(** Machine Learning Engineer Masters Program: https://www.edureka.co/masters-progra... **)
This PPT on Artificial Intelligence Interview Questions covers all the important concepts involved in the field of AI. This PPT is ideal for both beginners as well as professionals who want to learn or brush up their knowledge on AI concepts. Below are the topics covered in this tutorial:
1. Artificial Intelligence Basic Level Interview Question
2. Artificial Intelligence Intermediate Level Interview Question
3. Artificial Intelligence Scenario based Interview Question
Check out the entire Machine Learning Playlist: https://bit.ly/2NG9tK4
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Artificial Intelligence Course | AI Tutorial For Beginners | Artificial Intel...Simplilearn
This Artificial Intelligence presentation will help you understand what is Artificial Intelligence, types of Artificial Intelligence, ways of achieving Artificial Intelligence and applications of Artificial Intelligence. In the end, we will also implement a use case on TensorFlow in which we will predict whether a person has diabetes or not. Artificial Intelligence is a method of making a computer, a computer-controlled robot or a software think intelligently in a manner similar to the human mind. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. Artificial Intelligence is emerging as the next big thing in the technology field. Organizations are adopting AI and budgeting for certified professionals in the field, thus the demand for trained and certified professionals in AI is increasing. As this new field continues to grow, it will have an impact on everyday life and lead to considerable implications for many industries. Now, let us deep dive into the AI tutorial video and understand what is this Artificial Intelligence all about and how it can impact human life.
The topics covered in this Artificial Intelligence presentation are as follows:
1. What is Artificial intelligence?
2. Types of Artificial intelligence
3. Ways of achieving artificial intelligence
4. Applications of Artificial intelligence
5. Use case - Predicting if a person has diabetes or not
Simplilearn’s Artificial Intelligence course provides training in the skills required for a career in AI. You will master TensorFlow, Machine Learning and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems without explicit programming.
Why learn Artificial Intelligence?
The current and future demand for AI engineers is staggering. The New York Times reports a candidate shortage for certified AI Engineers, with fewer than 10,000 qualified people in the world to fill these jobs, which according to Paysa earn an average salary of $172,000 per year in the U.S. (or Rs.17 lakhs to Rs. 25 lakhs in India) for engineers with the required skills.
Those who complete the course will be able to:
1. Master the concepts of supervised and unsupervised learning
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.
Comprehend the theoretic
Learn more at: https://www.simplilearn.com
Deep Learning Tutorial | Deep Learning TensorFlow | Deep Learning With Neural...Simplilearn
This Deep Learning presentation will help you in understanding what is Deep Learning, why do we need Deep learning, what is neural network, applications of Deep Learning, what is perceptron, implementing logic gates using perceptron, types of neural networks. At the end of the video, you will get introduced to TensorFlow along with a usecase implementation on recognizing hand-written digits. Deep Learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. Deep Learning, on the other hand, uses advanced computing power and special type of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. W will also understand neural networks and how they work in this Deep Learning tutorial video. This Deep Learning tutorial is ideal for professionals with beginner to intermediate level of experience. Now, let us dive deep into this topic and understand what Deep Learning actually is.
Below topics are explained in this Deep Learning presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. What is Neural network?
4. What is Perceptron?
5. Implementing logic gates using Perceptron
6. Types of Neural networks
7. Applications of Deep Learning
8. Working of Neural network
9. Introduction to TensorFlow
10. Use case implementation using TensorFlow
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
Deep Learning Interview Questions and Answers | EdurekaEdureka!
*** AI and Deep-Learning with TensorFlow - https://www.edureka.co/ai-deep-learning-with-tensorflow ***
This PPT covers most of the hottest deep learning interview questions and answers. It also provides you with an understanding process of Deep Learning and the various aspects of it.
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AI vs Machine Learning vs Deep Learning | Machine Learning Training with Pyth...Edureka!
Machine Learning Training with Python: https://www.edureka.co/python )
This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) on "AI vs Machine Learning vs Deep Learning" talks about the differences and relationship between AL, Machine Learning and Deep Learning. Below are the topics covered in this tutorial:
1. AI vs Machine Learning vs Deep Learning
2. What is Artificial Intelligence?
3. Example of Artificial Intelligence
4. What is Machine Learning?
5. Example of Machine Learning
6. What is Deep Learning?
7. Example of Deep Learning
8. Machine Learning vs Deep Learning
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
Introduction to Artificial Intelligence | AI using Deep Learning | EdurekaEdureka!
This slide on Artificial intelligence will give you an introduction to artificial intelligence with futuristic applications of AI. It also tells you how to implement artificial intelligence using deep neural networks.
The slide covers the following topics:
1. What is Artificial Intelligence & its applications
2. Subsets of AI - Machine Learning & Deep Learning
3. What is Deep Learning?
4. Use Case - Recognizing handwritten digits from MNIST dataset
5. Applications of Deep Learning
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
Artificial Intelligence Course | AI Tutorial For Beginners | Artificial Intel...Simplilearn
This Artificial Intelligence presentation will help you understand what is Artificial Intelligence, types of Artificial Intelligence, ways of achieving Artificial Intelligence and applications of Artificial Intelligence. In the end, we will also implement a use case on TensorFlow in which we will predict whether a person has diabetes or not. Artificial Intelligence is a method of making a computer, a computer-controlled robot or a software think intelligently in a manner similar to the human mind. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. Artificial Intelligence is emerging as the next big thing in the technology field. Organizations are adopting AI and budgeting for certified professionals in the field, thus the demand for trained and certified professionals in AI is increasing. As this new field continues to grow, it will have an impact on everyday life and lead to considerable implications for many industries. Now, let us deep dive into the AI tutorial video and understand what is this Artificial Intelligence all about and how it can impact human life.
The topics covered in this Artificial Intelligence presentation are as follows:
1. What is Artificial intelligence?
2. Types of Artificial intelligence
3. Ways of achieving artificial intelligence
4. Applications of Artificial intelligence
5. Use case - Predicting if a person has diabetes or not
Simplilearn’s Artificial Intelligence course provides training in the skills required for a career in AI. You will master TensorFlow, Machine Learning and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems without explicit programming.
Why learn Artificial Intelligence?
The current and future demand for AI engineers is staggering. The New York Times reports a candidate shortage for certified AI Engineers, with fewer than 10,000 qualified people in the world to fill these jobs, which according to Paysa earn an average salary of $172,000 per year in the U.S. (or Rs.17 lakhs to Rs. 25 lakhs in India) for engineers with the required skills.
Those who complete the course will be able to:
1. Master the concepts of supervised and unsupervised learning
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.
Comprehend the theoretic
Learn more at: https://www.simplilearn.com
Deep Learning Tutorial | Deep Learning TensorFlow | Deep Learning With Neural...Simplilearn
This Deep Learning presentation will help you in understanding what is Deep Learning, why do we need Deep learning, what is neural network, applications of Deep Learning, what is perceptron, implementing logic gates using perceptron, types of neural networks. At the end of the video, you will get introduced to TensorFlow along with a usecase implementation on recognizing hand-written digits. Deep Learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. Deep Learning, on the other hand, uses advanced computing power and special type of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. W will also understand neural networks and how they work in this Deep Learning tutorial video. This Deep Learning tutorial is ideal for professionals with beginner to intermediate level of experience. Now, let us dive deep into this topic and understand what Deep Learning actually is.
Below topics are explained in this Deep Learning presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. What is Neural network?
4. What is Perceptron?
5. Implementing logic gates using Perceptron
6. Types of Neural networks
7. Applications of Deep Learning
8. Working of Neural network
9. Introduction to TensorFlow
10. Use case implementation using TensorFlow
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
Deep Learning Interview Questions and Answers | EdurekaEdureka!
*** AI and Deep-Learning with TensorFlow - https://www.edureka.co/ai-deep-learning-with-tensorflow ***
This PPT covers most of the hottest deep learning interview questions and answers. It also provides you with an understanding process of Deep Learning and the various aspects of it.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
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LinkedIn: https://www.linkedin.com/company/edureka
AI vs Machine Learning vs Deep Learning | Machine Learning Training with Pyth...Edureka!
Machine Learning Training with Python: https://www.edureka.co/python )
This Edureka Machine Learning tutorial (Machine Learning Tutorial with Python Blog: https://goo.gl/fe7ykh ) on "AI vs Machine Learning vs Deep Learning" talks about the differences and relationship between AL, Machine Learning and Deep Learning. Below are the topics covered in this tutorial:
1. AI vs Machine Learning vs Deep Learning
2. What is Artificial Intelligence?
3. Example of Artificial Intelligence
4. What is Machine Learning?
5. Example of Machine Learning
6. What is Deep Learning?
7. Example of Deep Learning
8. Machine Learning vs Deep Learning
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
Introduction to Artificial Intelligence | AI using Deep Learning | EdurekaEdureka!
This slide on Artificial intelligence will give you an introduction to artificial intelligence with futuristic applications of AI. It also tells you how to implement artificial intelligence using deep neural networks.
The slide covers the following topics:
1. What is Artificial Intelligence & its applications
2. Subsets of AI - Machine Learning & Deep Learning
3. What is Deep Learning?
4. Use Case - Recognizing handwritten digits from MNIST dataset
5. Applications of Deep Learning
Natural Language Processing (NLP) is often taught at the academic level from the perspective of computational linguists. However, as data scientists, we have a richer view of the world of natural language - unstructured data that by its very nature has important latent information for humans. NLP practitioners have benefitted from machine learning techniques to unlock meaning from large corpora, and in this class we’ll explore how to do that particularly with Python, the Natural Language Toolkit (NLTK), and to a lesser extent, the Gensim Library.
NLTK is an excellent library for machine learning-based NLP, written in Python by experts from both academia and industry. Python allows you to create rich data applications rapidly, iterating on hypotheses. Gensim provides vector-based topic modeling, which is currently absent in both NLTK and Scikit-Learn. The combination of Python + NLTK means that you can easily add language-aware data products to your larger analytical workflows and applications.
Quantum Computers new Generation of Computers part 7 by prof lili saghafi Qua...Professor Lili Saghafi
Quantum algorithm
algorithm for factoring, the general number field sieve
Optimization algorithm
deterministic quantum algorithm Deutsch-Jozsa algorithm
Entanglement
Enigma
Quantum Teleportation
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
AI and its applications are not going away and will cause a significant amount of change to everyday life over the next decade. Whilst there has been a lot of buzz in the past that has not been fulfilled, advances in skills, computing power and modelling and ensuring that the hype is finally being realised. To some extent, we don’t even know what AI is capable of yet which is both exciting and scary!
Natural Language Processing (NLP) & Text Mining Tutorial Using NLTK | NLP Tra...Edureka!
** NLP Using Python: - https://www.edureka.co/python-natural-language-processing-course **
This Edureka PPT will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and much more along with a demo on each one of the topics.
The following topics covered in this PPT:
1. The Evolution of Human Language
2. What is Text Mining?
3. What is Natural Language Processing?
4. Applications of NLP
5. NLP Components and Demo
Follow us to never miss an update in the future.
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Supervised Machine learning in R is discussed with R basics and how to clean, pre-process , partitioning. It also discusess some algorithms and how to control training itself using cross-validation.
Module 8: Natural language processing Pt 1Sara Hooker
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org. If you would like to use this material to further our mission of improving access to machine learning. Education please reach out to inquiry@deltanalytics.org .
How to use Artificial Intelligence with Python? EdurekaEdureka!
YouTube Link: https://youtu.be/7O60HOZRLng
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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Castbox: https://castbox.fm/networks/505?country=in
Artificial Intelligence with Python | EdurekaEdureka!
YouTube Link: https://youtu.be/7O60HOZRLng
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Quantum Computers new Generation of Computers part 7 by prof lili saghafi Qua...Professor Lili Saghafi
Quantum algorithm
algorithm for factoring, the general number field sieve
Optimization algorithm
deterministic quantum algorithm Deutsch-Jozsa algorithm
Entanglement
Enigma
Quantum Teleportation
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
AI and its applications are not going away and will cause a significant amount of change to everyday life over the next decade. Whilst there has been a lot of buzz in the past that has not been fulfilled, advances in skills, computing power and modelling and ensuring that the hype is finally being realised. To some extent, we don’t even know what AI is capable of yet which is both exciting and scary!
Natural Language Processing (NLP) & Text Mining Tutorial Using NLTK | NLP Tra...Edureka!
** NLP Using Python: - https://www.edureka.co/python-natural-language-processing-course **
This Edureka PPT will provide you with a comprehensive and detailed knowledge of Natural Language Processing, popularly known as NLP. You will also learn about the different steps involved in processing the human language like Tokenization, Stemming, Lemmatization and much more along with a demo on each one of the topics.
The following topics covered in this PPT:
1. The Evolution of Human Language
2. What is Text Mining?
3. What is Natural Language Processing?
4. Applications of NLP
5. NLP Components and Demo
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Supervised Machine learning in R is discussed with R basics and how to clean, pre-process , partitioning. It also discusess some algorithms and how to control training itself using cross-validation.
Module 8: Natural language processing Pt 1Sara Hooker
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org. If you would like to use this material to further our mission of improving access to machine learning. Education please reach out to inquiry@deltanalytics.org .
How to use Artificial Intelligence with Python? EdurekaEdureka!
YouTube Link: https://youtu.be/7O60HOZRLng
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Artificial Intelligence with Python | EdurekaEdureka!
YouTube Link: https://youtu.be/7O60HOZRLng
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Artificial Intelligence PowerPoint Presentation Slide Template Complete Deck is a comprehensive virtual solution for technology experts. With the help of this PowerPoint theme, you can elucidate the differences between machine intelligence, machine learning, and deep learning. Employ our PPT presentation to cover merits, demerits, learning techniques, and types of supervised machine learning. You can also elucidate the benefits, limitations, and types of unsupervised machine learning. Similarly, cover important aspects related to reinforcement learning. Our AI PowerPoint slideshow also helps you in elaborating back propagation of neural networks. Walk your audience through the expert system in artificial intelligence. Cover examples, features, components, application, benefits, limitations, and other aspects of the expert system. Consolidate the deep learning process, recurrent neural networks, and convolutional neural networks through this PPT template deck. Give a crisp introduction to artificial intelligence. Introduce types, algorithms, trends, and use cases of artificial intelligence. Hit the download icon and begin instant personalization. Our Artificial Intelligence PowerPoint Presentation Slide Template Complete Deck are explicit and effective. They combine clarity and concise expression. https://bit.ly/3nfgjaT
AI, Machine Learning and Deep Learning - The OverviewSpotle.ai
The deck takes you into a fascinating journey of Artificial Intelligence, Machine Learning and Deep Learning, dissect how they are connected and in what way they differ. Supported by illustrative case studies, the deck is your ready reckoner on the fundamental concepts of AI, ML and DL.
Explore more videos, masterclasses with global experts, projects and quizzes on https://spotle.ai/learn
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 High Technology PowerPoint Presentation Slides Comple...SlideTeam
Artificial Intelligence High Technology PowerPoint Presentation Slides Complete Deck combines state-of-the-art design with insightful info. This PPT template deck helps you express every important aspect of machine intelligence. Showcase definition, types, use cases, trends, application, and present situation of AI. Employ our PowerPoint theme to elucidate the differences between AI, machine learning, and deep learning. Our PPT layout designers incorporate cutting-edge diagrams, and graphics to simplify complex data and season bland content. Illustrate the application, selection method, significance, function, use cases, challenges, and limitations of machine learning. Also, walk your audience through ML algorithms, decision tree algorithm learning, and differences between traditional programming and machine learning. Offer a complete overview of deep learning including the process, application, limitations, and significance through this comprehensive PowerPoint presentation. Highlight reinforcement learning, classification of neural networks, deep learning networks, feed-forward neural networks, recurrent neural networks, and convolutional neural networks. You will also find data on supervised and unsupervised machine learning, back propagation, and AI expert systems. Smash the download icon to personalize. Our Artificial Intelligence High Technology PowerPoint Presentation Slides Complete Deck are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro. https://bit.ly/3oaWpPz
Artificial intelligence
what is AI?
History
foundations of AI
Types of AI
Applications of AI
machine learning and applications
AI Vs Machine learning
Deep learning- advantages and disadvantages
Applications of Deep learning
Why is deep learning better than machine learning
Deep learning vs machine learning
Artificial Neural Network (ANN)
Architecture of ANN
Types of ANN
Applications of ANN
Softwares of ANN and their applications
Sippin: A Mobile Application Case Study presented at Techfest LouisvilleDawn Yankeelov
"Sippin: A Mobile Application Case Study," was presented at Techfest Louisville 2017 hosted by the Technology Association of Louisville Kentucky on Aug. 16th-17th.
Artificial Intelligence (A.I.) is a multidisciplinary field whose goal is to automate
activities that presently require human intelligence. Recent successes in A.I. include
computerized medical diagnosticians and systems that automatically customize
hardware to particular user requirements. The major problem areas addressed in A.I. can
be summarized as Perception, Manipulation, Reasoning, Communication, and Learning.
Perception is concerned with building models of the physical world from sensory input
(visual, audio, etc.). Manipulation is concerned with articulating appendages (e.g.,
mechanical arms, locomotion devices) in order to effect a desired state in the physical
world. Reasoning is concerned with higher level cognitive functions such as planning,
drawing inferential conclusions from a world model, diagnosing, designing, etc.
Communication treats the problem understanding and conveying information through
the use of language. Finally, Learning treats the problem of automatically improving
system performance over time based on the system's experience. Many important
technical concepts have arisen from A.I. that unify these diverse problem areas and that
form the foundation of the scientific discipline. Generally, A.I. systems function based
on a Knowledge Base of facts and rules that characterize the system's domain of
proficiency. The elements of a Knowledge Base consist of independently valid (or at
least plausible) chunks of information. The system must automatically organize and
utilize this information to solve the specific problems that it encounters. This
organization process can be generally characterized as a Search directed toward specific
goals. The search is made complex because of the need to determine the relevance of
information and because of the frequent occurrence of uncertain and ambiguous data.
Heuristics provide the A.I. system with a mechanism for focusing its attention and
controlling its searching processes. The necessarily adaptive organization of A.I.
systems yields the requirement for A.I. computational Architectures. All knowledge
utilized by the system must be represented within such an architecture. The acquisition
and encoding of real-world knowledge into A.I. architecture comprises the subfield of
Knowledge Engineering.
KEYWORDS – Artificial Intelligence, Machine Learning, Deep Learning, Encoding,
Subfield, Perception, Manipulation, Reasoning, Communication, and Learning.
Reinforcement Learning In AI Powerpoint Presentation Slide Templates Complete...SlideTeam
Showcase how machines are built to perform intelligent tasks by using our content-ready Reinforcement Learning In AI PowerPoint Presentation Slide Templates Complete Deck. Take advantage of these artificial intelligence PowerPoint visuals, and describe how machine learning models are trained to make sequences of decisions in a complex environment. Showcase the types of artificial intelligence such as deep learning, machine learning. Explain the concept of machine learning which delivers predictive models based on the data fed into machine learning algorithms. Take the assistance of our visually attention-grabbing reinforcement learning PowerPoint templates and discuss the effective uses of artificial intelligence in various areas such as supply chain, human resources, fraud detection, knowledge creation, research, and development, etc. You can also present the usage of AI in healthcare. This includes treatment, diagnosis, training and research, early detection, etc. Explain the working of machine learning by downloading our attention-grabbing supervised learning PowerPoint presentation. https://bit.ly/3kQBnEZ
What to learn during the 21 days Lockdown | EdurekaEdureka!
Register Here: https://resources.edureka.co/21-days-learning-plan-webinar/
In light of the complete national lockdown for 21 days, we invite you to join a FREE webinar by renowned Mentor and Advisor, Nitin Gupta as he helps you create a 21-day learning gameplan to maximize returns for your career.
The webinar will help freshers and experienced professionals to capitalize on these 21 days and figure out the best technologies to learn while confined to home.
You will also get all your questions and doubts resolved in real-time.
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Top 10 Dying Programming Languages in 2020 | EdurekaEdureka!
YouTube Link: https://youtu.be/LSM7hD6GM4M
Get Edureka Certified in Trending Programming Languages: https://www.edureka.co
In this highly competitive IT industry, everyone wants to learn programming languages that will keep them ahead of the game. But knowing what to learn so you gain the most out of your knowledge is a whole other ball game. So, we at Edureka have prepared a list of Top 10 Dying Programming Languages 2020 that will help you to make the right choice for your career. Meanwhile, if you ever wondered about which languages are slated for continuing uptake and possible greatness, we have a list for that, too.
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Top 5 Trending Business Intelligence Tools | EdurekaEdureka!
YouTube Link: https://youtu.be/eEwq_mPd1iI
Edureka BI Certification Training Courses: https://www.edureka.co/bi-and-visualization-certification-courses
Receiving insights and finding trends is absolutely critical for businesses to scale and adapt as the years go on. This is exactly what business intelligence does and the best thing about these software solutions is that their potential uses are practically unlimited.
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Tableau Tutorial for Data Science | EdurekaEdureka!
YouTube Link:https://youtu.be/ZHNdSKMluI0
Edureka Tableau Certification Training: https://www.edureka.co/tableau-certification-training
This Edureka's PPT on "Tableau for Data Science" will help you to utilize Tableau as a tool for Data Science, not only for engagement but also comprehension efficiency. Through this PPT, you will learn to gain the maximum amount of insight with the least amount of effort.
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Edureka Python Certification Training: https://www.edureka.co/data-science-python-certification-course
This Edureka PPT on 'Python Programming' will help you learn Python programming basics with the help of interesting hands-on implementations.
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Get Edureka Certified in Trending Project Management Certifications: https://www.edureka.co/project-management-and-methodologies-certification-courses
Whether you want to scale up your career or are trying to switch your career path, Project Management Certifications seems to be a perfect choice in either case. So, we at Edureka have prepared a list of Top 5 Project Management Certifications that you must check out in 2020 for a major career boost.
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Top Maven Interview Questions in 2020 | EdurekaEdureka!
YouTube Link: https://youtu.be/5iTcAR4fScM
**DevOps Certification Courses - https://www.edureka.co/devops-certification-training***
This video on 'Maven Interview Questions' discusses the most frequently asked Maven Interview Questions. This PPT will help give you a detailed explanation of the topics which will help you in acing the interviews.
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** Linux Administration Certification Training - https://www.edureka.co/linux-admin **
Linux Mint is the first operating system that people from Windows or Mac are drawn towards when they have to switch to Linux in their work environment. Linux Mint has been around since the year 2006 and has grown and matured into a very user-friendly OS. Do watch the PPT till the very end to see all the demonstrations.
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How to Deploy Java Web App in AWS| EdurekaEdureka!
YouTube Link:https://youtu.be/Ozc5Yu_IcaI
** Edureka AWS Architect Certification Training - https://www.edureka.co/aws-certification-training**
This Edureka PPT shows how to deploy a java web application in AWS using AWS Elastic Beanstalk. It also describes the advantages of using AWS for this purpose.
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*** Edureka Digital Marketing Course: https://www.edureka.co/post-graduate/digital-marketing-certification***
This Edureka PPT on "Top 10 Reasons to Learn Digital Marketing" will help you understand why you should take up Digital Marketing
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** RPA Training: https://www.edureka.co/robotic-process-automation-training**
This PPT on RPA in 2020 will provide a glimpse of the accomplishments and benefits provided by RPA. Also, it will list out the new changes and technologies that will collaborate with RPA in 2020.
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**DevOps Certification Courses - https://www.edureka.co/devops-certification-training **
This PPT shows how to configure Jenkins to receive email notifications. It also includes a demo that shows how to do it in 6 simple steps in the Windows machine.
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EA Algorithm in Machine Learning | EdurekaEdureka!
YouTube Link: https://youtu.be/DIADjJXrgps
** Machine Learning Certification Training: https://www.edureka.co/machine-learning-certification-training **
This Edureka PPT on 'EM Algorithm In Machine Learning' covers the EM algorithm along with the problem of latent variables in maximum likelihood and Gaussian mixture model.
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PGP in AI and Machine Learning (9 Months Online Program): https://www.edureka.co/post-graduate/machine-learning-and-ai
This Edureka PPT on "Cognitive AI" explains cognitive computing and how it helps in making better human decisions at work. Also, it explains the differences between cognitive computing and artificial intelligence.
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Edureka AWS Architect Certification Training - https://www.edureka.co/aws-certification-training
This Edureka PPT on AWS Cloud Practitioner will provide a complete guide to your AWS Cloud Practitioner Certification exam. It will explain the exam details, objectives, why you should get certified and also how AWS certification will help your career.
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Blue Prism Top Interview Questions | EdurekaEdureka!
YouTube Link: https://youtu.be/ykbRdUNIbyQ
** RPA Training: https://www.edureka.co/robotic-process-automation-certification-courses**
This PPT on Blue Prism Interview Questions will cover the Top 50 Blue Prism related questions asked in your interviews.
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AWS Architect Certification Training: https://www.edureka.co/aws-certification-training
This PPT will help you in understanding how AWS deals smartly with Big Data. It also shows how AWS can solve Big Data challenges with ease.
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A star algorithm | A* Algorithm in Artificial Intelligence | EdurekaEdureka!
YouTube Link: https://youtu.be/amlkE0g-YFU
** Artificial Intelligence and Deep Learning: https://www.edureka.co/ai-deep-learni... **
This Edureka PPT on 'A Star Algorithm' teaches you all about the A star Algorithm, the uses, advantages and disadvantages and much more. It also shows you how the algorithm can be implemented practically and has a comparison between the Dijkstra and itself.
Check out our playlist for more videos: http://bit.ly/2taym8X
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Kubernetes Installation on Ubuntu | EdurekaEdureka!
YouTube Link: https://youtu.be/UWg3ORRRF60
Kubernetes Certification: https://www.edureka.co/kubernetes-certification
This Edureka PPT will help you set up a Kubernetes cluster having 1 master and 1 node. The detailed step by step instructions is demonstrated in this PPT.
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DevOps Training: https://www.edureka.co/devops-certification-training
This Edureka DevOps Tutorial for Beginners talks about What is DevOps and how it works. You will learn about several DevOps tools (Git, Jenkins, Docker, Puppet, Ansible, Nagios) involved at different DevOps stages such as version control, continuous integration, continuous delivery, continuous deployment, continuous monitoring.
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Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
6. www.edureka.co/masters-program/machine-learning-engineer-training
Question 2
“Artificial intelligence (AI) is an area of computer science that emphasizes
the creation of intelligent machines that work and react like humans.”
“The capability of a machine to imitate the intelligent human behaviour.”
WhatisArtificial Intelligence?Give
anexampleofwhereAIisusedona
dailybasis.
Artificial Intelligence
Interview Questions
Machine Learning Engineer Masters Program
8. www.edureka.co/masters-program/machine-learning-engineer-training
Question 3
WhatarethedifferenttypesofAI?
Reactive Machines AI: Based on present actions, it cannot use previous
experiences to form current decisions and simultaneously update their memory.
Example: Deep Blue
Limited Memory AI: Used in self-driving cars. They detect the movement of
vehicles around them constantly and add it to their memory.
Theory of Mind AI: Advanced AI that has the ability to understand emotions,
people and other things in the real world.
Self Aware AI: AIs that posses human like consciousness and reactions. Such
machines have the ability to form self-driven actions.
Artificial Narrow Intelligence (ANI): General purpose AI, used in building virtual
assistants like Siri.
Artificial General Intelligence (AGI): Also known as strong AI. An example is the
Pillo robot that answers questions related to health.
Artificial Superhuman Intelligence (ASI): AI that possesses the ability to do
everything that a human can do and more. An example is the Alpha 2 which is the
first humanoid ASI robot.
Machine Learning Engineer Masters Program
Artificial Intelligence
Interview Questions
12. www.edureka.co/masters-program/machine-learning-engineer-training
Question 5
Machine Learning is a subset of Artificial Intelligence. It is the science of
getting computers to act by feeding them data and letting them learn a
few tricks on their own, without being explicitly programmed to do so.
HowisMachineLearningrelatedto
ArtificialIntelligence?
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A technique that
enables machines to
mimic human behaviour
Subset of AI which uses
data to enable machines
in solving problems
Machine Learning
Artificial Intelligence
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Question 7
The Q-learning is a Reinforcement Learning algorithm in which an agent
tries to learn the optimal policy from its past experiences with the
environment. The past experiences of an agent are a sequence of state-
action-rewards:
WhatisQ-Learning?
s0 a0 r1 s1
• s -> state
• a -> action
• r -> reward
Agent was in State s0 and did Action a0, which resulted in it
receiving Reward r1 and being in State s1
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Question 9
Explainhowdeeplearningworks.
• Deep Learning studies the basic unit of a brain called a brain cell or a
neuron. Inspired from a neuron, an artificial neuron or a perceptron
was developed.
• A biological neuron, has dendrites which is used to receive inputs.
• Similarly, a perceptron receives multiple inputs, applies various
transformations and functions and provides an output.
• Our brain consists of multiple connected neurons called neural
network, we can also have a network of artificial neurons called
perceptron's to form a Deep neural network.
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Question 10
Explainthecommonlyused
ArtificialNeuralNetworks.
Feedforward Neural Network
• Simplest form of ANN, where the data or the input travels in one
direction.
• The data passes through the input nodes and exit on the output
nodes. This neural network may or may not have the hidden layers.
Convolutional Neural Network
• Here, input features are taken in batch wise like a filter. This will help
the network to remember the images in parts and can compute the
operations.
• Mainly used for signal and image processing
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Question 10
Explainthecommonlyused
ArtificialNeuralNetworks.
Recurrent Neural Network(RNN) – Long Short Term Memory
• Works on the principle of saving the output of a layer and feeding
this back to the input to help in predicting the outcome of the layer.
• Here, you let the neural network to work on the front propagation
and remember what information it needs for later use
• This way each neuron will remember some information it had in the
previous time-step.
Autoencoders
• These are unsupervised learning models with an input layer, an
output layer and one or more hidden layers connecting them.
• The output layer has the same number of units as the input layer. Its
purpose is to reconstruct its own inputs.
• Typically for the purpose of dimensionality reduction and for
learning generative models of data.
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Question 13
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HowdoesReinforcementLearning
work?Explainwithanexample.
1. The RL Agent (Player1) collects state S⁰ from the environment
2. Based on the state S⁰, the RL agent takes an action A⁰, initially the
action is random
3. The environment is now in a new state S¹
4. RL agent now gets a reward R¹ from the environment
5. The RL loop goes on until the RL agent is dead or reaches the
destination
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ExplainMarkov’sdecisionprocess
withanexample.
The mathematical approach for mapping a solution in reinforcement
learning is called Markov Decision Process (MDP)
In this problem,
• Set of states are denoted by nodes i.e. {A, B, C, D}
• Action is to traverse from one node to another {A -> B, C -> D}
• Reward is the cost represented by each edge
• Policy is the path taken to reach the destination {A -> C -> D}
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Question 14
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Whatisthedifference between
Hyperparametersandmodel
parameters?
Model Parameters Hyperparameters
Model parameters are the
features of training data that will
learn on its own during training.
Model Hyperparameters are the
parameters that determine the
entire training process.
For example,
• Weights and Biases
• Split points in Decision Tree
For example,
• Learning Rate
• Hidden Layers
• Hidden Units
They are internal to the model
and their value can be estimated
from data.
They are external to the model
and their value cannot be
estimated from data.
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Question 18
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WhatarehyperparametersinDeep
NeuralNetworks?
• Hyperparameters are the variables which define the structure of the
network and the variables such as the learning rate, which define how
the network is trained.
• They determine the number of ideal hidden layers that must be
present in a network.
• More hidden units within a layer with regularization techniques can
increase the accuracy of the network, whereas lesser number of units
may cause underfitting.
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Hyperparameters
Question 19
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Explainthedifferentalgorithms
usedforhyperparameter
optimization.
Grid Search
Grid search trains the network for every combination by using the two set
of hyperparameters, learning rate and number of layers. Then evaluates
the model by using Cross Validation techniques.
Random Search
It randomly samples the search space and evaluates sets from a particular
probability distribution. For example, instead of checking all 10,000
samples, randomly selected 100 parameters can be checked.
Bayesian Optimization
This includes fine tuning the hyperparameters by enabling automated
model tuning. The model used for approximating the objective function is
called surrogate model (Gaussian Process). Bayesian Optimization uses
Gaussian Process (GP) function to get posterior functions to make
predictions based on prior functions.
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Question 20
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Howdoesdataoverfitting occur
andhowcanitbefixed?
Overfitting occurs when a statistical model or machine learning algorithm
captures the noise of the data. This causes an algorithm to show low bias but
high variance in the outcome.
How to Prevent Overfitting:
❑ Cross – validation
❑More training data
❑Remove features
❑Early stopping
❑Regularization
❑Use Ensemble models
Question 21
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Mentionatechniquethathelpsto
avoidoverfittinginaneural
network.
Dropout is a type of regularization technique used to avoid overfitting
in a neural network. It is a technique where randomly selected neurons
are dropped during training.
• Dropout value of approx 20%-50% of neurons with 20% providing a
good starting point. Too low value has minimal effect and a value
too high results in under-learning by the network.
• Dropout regularization used on a larger network, gives the model
more of an opportunity to learn independent representations.
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Question 22
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WhatisthepurposeofDeep
Learningframeworkssuchas
Keras,TensorFlowandPyTorch?
Keras is an open source neural network library written in
Python. It is designed to enable fast experimentation with
deep neural networks.
TensorFlow is an open-source software library for
dataflow programming. It is used for machine learning
applications like neural networks.
PyTorch is an open source machine learning library for
Python, based on Torch. It is used for applications such as
natural language processing
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Question 23
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Differentiate betweenNLPandText
mining
Text Mining Natural Language Processing
Aim of text mining is to extract
useful insights from structured &
un-structured text.
Aim of NLP is to understand
what is conveyed in speech.
Text Mining can be done using
text processing languages like
Perl, statistical models, etc.
NLP can be achieved using
advanced machine learning
models, deep neural networks,
etc.
Outcome:
• Frequency of words
• Patterns
• Correlations
Outcome:
• Semantic meaning of text
• Sentimental analysis
• Grammatical structure
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Question 24
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1. Fuzzification Module − It transforms the system inputs, which are crisp
numbers, into fuzzy sets.
2. Knowledge Base − It stores IF-THEN rules provided by experts.
3. Inference Engine − It simulates the human reasoning process by making
fuzzy inference on the inputs and IF-THEN rules.
4. Defuzzification Module − It transforms the fuzzy set obtained by the
inference engine into a crisp value.
ExplainFuzzyLogicarchitecture.
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Question 27
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Question 32
HowisGametheoryandAIrelated?
“In the context of artificial intelligence(AI) and deep learning
systems, game theory is essential to enable some of the key
capabilities required in multi-agent environments in which
different AI programs need to interact or compete in order to
accomplish a goal.”
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Question 33
WhatistheMinimaxAlgorithm?
Explaintheterminologies involved
inaMinimaxproblem.
Minimax is a recursive algorithm used to choose an
optimal move for a player assuming that the other
player is also playing optimally.
A game can be defined as a search problem with the following components:
• Game Tree: A tree structure containing all the possible moves.
• Initial state: The initial position of the board and showing whose move it is.
• Successor function: It defines the possible legal moves a player can make.
• Terminal state: It is the position of the board when the game ends.
• Utility function: It is a function which assigns a numeric value for the
outcome of a game.
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Question 35
Whichmethodisusedfor
optimizingaMinimaxbasedgame?
Alpha-beta Pruning
If we apply alpha-beta pruning to a standard minimax algorithm, it returns the
same move as the standard one, but it removes all the nodes that are possibly
not affecting the final decision.
In this case,
Minimax Decision = MAX{MIN{3,5,10}, MIN{2,a,b}, MIN{2,7,3}}
= MAX{3,c,2}
= 3
Hint: (MIN{2,a,b} would certainly be less than or equal to 2, i.e., c<=2 and
hence MAX{3,c,2} has to be 3.)
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Question 36
WhichalgorithmdoesFacebookuse
forfaceverificationandhowdoes
itwork?
DeepFace tool works on face verification algorithm, structured by Artificial
Intelligence (AI) techniques using neural network models.
Input: Scan a wild form of photos with large complex data
Process: In modern face recognition, the process completes in 4 raw steps:
• Detect
• Align
• Represent
• Classify
Output: Final result is a face representation, which is derived from a 9-layer
deep neural net
Training Data: More than 4 million facial images of more than 4000 people
Result: Facebook can detect whether the two images represent the same
person or not
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Question 38
HowcanAIbeusedindetecting
fraud?
Artificial Intelligence is used in Fraud detection problems by implementing
Machine Learning algorithms for detecting anomalies and studying hidden
patterns in data.
The following approach is followed for detecting fraudulent activities:
1. Data Extraction
2. Data Cleaning
3. Data Exploration & Analysis
4. Building a Machine Learning model
5. Model Evaluation
Data
Collection
Data
Cleaning
Exploration
& Analysis
Building a
Model
Model
Evaluation
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Question 44
Market basket analysis explains the combinations of products that frequently
co-occur in transactions.
Market Basket Analysis algorithms:
1. Association Rule Mining
2. Apriori
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Whatismarketbasketanalysisand
howcanArtificial Intelligencebe
usedtoperformthis?
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Question 44
• Association rule mining is a technique that shows how items are associated to
each other.
• Apriori algorithm uses frequent item sets to generate association rules. It is
based on the concept that a subset of a frequent itemset must also be a frequent
itemset.
A B
Example of Association rule:
➢ It means that if a person buys item A then he will also buy item B
Customer who purchase bread have a 60% likelihood of also
purchasing jam.
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Whatismarketbasketanalysisand
howcanArtificial Intelligencebe
usedtoperformthis?
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Question 45
ThecropyieldinIndiaisdegrading
becausefarmersareunableto
detectdiseasesincropsduringthe
earlystages.CanAIbeusedfor
diseasedetection incrops?Ifyes,
explain.
• Image Acquisition: The sample images are collected and stored as input
database.
• Image Pre-processing: Aim is to improve image data that suppress undesired
distortions as well as enhance specific image features.
Image enhancement
Colorspace conversion
• YCbCr
• L*a*b*
• Image Segmentation: The goal is to simplify and modify an image into
something that is easier to analyse. K- means clustering algorithm is used for
segmentation of the images.
• Feature Extraction: To extract the information that can be used to find the
significance of a given sample. The Gray-Level Co-Occurrence Matrix can be
used here.
• Classification: Linear Support Vector Algorithm is used for classification of leaf
disease. SVM is a binary classifier which uses a hyper plane called the decision
boundary between two classes
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