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
Machine Learning. What is machine learning. Normal computer vs ML. Types of Machine Learning. Some ML Object detection methods. Faster CNN, RCNN, YOLO, SSD. Real Life ML Applications. Best Programming Languages for ML. Difference Between Machine Learning And Artificial Intelligence. Advantages of Machine Learning. Disadvantages of Machine Learning
Artificial Intelligence Machine Learning Deep Learning Ppt Powerpoint Present...SlideTeam
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
Artificial Intelligence, Machine Learning and Deep LearningSujit Pal
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
Machine Learning. What is machine learning. Normal computer vs ML. Types of Machine Learning. Some ML Object detection methods. Faster CNN, RCNN, YOLO, SSD. Real Life ML Applications. Best Programming Languages for ML. Difference Between Machine Learning And Artificial Intelligence. Advantages of Machine Learning. Disadvantages of Machine Learning
Artificial Intelligence Machine Learning Deep Learning Ppt Powerpoint Present...SlideTeam
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
Artificial Intelligence, Machine Learning and Deep LearningSujit Pal
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
A PPT which gives a brief introduction on Machine Learning and on the products developed by using Machine Learning Algorithms in them. Gives the introduction by using content and also by using a few images in the slides as part of the explanation. It includes some examples of cool products like Google Cloud Platform, Cozmo (a tiny robot built by using Artificial Intelligence), IBM Watson and many more.
This is a deep learning presentation based on Deep Neural Network. It reviews the deep learning concept, related works and specific application areas.It describes a use case scenario of deep learning and highlights the current trends and research issues of deep learning
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
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.
What Is Machine Learning? | What Is Machine Learning And How Does It Work? | ...Simplilearn
This presentation on Machine Learning will help you understand what is Machine Learning, Artificial Intelligence vs Machine Learning vs Deep Learning, how does Machine Learning work, types of Machine Learning, Machine Learning pre-requisites and applications of Machine Learning. Machine learning is a core sub-area of artificial intelligence. Machine Learning is a technique which uses statistical methods enabling machines to learn from their past data. it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. While the concept of machine learning has been around for a long time, the ability to apply complex mathematical calculations to big data has been gaining momentum over the last several years. Now, let us get started and understand the concept of Machine Learning in detail.
Below topics are explained in this "What is Machine Learning?" presentation:
1. Machine Learning
- What is Machine Learning
2. Artificial intelligence vs Machine Learning vs Deep Learning
3. How does Machine Learning work?
4. Types of Machine Learning
5. Machine Learning pre-requisites
6. Applications of 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 modelling.
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 neighbours, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers
2. Information Architects
3. Analytics Professionals
4. Graduates
Learn more at https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course
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!
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. 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. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels 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. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
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. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
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
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 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
A PPT which gives a brief introduction on Machine Learning and on the products developed by using Machine Learning Algorithms in them. Gives the introduction by using content and also by using a few images in the slides as part of the explanation. It includes some examples of cool products like Google Cloud Platform, Cozmo (a tiny robot built by using Artificial Intelligence), IBM Watson and many more.
This is a deep learning presentation based on Deep Neural Network. It reviews the deep learning concept, related works and specific application areas.It describes a use case scenario of deep learning and highlights the current trends and research issues of deep learning
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
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.
What Is Machine Learning? | What Is Machine Learning And How Does It Work? | ...Simplilearn
This presentation on Machine Learning will help you understand what is Machine Learning, Artificial Intelligence vs Machine Learning vs Deep Learning, how does Machine Learning work, types of Machine Learning, Machine Learning pre-requisites and applications of Machine Learning. Machine learning is a core sub-area of artificial intelligence. Machine Learning is a technique which uses statistical methods enabling machines to learn from their past data. it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. While the concept of machine learning has been around for a long time, the ability to apply complex mathematical calculations to big data has been gaining momentum over the last several years. Now, let us get started and understand the concept of Machine Learning in detail.
Below topics are explained in this "What is Machine Learning?" presentation:
1. Machine Learning
- What is Machine Learning
2. Artificial intelligence vs Machine Learning vs Deep Learning
3. How does Machine Learning work?
4. Types of Machine Learning
5. Machine Learning pre-requisites
6. Applications of 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 modelling.
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 neighbours, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers
2. Information Architects
3. Analytics Professionals
4. Graduates
Learn more at https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course
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!
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. 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. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels 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. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
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. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
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
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 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 High Technology PowerPoint Presentation Slides Comple...SlideTeam
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Back Propagation Neural Network In AI PowerPoint Presentation Slide Templates...SlideTeam
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Reinforcement Learning In AI Powerpoint Presentation Slide Templates Complete...SlideTeam
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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
Artificial Intelligence Machine Learning Deep Learning PPT PowerPoint Present...SlideTeam
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Differences Between Machine Learning Ml Artificial Intelligence AI And Deep L...SlideTeam
Differences between Machine Learning ML Artificial Intelligence AI and Deep Learning DL is for the mid level managers to give information about what is AI, what is Machine Learning, what is deep learning, Machine learning process. You can also know the difference between Machine learning and Deep learning to understand AI, ML, and DL in a better way for business growth. https://bit.ly/2zFJeSC
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Introduction To Artificial Intelligence PowerPoint Presentation SlidesSlideTeam
Introduction to Artificial Intelligence is for the mid level managers giving information about what is AI, AI levels, types of AI, where AI is used. You can also know the difference between AI vs Machine learning vs Deep learning to understand expert system in a better way for business growth. https://bit.ly/3er7KWI
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This presentation gives you a broad overview of Artificial Intelligence. It explains briefly the technologies and concepts that fall under the domain of AI.
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After sales service encompasses all the assistance provided to customers after purchasing a product or service. Grab our informative PowerPoint presentation on Customer Support and Services Guide. It overviews after sales services, their benefits, needs, and best practices. Implementing an effective after sales support strategy can enhance customer satisfaction, promote brand loyalty, and even generate positive word of mouth advertising. Our customer services deck delves into various aftersales services, such as training, assurance, support, assistance, and rewards. Additionally, it highlights the positive impact of excellent after sales services and offers a set of dashboards to monitor customer retention and satisfaction. Moreover, our customer support PPT covers a range of services, including product guarantees, warranty, returns, replacements, configurations, installations, maintenance, and relocation. You can access our 100 percentage customizable template by downloading it, and its also compatible with Google Slides.
Word Of Mouth WOM Marketing Strategies To Build Brand Awareness Powerpoint Pr...SlideTeam
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Word of mouth marketing generates honest discussions about and recommendations for a product or company by getting people to discuss your brand, product, or business. Grab our Word of Mouth WOM Marketing Strategies to Build Brand Awareness template. It includes a brief overview of the concept and benefits of word of mouth marketing. Our WOM strategies deck contains significant industry statistics, types, and challenges. Additionally, it covers the need for and major types of word of mouth marketing strategies such as customer referral marketing, social media influencer marketing, brand ambassador marketing, affiliate marketing. It further incorporates the user generated content, employee advocacy, customer reviews and testimonials, buzz marketing, viral marketing, guerrilla marketing, and other strategies. Finally, the module highlights the impact, campaign budget, plan, and implementation framework for word of mouth marketing. Get to know more by downloading our 100 percentage editable and customizable template, which is also compatible with google slides.
Strategic Staff Engagement Action Plan Powerpoint Presentation SlidesSlideTeam
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Employee engagement refers to the level of involvement and enthusiasm employees have towards their work, which ultimately leads to better business outcomes. Grab Our Strategic staff engagement action plan can showcase the commitment level of employees in the workplace. It discusses the challenges and issues organizations face due to disengaged employees. Our Workforce participation deck includes an employee engagement survey to evaluate staff participation and satisfaction rates. Additionally, it provides industry statistics that demonstrate the importance of employee engagement. These resources can help companies develop an improved employee engagement action plan to achieve their goals and objectives. Further, Our Employee job satisfaction offers practical strategies to enhance productivity, retention, and participation rates. It further focuses on optimizing communication practices and employee work life balance, as well as implementing reward and recognition programs, compensation and benefits, and training and development opportunities. Finally, our HR Engagement Strategies module provides metrics to measure the impact of employee engagement activities. Get instant access.
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Traditional marketing uses offline media to reach, target, and attract a wider audience. Grab our insightfully designed template on Traditional Marketing Channel Analysis that provides an overview of offline marketing. It includes Traditional marketing techniques, that are more resilient and leave a stronger impression on the customers. It also has a positive impact on businesses as it helps to capture a broader range of audience. It includes key features, statistics, best practices, and benefits audience growth, reusable material, increased credibility, and brand exposure. Our conventional marketing deck further includes approaches that help organizations increase sales and attract customers, such as newspaper and magazine advertising, company brochures, and direct mail. The PowerPoint presentation also incorporates outdoor broadcasting, one on one, referral marketing, radio ads, billboards, transit advertising, sticker, and word of mouth marketing. Lastly, it highlights the critical marketing team players and the cost spent on every advertisement channel. Get access to this powerful template now.
Integrating Asset Tracking System To Enhance Operational Effectiveness Comple...SlideTeam
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An asset tracking system is a technique to monitor an organizations assets more efficiently to enhance customer service and improve equipment longevity. Grab our Integrating asset tracking system to enhance the operational effectiveness template. It is expertly crafted to keep track of purchases to strengthen security and IT safety. Our IT asset management deck provides the latest technology trends for keeping track of assets. It involves the current scenario of an organizations asset and inventory management systems. Additionally, our Inventory management system PPT offers essential methods of an asset tracking system such as RFID tags, barcode scanning, Bluetooth low energy, near-field communication, and more. It further showcases detailed working and key benefits of implementing these systems. Furthermore, our RFID integration module includes a workflow chart and the roles and responsibilities of the HR manager and team. It contains cost analysis and comparative software assessment for enhancing overall performance. Lastly, our Asset management template exhibits the KPI dashboards and impact analysis for measuring the effectiveness of implementing a tracking system. Get instant access.
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A leadership brand is a way to make others aware of your unique qualities as a leader and how you can make a positive impact. If youre interested, our Designing and Implementing Brand Leadership Strategy template is available for download. It provides an overview of brand leadership, including its benefits, essential factors, steps, and strategies for creating a strong leadership brand. Our Brand Identity deck includes tips for becoming a market leader, three leadership groups, and a leadership matrix. It also explores the relationship between value proposition and brand leadership. Moreover, our Brand Architecture PPT explains strategic brand leadership tasks such as organizational structure and process, brand architecture, and brand building function. Additionally, it includes dashboards for tracking brand awareness and performance. Finally, our Brand Building module discusses how brands can provide unique value to customers and consists of a case study on Coca Colas success as a market leader. The template is fully editable and customizable and can be used with Google Slides. Get access now.
How Amazon Generates Revenues Across Globe Powerpoint Presentation Slides Str...SlideTeam
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The primary objective of Amazons business strategy is to meet customer demands by providing an exceptional experience. Our professionally designed How Amazon Generates Revenues Across the Globe template presents an overview of Amazons marketing strategy, including partnerships, initiatives, marketing activities, and brand positioning. The deck focuses on gaining a competitive advantage by showcasing the progress Amazon has made over the years, such as their successful IPO investment, performance progress, and managing competition. The Amazon Business Strategy module shows the companys performance in managing SWOT analysis, BCG matrix, and VRIO analysis. The PPT also includes an assessment of Amazons marketing mix, advertising strategy, and business model to enhance revenue and market share. Additionally, the template presents the selling process across the platform, financial review, and future predictions. Finally, it showcases the critical executives associated with Amazons core team. Gain access to this powerful template now and unlock Amazons business strategy secrets.
Guide For Effective Event Marketing Powerpoint Presentation Slides Mkt CdSlideTeam
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Event marketing is a strategy used to create awareness of a companys products or services through direct customer engagement. Check out Our Guide for an Effective Event Marketing template. It has been carefully designed to help companies engage their target audience effectively. Our Social Media Advertising deck highlights the benefits, emerging trends, and statistics of event advertising while also addressing common challenges faced by marketers, such as audience engagement and converting pre registrants to attendees. Our Conference Marketing PPT covers virtual and physical events, including webinars, live streaming, tradeshows, and conferences. It also provides insights on social media platforms used for promotion and outlines critical steps in event marketing, including goal setting, management checklists, activity management, audience attraction, and performance evaluation. Lastly, our Audience Engagement module details the marketing team, impact, and advertising channel costs. Access our resources today and take your event marketing to the next level.
Managing Business Customers Technology Powerpoint Presentation SlidesSlideTeam
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Managed Services refers to outsourcing IT services to improve operational efficiency and reduce costs for an organization. The Managing Business Customers Technology PowerPoint presentation provides insights on pricing and growth strategies for these services, making it useful for IT and Chief Strategy managers looking to determine the right pricing model. The Capacity Services Management presentation includes an analysis of the global managed services market, competitive landscape, and pricing models such as Per User, Per Device, A La Carte, and Tiered Pricing. These models are compared based on scalability, cost, and technology, and the most suitable one is selected. The presentation also highlights the impact of implementing the pricing strategy and the key strategies for managed services growth, which can lead to increased sales, reduced operational costs, and increased market share. Gain access to this informative template now.
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The story of Apples brand emphasizes the importance of its identity in building its strength. View our professionally designed Unearthing Apples billion-dollar branding secret PowerPoint presentation. It showcases how Apple has established itself as an innovative market leader. Our market leader deck helps present Apples success as a market leader through its unique brand personality and clear vision. Apples primary goal is to develop ground-breaking solutions that positively impact peoples lives continually. Additionally, our Apple story PPT highlights Apples progress as a global leader with substantial brand value. Managing Apples brand performance involves various strategies such as advertising, product development, and marketing mix. Furthermore, it details the close relationship between Apple and its customers, including brand architecture, archetypes, and brand value assessment. Finally, our brand reputation module highlights the critical members associated with the brand and the role of Apples leaders in fostering an environment of consistent innovation. Get access to this powerful template now.
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Due to reduced job satisfaction, low employee productivity, and high-stress levels, many companies are transitioning to flexible work arrangements. Grab our Developing Flexible Working Practices to Improve Employee Engagement template. It can assist companies in increasing employee engagement rates and promoting workplace diversity. Our Flexible working hours deck addresses the issues caused by a rigid work schedule and presents statistics, benefits, trends, and growth drivers associated with hybrid work. Additionally, it provides guidelines and policies for the flexibility of work. Our Telecommuting PPT further showcases employee engagement strategies and productivity software tools for remote teams to achieve flexible work objectives. It also covers common issues managers face when implementing flexible work hours and ways to overcome them. Finally, our Job flexibility module exhibits the critical performance indicators KPIs and dashboards to help companies evaluate the impact of flexible employment strategies on employees and business performance. Download our 100 percent customizable template, also compatible with Google Slides.
Product Corporate And Umbrella Branding Powerpoint Presentation Slides Brandi...SlideTeam
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Grab Our meticulously crafted template titled Product, corporate and umbrella branding, a valuable resource for enhancing the overall value of your brand. It assists retailers in refining their product identity and differentiating them from competitors. Our Brand identity deck includes a brand portfolio and Kotlers five-level model for comparative product analysis. It also features tracking of customer service performance, budget analysis for product branding, and a product performance tracking dashboard. Additionally, our Corporate branding PPT emphasizes the significance of leveraging brand value, outlining its benefits and role. It provides insights into implementing corporate branding through critical elements, a deployment checklist, an activity tracking timeline, and significant steps for implementation. Furthermore, our Brand personality module includes a comprehensive overview of Umbrella branding, enhancing overall brand performance by improving brand equity. It also highlights the purpose, importance, advantages, and disadvantages associated with umbrella branding. Finally, the umbrella branding performance is managed through budget assessment for brand maintenance and an activity tracking dashboard for the umbrella brand. Access this powerful template now to augment your brand management strategies.
Communication Channels And Strategies For Shareholder Engagement Powerpoint P...SlideTeam
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A shareholder engagement strategy can help an organization develop and strengthen its relationship with investors. Grab our Communication Channels and Strategies for Shareholder Engagement template that helps to communicate about the latest updates and strategies implemented by the company to shareholders for building trust. It starts by highlighting benefits, concerns, and topics for shareholder engagement. Our Investor relations deck also shows the different types of companies for investor engagement through various communication techniques. Additionally, it highlights planning techniques like formulating a governance committee and communication calendar. Further, our Investor engagement PPT communication channels like website, email, and social media for shareholder engagement. It also showcases various shareholder strategies for websites like sharing annual reports, posting SEC filings, proxy statements, governance roadshows, and podcasts. Our Corporate strategy module exhibits an email channel to communicate critical updates and techniques to shareholders. Lastly, it shows various social media channels, like Facebook, YouTube, etc., for communicating with shareholders and covers other strategies like meetings, sharing ESG practices, and conducting investors survey for shareholder engagement. Get instant access.
Responsible Tech Playbook To Leverage Business Ethics Powerpoint Presentation...SlideTeam
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Grab our completely designed, Responsible tech playbook to leverage business ethics template. It provides a comprehensive guide to cater tools, methods, and frameworks to help firms make better technological decisions. Our Technological transformation deck helps mitigate software and product development risks and helps manage how technology interacts with society or the community. The playbook covers summaries of various technologies utilized. Each synopsis incorporates slides outlining an overview of the technique in terms of goal, aim, duration, and stakeholders involved. Additionally, our Technology governance PPT highlights the Primary methods, including agile threat modeling, data ethics canvas, consequence scanning, unintended and intended consequence scanning, and ethical OS. It also contains FMEA, interpret ML, materiality matrix assessment, tarot cards of tech, responsibility strategy, and humane design guide. Lastly, our Business ethics module exhibits the different approaches associated with responsible technology, including polarity thinking, participatory design, speculative design provocations, and bracketing. Get immediate access.
Generating Leads Through Targeted Digital Marketing Campaign Powerpoint Prese...SlideTeam
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Personalized marketing help organization run targeted promotional campaigns and enhance the customer experience. The following presentation Generating Leads through Targeted Digital Marketing Campaign can help the marketing department to improve brand perception and increase the loyalty of customers towards the company. It initially showcases the collection and analysis of data for preparing ideal customer personas. This PPT also highlights personalized email and video marketing strategies that can help to target an online audience and increase the conversion rate. It also shows website optimization tactics that can result in high-converting landing pages and shorter sales cycles. It also shows various other personalized strategies which are social media and mobile application marketing. This presentation also showcases a checklist that can help to manage and ensure the effective implementation of personalized marketing campaigns. It also highlights the budget that can be allocated to marketing activities for customer targeting. In the end, this presentation shows KPIs and dashboards that can help to track and evaluate the effectiveness of personalized customer marketing campaigns. Download it now.
Boosting Profits With New And Effective Sales Strategic Plan Powerpoint Prese...SlideTeam
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A sales plan is the first and foremost step towards developing a sales strategy, enabling a companys team to predict and avoid business related risks. Grab our Boosting Profits with a New and Effective Sales Strategic Plan template. It formulates the right target audience, appropriate sales representatives, software tools, revenue goals, and methods to track performance. Our Sales Strategy deck provides the company overview, products offered, value proposition, target market analysis, etc. It focuses on understanding the need for a new strategic sales plan. Our Brand Promotion PPT showcases multiple steps of building a sales strategy, like setting targets, creating customer profiles, lead generation strategies, etc. Additionally, it includes various stages of the sales process, namely, prospecting and lead generation, setting customer qualification criteria, scheduling product demos, defining customer needs, etc. It also caters to slides about multiple sales assessment techniques. Further, our Promotional Campaigns module includes campaigns like using referrals, loyalty programs, buy more and save more programs, device trade ins, etc. Lastly, it caters to the cost analysis, impact assessment of multiple sales activities, and dashboards related to tracking improvements with implementing new sales plans. Get access now.
ChatGPT IT Powerpoint Presentation SlidesSlideTeam
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Chat GPT multitasks by picking up on and naturally expressing more than one aim at a time. Grab our professionally curated ChatGPT IT template. It includes an introduction, advantages, and features of OpenAIs ChatGPT model and discusses the price and availability of its enhanced version, called ChatGPT Pro. Our ChatGPT deck represents the working and architecture of ChatGPT technology, including a large language model and self-attention mechanism. Additionally, it demonstrates the various applications of ChatGPT in several domains, such as education, medicine, research, information technology, advertisement, banking, finance, etc. Our chatbot using GPT-3 PowerPoint presentation represents the three reinforcement learning from human feedback models supervised fine-tuning, reward, and reinforcement learning. Furthermore, it discusses integrating the ChatGPT model into web applications and best practices for successful deployment. Moreover, our ChatGPT integration into the web applications module contains sections about the impact of ChatGPT on social media and artificial intelligence tokens. Lastly, this chatbot using GPT-3 deck comprises a roadmap, a timeline, a 30-60-90 days plan, a checklist to integrate OpenAIs GPT-3 model into web applications and a case study on mental health and ChatGPT collaboration. Get access now.
Methods To Implement Traditional Marketing Powerpoint Presentation Slides Mkt CdSlideTeam
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Our expertly crafted template on Methods to Implement Traditional Marketing provides a comprehensive overview of offline marketing strategies that are highly effective in reaching and engaging a wider audience. These traditional marketing methods are not only resilient but also leave a lasting impression on customers, providing numerous benefits such as audience growth, increased credibility, and brand exposure. The template highlights key features, statistics, and best practices for implementing these strategies, including newspaper and magazine advertising, company brochures, direct mail, outdoor broadcasting, referral marketing, radio ads, billboards, transit advertising, sticker marketing, and word of mouth marketing. Additionally, the presentation includes information on the critical team players involved in the marketing process and the costs associated with each advertising channel. By utilizing these tried and true techniques, businesses can increase sales, attract customers, and capture a broader audience. Dont miss out on this powerful tool access our template today.
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Sports marketing involves promoting goods and services through sporting events, endorsement of athletes and teams, etc. Grab our Comprehensive guide to sports marketing strategy template. Its primary purpose is to promote sporting goods and equipment through multiple channels to improve the overall lifestyle of customers and sporting fans. Our Sports brand advertising deck provides a general overview of sports marketing along with processes, challenges, trends, etc. It offers access to multiple strategies that can help sports retailers increase customer experience, club sponsorships, and overall brand recall these include athlete branding and digital sports brand promotion. Additionally, our Sports marketing programs PPT contains slides that can guide sports brands on utilizing the eSports industry as an opportunity to promote their products. It further helps the firms to sponsor famous eSports teams, host related events, and deploy advergaming for immersive customer experience. Furthermore, our Athlete branding module offers content that can guide marketers to track sports advertising campaigns. Lastly, it can help assess the performance of the above strategies through impact analysis, KPIs, and dashboards. Get access now.
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With decreased job satisfaction, poor employee productivity, and a high stress level, many companies are shifting to flexible work arrangements. Remote working gives an employee the freedom to decide when they start working, where they work, and for how long. With our professionally designed Global Shift Towards Flexible Working PowerPoint presentation, corporates can successfully improve employee engagement rate and improve workplace diversity. Initially, the PPT begins with multiple issues faced by the company because of its inflexible working schedule. Secondly, the complete deck caters to a section through which professionals can highlight major statistics, advantages, trends, and growth drivers associated with hybrid working. Going forward, the firm can achieve flexible working objectives by developing guidelines, policies, employee engagement strategies, and productivity software tools for remote teams. In addition to this, the module covers a section on different issues faced by managers when implementing flexible working hours and the ways in which businesses can overcome them. At last, through key performance indicators KPIs metrics and dashboards, organizations can successfully evaluate the impact of flexible employment strategies on employees and the companys performance. Download our 100 percentage editable and customizable template, which is also compatible with Google Slides.
This presentation by Morris Kleiner (University of Minnesota), was made during the discussion “Competition and Regulation in Professions and Occupations” held at the Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found out at oe.cd/crps.
This presentation was uploaded with the author’s consent.
Acorn Recovery: Restore IT infra within minutesIP ServerOne
Introducing Acorn Recovery as a Service, a simple, fast, and secure managed disaster recovery (DRaaS) by IP ServerOne. A DR solution that helps restore your IT infra within minutes.
Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...Orkestra
UIIN Conference, Madrid, 27-29 May 2024
James Wilson, Orkestra and Deusto Business School
Emily Wise, Lund University
Madeline Smith, The Glasgow School of Art
This presentation, created by Syed Faiz ul Hassan, explores the profound influence of media on public perception and behavior. It delves into the evolution of media from oral traditions to modern digital and social media platforms. Key topics include the role of media in information propagation, socialization, crisis awareness, globalization, and education. The presentation also examines media influence through agenda setting, propaganda, and manipulative techniques used by advertisers and marketers. Furthermore, it highlights the impact of surveillance enabled by media technologies on personal behavior and preferences. Through this comprehensive overview, the presentation aims to shed light on how media shapes collective consciousness and public opinion.
0x01 - Newton's Third Law: Static vs. Dynamic AbusersOWASP Beja
f you offer a service on the web, odds are that someone will abuse it. Be it an API, a SaaS, a PaaS, or even a static website, someone somewhere will try to figure out a way to use it to their own needs. In this talk we'll compare measures that are effective against static attackers and how to battle a dynamic attacker who adapts to your counter-measures.
About the Speaker
===============
Diogo Sousa, Engineering Manager @ Canonical
An opinionated individual with an interest in cryptography and its intersection with secure software development.
Have you ever wondered how search works while visiting an e-commerce site, internal website, or searching through other types of online resources? Look no further than this informative session on the ways that taxonomies help end-users navigate the internet! Hear from taxonomists and other information professionals who have first-hand experience creating and working with taxonomies that aid in navigation, search, and discovery across a range of disciplines.
2. Table of Content
2
03. Deep Learning
✓ What is Deep Learning?
✓ Deep learning Process
✓ Classification of Neural Networks
✓ Types of Deep Learning Networks
✓ Feed-forward neural networks
✓ Recurrent neural networks (RNNs)
✓ Convolutional neural networks (CNN)
✓ Reinforcement Learning
✓ Examples of deep learning applications
✓ Why is Deep Learning Important?
✓ Limitations of deep learning
02. Machine Learning
✓ What is Machine Learning?
✓ 7 Steps of Machine learning
✓ Machine Learning vs. Traditional Programming
✓ How does machine learning work?
✓ Machine learning Algorithms
✓ Machine learning use cases
✓ How to choose Machine Learning Algorithm
✓ Why to use decision tree algorithm learning
✓ Challenges and Limitations of Machine learning
✓ Application of Machine learning
✓ Why is machine learning important?
04. Difference between AI vs ML vs DL
✓ What is AI?
✓ What is ML?
✓ What is Deep Learning?
✓ Machine Learning Process
✓ Deep Learning Process
✓ Difference between Machine Learning and Deep Learning
✓ Which is better to start AI,ML or Deep learning
01. Introduction
✓ What is AI?
✓ Introduction to AI Levels?
✓ Types of Artificial Intelligence
✓ AI VS machine learning vs deep learning
✓ Where is AI used?
✓ AI use cases
✓ Why is AI booming now?
✓ AI trend in 2020
3. Table of Content
3
Supervised Machine Learning
✓ Types of Machine Learning
✓ What is Supervised Machine Learning?
✓ How Supervised Learning Works
✓ Types of Supervised Machine Learning Algorithms
✓ Supervised vs. Unsupervised Machine learning techniques
✓ Advantages of Supervised Learning
✓ Disadvantages of Supervised Learning
Unsupervised Machine Learning
✓ What is Unsupervised Learning?
✓ How Unsupervised Machine Learning works
✓ Types of Unsupervised Learning
✓ Disadvantages of Unsupervised Learning
Reinforcement Learning
✓ What is reinforcement learning?
✓ How reinforcement learning works
✓ Types of reinforcement learning
✓ Advantage of reinforcement learning
✓ Disadvantage of reinforcement learning
Back Propagation Neural Network in AI
✓ Back Propagation Neural Network in AI
✓ What is Artificial Neural Networks?
✓ What is Backpropagation?
✓ Why We Need Backpropagation?
✓ What is a Feed Forward Network?
✓ Types of Backpropagation Networks
✓ Best practice Backpropagation
Expert System in Artificial Intelligence
✓ What is an Expert System?
✓ Examples of Expert Systems
✓ Characteristic of Expert System
✓ Components of the expert system
✓ Conventional System vs. Expert system
✓ Human expert vs. expert system
✓ Benefits of expert systems
✓ Limitations of the expert system
✓ Applications of expert systems
4. 4
Introduction
✓ What is AI?
✓ Introduction to AI Levels?
✓ Types of Artificial Intelligence
✓ AI VS machine learning vs deep learning
✓ Where is AI used?
✓ AI use cases
✓ Why is AI booming now?
✓ AI trend in 2020
01
5. Artificial Intelligence
Transforming the Nature of Work, Learning, and Learning to Work
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.
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Artificial Intelligence
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audience's attention.
Machine Learning
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Deep Learning
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6. Introduction to AI Levels?
6
Types of
Artificial
Intelligence
Artificial Narrow
Intelligence
Artificial Super
Intelligence
Artificial General
Intelligence
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7. Types of Artificial Intelligence
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Deep Learning
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Machine Learning
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Artificial Intelligence
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8. Artificial Intelligence
8
2016 2017 2018 2019 2020
2020 2019 2018
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35%
2018
2019
2020 65%
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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.
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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Adapt it to your needs and
capture your audience's
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Information
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10. Deep Learning
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&
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
Deep Learning is a subfield of
machine learning that is concerned
with algorithms inspired by the brain's
structure
11. AI VS Machine Learning VS Deep Learning
11
Artificial Intelligence
✓ Artificial Intelligence originated around
1950s
✓ AI represents simulate intelligence in
machines
✓ AI is a subset of data science
✓ Aim is to build machines which are
capable of thinking like humans
Machine Learning
✓ Machine Learning originated around
1960s
✓ Machine learning is the practice of
getting machines to make decisions
without being programmed
✓ Machine learning is a subset of AI &
Data Science
✓ Aim is to make machines learn through
data so that they can solve problems
Deep Learning
✓ Deep Learning originated around 1970s
✓ Deep Learning is the process of using
artificial neural networks to solve
complex problems
✓ Deep Learning is a subset of Machine
Learning, AI & Data Science
✓ Aim is to build neural networks that
au6tonetically discover patterns for
feature detection
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12. Where is AI used?
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Real-time
Operations Management
Customer Services
Risk Management
& Analytics
Customer Insight
Pricing & Promotion
Predictive Analytics
Customer Experience
Supply Chain
Knowledge Creation
Research & Development
Fraud Detection
Human Resources
13. AI Usecase in HealthCare
13
AI and
Robotics
Training
Research Early Detection
Keeping Well
End of Life Care
Treatment Decision Making
Diagnosis
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14. AI Use Cases in Human Resource
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Recruiting
✓ Dynamic Career Sites
✓ Smart Sourcing
Onboarding
✓ Automated Messages
✓ Curated Videos
Learning
✓ Curated Training
✓ Skill Development
Engagement
✓ HR Chatbot
✓ Engagement Surveys
15. AI in Banking for Fraud Detection
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Cardholder
Profiles
Postings
Payment System
Nonmonetary System
Analyst
Workstation
Payment and
Non-Monetary Transactions
8
Authorization System
Rules Definition
Configuration
Workstation
Case
Management
Database
Case Creation Module
Neural
Network Engine
Scoring Engine
Expert Authorization
Response Module
Expert Rules Base6
Case Creation Rules
Execute
Auth Request1
Expert Rules Execute3
Auth Recommendation4
Auth Request & Score22
Transaction & Score5
Case Information7
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
Digital Ecosystem Data Lake
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
Procurement Manufacturing Customers ServiceLogistics
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
Healthbots
Bring all of the above together
for
healthcare use cases
Artificial Intelligence
Self Learning machines
becomes smarter
the more they are used
Messenger Apps
Lets users chat anywhere,
anytime
App Eco-system
Lets users download and
use apps easily
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18. Why is AI booming now?
18
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
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
Global AI Revenue Forecast by 2025, Ranked by Use Case in millions US Dollar
Penetration of Artificial Intelligence Skills, by Country Organizations deploying AI, by Functional Areas
United
States
100% 92%
China
84%
India
54%
Israel
45%
Germany
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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. Machine Learning
02
✓ What is Machine Learning?
✓ 7 Steps of Machine learning
✓ Machine Learning vs. Traditional Programming
✓ How does machine learning work?
✓ Machine learning Algorithms
✓ Machine learning use cases
✓ How to choose Machine Learning Algorithm
✓ Why to use decision tree algorithm learning
✓ Challenges and Limitations of Machine learning
✓ Application of Machine learning
✓ Why is machine learning important?
20
21. Machine Learning
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Traditional
Programming
Data
(Input)
Program
Output
Data
(Input)
Output
Machine
Learning
Program
Machine Learning is the result of General AI that involves developing machines that can
deliver results better than humans
Input Data:
Feed Learner
Various Data
Output Data:
Present Rules
“Learning”
Machine
Learning System
22. 7 Steps of Machine Learning
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01
02
03
04
05
06
07
Choosing a ModelTraining
Preparing that DataEvaluation
Prediction
Gathering DataHyperparameter Tuning
23. Machine Learning vs. Traditional Programming
23
Prediction
ResultComputer
Data
Handcrafted
ModelLearning
Model
Prediction
Result
New Data
Model
Sample Data
Expected
Result
Computer
Computer
Traditional Modelling
Machine Learning
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24. How does Machine Learning Work?
24
Collect data from hospitals,
health insurance companies,
social service agencies,
police and fire dept.
Collect Data
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
Depend on the problem to be
solved and the type of data
an appropriate algorithm will
be chosen.
Select Algorithm
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
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
✓ KNN
✓ Trees
✓ Logistic Regression
✓ Naïve-Bayes
✓ SVM
Reinforcement
Categorical
Machine Learning
✓ Apriori
✓ FP-Growth
Association
Anlaysis
Hidden
Markov Model
✓ SVD
✓ PCA
✓ K-means
Clustering
Unsupervised
✓ Linear
✓ Polynomial
Regression
Decision Tree
Classification
Supervised
Random Forest
Continuous
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26. Machine Learning Use Cases
26
Energy Feedstock
& Utilities
✓ Power Usage Analytics
✓ Seismic Data Processing
✓ Your Text Here
✓ Smart Grid Management
✓ Energy Demand & Supply
Optimization
Financial
Services
✓ Risk Analytics & Regulation
✓ Customer Segmentation
✓ Your Text Here
✓ Credit Worthiness Evaluation
Travel
& Hospitality
✓ Aircraft Scheduling
✓ Dynamic Pricing
✓ Your Text Here
✓ Traffic Patterns &
Congestion Management
Manufacturing
✓ Predictive Maintenance
or Condition Monitoring
✓ Your Text Here
✓ Demand Forecasting
✓ Process Optimization
✓ Telematics
Retail
✓ Predictive Inventory
Planning
✓ Recommendation
Engines
✓ Your Text Here
✓ Customer ROI & Lifetime
Value
Healthcare &
Life Sciences
✓ Alerts & Diagnostics from
Real-time Patient Data
✓ Your Text Here
✓ Predictive Health
Management
✓ Healthcare Provider
Sentiment Analysis
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27. How to Choose Machine Learning Algorithm
27
What do you want to do
with your Data?
Algorithm Cheat Sheet
Additional Requirements Accuracy Linearity Number of
Parameters
Training Time Number of
Features
How to Select Machine Learning Algorithms
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28. Why use Decision Tree Machine Learning Algorithm?
28This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
To Classify
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
Use Standard
Classification here
Use c4.5
Implementation
To Predict
Responsible variable
is Continuous
Decision Trees
Response Variable has
Multiple Categories
29. Challenges and Limitations of Machine learning
29
Advantages
Easily Identifies Trends and Patterns
No Human Intervention needed
Handling multi-dimensional
& multi-variety Data
Continuous Improvement
Wide Applications
Data
Acquisition
Time and
Resources
High error-
Susceptibility
Interpretation
Results
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Disadvantages
30. Application of Machine Learning
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Automatic Language
Translation
Medical Diagnosis
Stock Market Trading
Online Fraud Detection
Virtual Personal Assistant
Email Spam and Malware FilteringSelf Driving Cars
Product Recommendations
Traffic Prediction
Speech Recognition
Image Recognition
31. Why is Machine Learning Important?
31
Phase 1 : Learning
Training Data
Phase 2: Prediction
✓ Precision/recall
✓ Over fitting
✓ Test/cross Validation
data, etc.
Error Analysis
✓ Normalization
✓ Dimension Reduction
✓ Image Processing, etc.
Pre-Processing
✓ Supervised
✓ Unsupervised
✓ Minimization, etc.
Learning
Predicted DataPrediction
New Data
Model
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32. Deep Learning
03
✓ What is Deep Learning?
✓ Deep learning Process
✓ Classification of Neural Networks
✓ Types of Deep Learning Networks
✓ Feed-forward neural networks
✓ Recurrent neural networks (RNNs)
✓ Convolutional neural networks (CNN)
✓ Reinforcement Learning
✓ Examples of deep learning applications
✓ Why is Deep Learning Important?
✓ Limitations of deep learning
32
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.
Car
Not Car
OutputInput Feature Extraction
+ Classification
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What is Deep Learning?
34. Deep Learning Process
34
Understand
the Problem
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needs and capture your
audience's attention.
Identify
Data
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Select Deep Learning
Algorithms
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Training
the Model
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Test the
Model
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35. Classification of Neural Networks
35
Input Layer Hidden Layer Output Layer
x1
x2
xn
v11
v12
vpn
w11
w22
wmp ym
y2
y1
V1n
1
2
p
w1p
1
2
m
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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
Deep Learning
Models
Supervised
✓ Artificial Neural Networks (ANN)
✓ Convolutional Neural Networks
(CNN)
✓ Recurrent Neural Networks (RNN)
Unsupervised
✓ Self Organizing Maps (SOM)
✓ Boltzmann Machines (BM)
✓ AutoEncoders (AE)
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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
State, Reward
Action
Agent Environment
Exploration Policy Neural Networks Filters
Memory Algorithm
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.
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41. Examples of Deep Learning Applications
41
Image
Recognition
Natural Language
Processing
Portfolio Management &
Prediction of Stock Price
Movements
Drug Discovery & Better
Diagnostics of Diseases
in Healthcare
Speech
Recognition
Robots and Self -
Driving Cars
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42. Why is Deep Learning Important?
42This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
Performance
Data
Deep Learning
Other Learning
Algorithms
43. Limitations of Deep Learning
43
Limitations of
Deep Learning
Amount of
Data
Statistical
Reasoning
Interpretability
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44. Difference between
AI vs ML vs DL
04
✓ What is AI?
✓ What is ML?
✓ What is Deep Learning?
✓ Machine Learning Process
✓ Deep Learning Process
✓ Difference between Machine Learning and Deep Learning
✓ Which is better to start AI,ML or Deep learning
44
45. Difference between AI vs ML vs DL
45
Machine
Learning
Ability to learn without
being explicitly
programmed
Deep
Learning
Learning based on
deep neural network
Artificial
Intelligence
Engineering of making
intelligent machines
and programs
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46. What is AI?
46This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
Artificial
Intelligence
Artificial
Intelligence
With rapid advancements in deep
learning and machine learning, the tech
industry is transforming radically.
(AI) is a popular branch of computer
science that concerns with building
“intelligent” smart machines capable of
performing intelligent tasks.
47. What is ML?
47
Learns
Predicts
Improves
Machine
Learning
Ordinary
System
With
AI
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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Introduction to Machine learning
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. Machine Learning Process
49
Data
Raw &
Training Data
Modelling
Candidate
& Final
Visualisation
Predictions
& Strategy
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Data
Gathering
Data
Cleaning
Selecting
Right Algorithms
Building
Model & Finalising
Data Transformation
into Predictions
50. Deep Learning Process
50
Test the
Model
Understand
the Problem
Identify Data
Select Deep Learning
Algorithm
Training the
Model
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51. Difference between Machine Learning and Deep Learning
51This slide is 100% editable. Adapt it to your needs and capture your audience's attention.
Input Feature
Extraction
Classifica
-tion
Output
Car
Not Car
Input Feature Extraction +
Classification
Output
Car
Not Car
52. Which is better to start AI,ML or DL?
52
Any Technique which enables computers to mimic
human behavior.
Artificial Intelligence
Subset of AI Techniques which use Statistical Methods to
Enable Machines to Improve with Experiences.
Machine Learning
Subset of ML which make the Computation of Multi-layer
Neural Networks Feasible.
Deep Learning
01
02
03
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53. Supervised
Machine Learning
05
✓ Types of Machine Learning
✓ What is Supervised Machine Learning?
✓ How Supervised Learning Works
✓ Types of Supervised Machine Learning Algorithms
✓ Supervised vs. Unsupervised Machine learning techniques
✓ Advantages of Supervised Learning:
✓ Disadvantages of Supervised Learning
53
54. Types of Machine Learning
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Inputs
Training
Inputs
Makes Machine Learn
Explicitly
Data with Clearly defined
Output is given
Direct feedback is given
Predicts outcome/future
Resolves Classification and
Regression Problems
Supervised Learning Unsupervised Learning
Machine Understands the data
(Identifies Patterns/ Structures)
Evaluation is Qualitative or
Indirect
Does not Predict/Find
anything Specific
Reinforcement Learning
An approach to AI
Reward Based Learning
Learning form +ve & +ve
Reinforcement
Machine Learns how to act in
a Certain Environment
To Maximize Rewards
Inputs
Rewards
Outputs Outputs Outputs
55. What is Supervised Machine Learning?
55
Input Raw Data Output
ProcessingAlgorithm
Training
Data set
Desired
Output
Supervisor
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Supervised Learning
56. How Supervised Machine Learning works
56
Classification
Sorting Items into Categories
Regression
Identifying Real Values
(Dollars, Weight, etc.)
Machine
Label
“Group 1”
Step1
Provide the Machine Learning Algorithm
Categorized or “labeled” Input and Output
Data from to Learn
Machine
Group 1
Group 2
Step2
Feed the Machine New, Unlabeled Information to
See if it Tags New Data Appropriately. If not,
Continue Refining the Algorithm
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Types of Problems to which it’s Suited
57. Types of Supervised Machine Learning Algorithms
57
Classification
✓ Fraud Detection
✓ Email Spam Detection
✓ Diagnostics
✓ Image Classification.
Regression
✓ Risk Assessment
✓ Score Prediction
Supervised
Learning
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58. Supervised vs. Unsupervised Machine Learning Techniques
58
VS
Supervised Learning
✓ Classification
✓ Regression
Input & Output Data
Predictions &
Predictive Models
Unsupervised Learning
✓ Clustering
✓ Association
Input Data
Patterns / Structure
Discovery
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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 of Supervised Learning
59
Advantages
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60. Disadvantages of Supervised Learning
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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.
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.
It is needed tons of computation time for training.
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.
61. Unsupervised
Machine Learning
06
✓ What is Unsupervised Learning?
✓ How Unsupervised Machine Learning works
✓ Types of Unsupervised Learning
✓ Disadvantages of Unsupervised Learning
61
62. What is Unsupervised Learning?
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Input Raw Data OutputAlgorithm
Interpretation Processing
✓ Unknown output
✓ No Training Data Set
Unsupervised Learning
63. How Unsupervised Machine Learning works
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Machine
Step1
Provide the machine learning
algorithm uncategorized, unlabeled
input data to see what patterns it finds
Similar Group 1
Similar Group 2
Machine
Step2
Observe and learn from the patterns the
machine identifies
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?
64. Types of Unsupervised Learning
64
Dimensionality
Reduction
✓ Text Mining
✓ Face Recognition
✓ Big Data Visualization
✓ Image Recognition
Clustering
✓ Biology
✓ City Planning
✓ 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. Reinforcement learning
07
✓ What is Unsupervised Learning?
✓ How Unsupervised Machine Learning works
✓ Types of Unsupervised Learning
✓ Disadvantages of Unsupervised Learning
66
67. What is Reinforcement Learning?
67
Reinforced
Response
Input
Input
Response
It’s a
mango
Feedback
Wrong!
It’s an apple
Learns
Noted
It’s an
Apple
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68. How Reinforcement Learning Works?
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Input Raw Data
Reward
State
Selection of
Algorithm
Best Action
Environment
Agent
Output
Reinforcement Learning
69. Types of Reinforcement Learning
69
Gaming
Finance Sector
Inventory Management
Robot Navigation
Manufacturing
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70. Disadvantage of Reinforcement Learning
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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
Reinforcement learning as a framework is wrong in many various ways, but it's precisely this quality that creates it useful.
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.
71. Back Propagation
Neural Network in AI
08
✓ Back Propagation Neural Network in AI
✓ What is Artificial Neural Networks?
✓ What is Backpropagation?
✓ Why We Need Backpropagation?
✓ What is a Feed Forward Network?
✓ Types of Backpropagation Networks
✓ Best practice Backpropagation
71
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
Mostprominentadvantagesof
BackpropagationAre: Backpropagation is fast, simple and
straightforward to program.
It has no parameters to tune aside from the
numbers of input.
It is a typical method that generally works well.
It doesn't need any special mention of the features of the
function to be learned.
It is a versatile method because it doesn't require
prior knowledge about the network.
76. What is a Feed Forward Network?
76
Input Layer
Hidden Layer
Output Layer
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77. Types of Backpropagation Networks
77
✓ Static Back-propagation
✓ Recurrent Backpropagation
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
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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 is fast, simple and
straightforward to program.
A feedforward neural network is a man-
made neural network.
Backpropagation may be a short form for "backward
propagation of errors." it's a typical method of
coaching artificial neural networks.
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79. Expert System in
Artificial Intelligence
09
✓ What is an Expert System?
✓ Examples of Expert Systems
✓ Characteristic of Expert System
✓ Components of the expert system
✓ Conventional System vs. Expert system
✓ Human expert vs. expert system
✓ Benefits of expert systems
✓ Limitations of the expert system
✓ Applications of expert systems
79
80. Types of Deep Learning Networks
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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
Knowledge
Base
Inference
Engine
User
Interface
User
(May not be an expert)
Human Expert
Knowledge
Engineer
81. Examples of Expert Systems
81
The Highest Level of Expertise
The expert system offers the very best level of experience. It provides
efficiency, accuracy and imaginative problem-solving.
Right on Time Reaction
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
The expert system must be reliable, and it must not make any an error.
Flexible
It is significant that it remains flexible because it the is possessed by an Expert
system.
Capable of Handling Challenging Decision & Problems
An expert system is capable of handling challenging decision problems and
delivering solutions.
Effective Mechanism
Expert System must have an efficient mechanism to administer the
compilation of the prevailing knowledge in it.
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Expert System
Inference
Engine
Non-expert
User
Knowledge
from an expert
User
Interface
Knowledge
Base
Query
Advice
82. Characteristic of Expert System
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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
High level Performance
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
The expert system must be as reliable as a
person's expert
Good Reliability
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
Adequate Response Time
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
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
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
vs
Knowledge domain break away the
mechanism processing
01
The program could have
made an error
02
Not necessarily need all
the input/data
03
Changes within the rule are
often made with ease
04
The system can work only with
the rule as a tittle
05
Information and processing
combined during a sequential file
01
The program isn't wrong 02
Need all the input file 03
Changes to the program are
inconvenient
04
The system works if it's complete 05
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85. Human Expert vs. Expert System
85
Human Experts (Artificial ) Expert Systems
PermanentPerishable
Easy to TransferDifficult to Transfer
Easy to DocumentDifficult to Document
Affordable, costly to develop, but cheap
to operate
Expensive, especially top notch
Add Your Text HereAdd Your Text Here
86. Benefits of Expert Systems
86
01
Easy to Develop and
Modify the System
02
Fast
Response
03
Low
Accessibility Cost
04
Error Rate are Very
Low
05
Humans Emotions are not
Affected
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87. Limitations of the Expert System
87
Its Developed for a Specific Domain
It cant Deal with the Mixed Knowledge
There are Chances of Errors
Don’t Have Decision Making Power Like Humans
Expert System is not Widely used or Tested
Its Difficult to Maintain
Development Cost is High
Not Able to Explain the Logic Behind the Decision
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88. Applications of Expert Systems
88
Medical domain
(Diagnosis system,
medical operations
Process
Control System
Finance/Commerce (Stock
market trading, airline
scheduling cargo
scheduling)
Warehousing
Optimization
Knowledge domain
(Finding out the faults
in vehicles, computer)
RepairingMonitoring
system
Design domain (Camera
lens design,automobile
design)
Shipping
92. Bar Chart
92
This graph/chart is linked to excel, and changes
automatically based on data. Just left click on it and
select “Edit Data”.
Product02
This graph/chart is linked to excel, and changes
automatically based on data. Just left click on it and
select “Edit Data”.
Product01
0
10
20
30
40
50
60
70
80
90
100
Jan Feb Mar Apr May Jun
Sales(inUSDmillions)
Year 2020
100%
Product01
Product02
93. Stacked Line With Markers
93
This graph/chart is linked to excel, and changes
automatically based on data. Just left click on it and
select “Edit Data”.
Product02
This graph/chart is linked to excel, and changes
automatically based on data. Just left click on it and
select “Edit Data”.
Product01
3628.4
3573.9
3484.0
3532.1
3740.3
3881.7
3528.4
3873.9
3584.0
3732.1
3640.3
3981.7
3400
3500
3600
3700
3800
3900
4000
4100
2015 2016 2017 2018 2019 2020
InMillions
YEARS
94. This slide is 100% editable. Adapt it to your needs and capture your
audience's attention.
Agenda 01
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Agenda 02
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Agenda 03
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Agenda 05
Welcome to Our Agenda
94
95. Timeline
95
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Four
2020
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Three
2019
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Two
2018
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2017
96. Circular Diagram
96
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01
02
03
04
05
06
97. Venn
97
01 02 03
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98. Thank You
98
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