
90
100
Jan Feb Mar Apr May Jun
Sales
(
in
USD
millions)
Year 2020
100%
This graph/chart is linked to excel, and
changes automatically based on data. Just
left click on it and select “Edit Data”.
Product 01
This graph/chart is linked to excel, and
changes automatically based on data. Just
left click on it and select “Edit Data”.
Product 02
Product
02
Product
01
92. Stacked Column
92
This graph/chart is linked to excel, and
changes automatically based on data. Just
left click on it and select “Edit Data”.
Product 01
This graph/chart is linked to excel, and
changes automatically based on data. Just
left click on it and select “Edit Data”.
Product 02
4.3
2.5
-3.5
-4.5
2.4
4.4
-1.8
-0.8
-6
-4
-2
0
2
4
6
8
Sales(in
USD
millions)
93. Welcome to Our Agenda
93
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Agenda 01
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Agenda 02
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Agenda 03
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Agenda 04
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Agenda 05
94. Our Goal
94
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Goal 3
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Goal 1
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Goal 2
95. Idea
Generation
95
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96. Venn
96
25%
75%
35%
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97. Timeline
97
2020
2016 2017 2018 2019
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98. Post It Notes
98
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99. Thank You
99
# street number, city, state
Address
0123456789
Contact Number
emailaddress123@gmail.com
Email Address