Banks are leading the way in becoming AI first banks. And this wave of technology focused approach is becoming the standard throughout the Financial services.
This document provides an introduction to machine learning. It discusses key machine learning concepts like supervised learning, unsupervised learning, reinforcement learning, batch learning, online learning, instance-based learning, and model-based learning. It also discusses applications of machine learning like spam filtering, clustering, and anomaly detection. Machine learning algorithms like artificial neural networks and deep learning are also introduced. The document aims to explain machine learning concepts and techniques in a clear and intuitive manner using examples.
The document provides information about Nettech India's data science course. It discusses the high demand for data scientists and what data science entails, including organizing, packaging and delivering data. It also defines what a data scientist does. The course covers topics like natural language processing, OpenCV, deep learning, and Tableau. It provides overviews of each topic and what students will learn, such as applying deep learning models to tasks like machine translation and using OpenCV for image processing, recognition and detection.
Machine learning is as valuable as the problems it solves. With this introductory talk in cooperation with AI Innovation Center and Ipsumio, we aim to provide the power of this tool to professionals dealing with important problems in healthcare, physics, manufacturing, and many others. This is a non-technical talk for domain experts to get a high-level understanding of this technology.
This document provides an introduction and agenda for a machine learning marketing use case presentation. It begins with introducing the presenter and their company Cup of Data, which is hiring data scientists. The basic agenda is then outlined, covering goals, the data science process, a machine learning primer, optimization techniques, and marketing examples. The remainder of the document dives deeper into each section of the agenda, providing overviews and explanations of topics like the data science workflow process, data preparation techniques, grouping algorithms, and deep learning.
This document provides an overview of machine learning, including definitions, common applications, and examples of companies using machine learning. It discusses how BuildFax, a company that provides building permit data and services to industries like insurance, used Amazon Machine Learning to build more accurate predictive models for roof age and job cost estimates. By leveraging Amazon ML, BuildFax was able to build models much faster and provide more precise, property-specific predictions to customers through APIs.
This talk was presented in Startup Master Class 2017 - http://aaiitkblr.org/smc/ 2017 @ Christ College Bangalore. Hosted by IIT Kanpur Alumni Association and co-presented by IIT KGP Alumni Association, IITACB, PanIIT, IIMA and IIMB alumni.
My co-presenter was Biswa Gourav Singh. And contributor was Navin Manaswi.
http://dataconomy.com/2017/04/history-neural-networks/ - timeline for neural networks
The document discusses decentralizing artificial intelligence to make it more secure and prevent misuse. It notes current issues like deep fakes and how AI could potentially be used for evil ends. The author argues that to solve these problems, AI needs to be made more open source, community governed, and profitable. Examples given of moving in this direction include OpenBazaar, NumerAI and Opus. The conclusion is that decentralizing AI following the Asilomar principles could help democratize AI and contain any potential threats.
Fontys ICT - Minor Applied Data ScienceOlaf Janssen
This document outlines the Applied Data Science (Big Data) minor program at Fontys Eindhoven. The minor runs for 20 weeks and earns students 30 ECTS credits. It covers topics like data preprocessing, machine learning algorithms, visualization, and social/ethical implications of data science. Students work on an integral project and learn skills like Python, Hadoop/Spark, MongoDB, and data visualization tools. The minor is suited for students interested in programming, data, and using math and statistics to gain insights from large datasets.
This document provides an introduction to machine learning. It discusses key machine learning concepts like supervised learning, unsupervised learning, reinforcement learning, batch learning, online learning, instance-based learning, and model-based learning. It also discusses applications of machine learning like spam filtering, clustering, and anomaly detection. Machine learning algorithms like artificial neural networks and deep learning are also introduced. The document aims to explain machine learning concepts and techniques in a clear and intuitive manner using examples.
The document provides information about Nettech India's data science course. It discusses the high demand for data scientists and what data science entails, including organizing, packaging and delivering data. It also defines what a data scientist does. The course covers topics like natural language processing, OpenCV, deep learning, and Tableau. It provides overviews of each topic and what students will learn, such as applying deep learning models to tasks like machine translation and using OpenCV for image processing, recognition and detection.
Machine learning is as valuable as the problems it solves. With this introductory talk in cooperation with AI Innovation Center and Ipsumio, we aim to provide the power of this tool to professionals dealing with important problems in healthcare, physics, manufacturing, and many others. This is a non-technical talk for domain experts to get a high-level understanding of this technology.
This document provides an introduction and agenda for a machine learning marketing use case presentation. It begins with introducing the presenter and their company Cup of Data, which is hiring data scientists. The basic agenda is then outlined, covering goals, the data science process, a machine learning primer, optimization techniques, and marketing examples. The remainder of the document dives deeper into each section of the agenda, providing overviews and explanations of topics like the data science workflow process, data preparation techniques, grouping algorithms, and deep learning.
This document provides an overview of machine learning, including definitions, common applications, and examples of companies using machine learning. It discusses how BuildFax, a company that provides building permit data and services to industries like insurance, used Amazon Machine Learning to build more accurate predictive models for roof age and job cost estimates. By leveraging Amazon ML, BuildFax was able to build models much faster and provide more precise, property-specific predictions to customers through APIs.
This talk was presented in Startup Master Class 2017 - http://aaiitkblr.org/smc/ 2017 @ Christ College Bangalore. Hosted by IIT Kanpur Alumni Association and co-presented by IIT KGP Alumni Association, IITACB, PanIIT, IIMA and IIMB alumni.
My co-presenter was Biswa Gourav Singh. And contributor was Navin Manaswi.
http://dataconomy.com/2017/04/history-neural-networks/ - timeline for neural networks
The document discusses decentralizing artificial intelligence to make it more secure and prevent misuse. It notes current issues like deep fakes and how AI could potentially be used for evil ends. The author argues that to solve these problems, AI needs to be made more open source, community governed, and profitable. Examples given of moving in this direction include OpenBazaar, NumerAI and Opus. The conclusion is that decentralizing AI following the Asilomar principles could help democratize AI and contain any potential threats.
Fontys ICT - Minor Applied Data ScienceOlaf Janssen
This document outlines the Applied Data Science (Big Data) minor program at Fontys Eindhoven. The minor runs for 20 weeks and earns students 30 ECTS credits. It covers topics like data preprocessing, machine learning algorithms, visualization, and social/ethical implications of data science. Students work on an integral project and learn skills like Python, Hadoop/Spark, MongoDB, and data visualization tools. The minor is suited for students interested in programming, data, and using math and statistics to gain insights from large datasets.
This document provides an overview of getting started with data science using Python. It discusses what data science is, why it is in high demand, and the typical skills and backgrounds of data scientists. It then covers popular Python libraries for data science like NumPy, Pandas, Scikit-Learn, TensorFlow, and Keras. Common data science steps are outlined including data gathering, preparation, exploration, model building, validation, and deployment. Example applications and case studies are discussed along with resources for learning including podcasts, websites, communities, books, and TV shows.
[The full content of this talk can be found in this article: https://medium.com/@clarecorthell/hybrid-artificial-intelligence-how-artificial-assistants-work-eefbafbd5334]
When we think about automated, learning systems, we often think of a world without humans - but there are many signs and limitations that show we’re not moving toward a human-free world. We’ll explore the strengths and weaknesses of humans and computers, and explore the new design paradigm that’s making artificial intelligence more powerful than it’s ever been before.
Thinking Machines Conference, February 2016, Manila
http://thinkingmachin.es/events/
Future of data science as a professionJose Quesada
How can you thrive in a future where machine learning has been popular for a few years already?
In this talk, I will give you actionable advice from my experience training serious data scientists at our retreat center in Berlin. You are going to face these pointy, hard questions:
- What is the promise of machine learning? Has it happened yet?
- Is it easy to take advance of machine learning, now that most algorithms are nicely packaged in APIs and libraries?
- How much time should I spend getting good at machine learning? Am I good enough now?
- Are data scientists going to be replaced by algorithms? Are we all?
- Is it easy to hire talent in machine learning after the explosion of MOOCs?
A Comprehensive Learning Path to Become a Data Science 2021.pptxRajSingh512965
The 2021 data science learning path provides a comprehensive curriculum to become a data scientist. It includes extended skills in storytelling, model deployment, unsupervised learning, exercises, and projects. The path covers key skills and tools like Python, R, machine learning algorithms, deep learning, natural language processing, and model deployment. It consists of monthly modules that progress from the data science toolkit to advanced topics, with hands-on training and real-world projects.
This document summarizes an introductory presentation on data science. It introduces the presenter and their background in data and analytics. The goals of the presentation are to define what a data scientist is, how the field has emerged, and how to become one. It discusses the growing demand and salaries for data scientists. Examples are given of how data science has been applied at companies like LinkedIn and Netflix. The presentation covers big data, Hadoop, data processing techniques, machine learning algorithms, and tools used in data science. Finally, attendees are encouraged to consider Thinkful's data science bootcamp program.
The document discusses onboarding artificial intelligence and machine learning. It begins by outlining some key questions to consider when introducing advanced technology like AI into an organization, such as how to introduce it to staff and address concerns about jobs being replaced. It then provides definitions and examples of artificial intelligence and machine learning. The rest of the document covers topics like how AI is used in everyday life via applications like voice assistants and image recognition. It also discusses challenges of AI and ways libraries can embrace and apply AI concepts.
AI - Artificial Intelligence - Implications for LibrariesBrian Pichman
What does the world of AI (artificial intelligence) mean for libraries? Can AI replace library services or how can libraries leverage the technology for more streamlined services. From Smart Houses, to Robots, to technology yet to be mainstreamed, this session will cover it all to help you better prepare and plan for the future.
This document discusses Microsoft's efforts in artificial intelligence and machine learning. It provides context on the current state of AI, highlighting how machine learning has progressed from addressing specific tasks to becoming more general. It outlines Microsoft's investments in AI, including forming a new 5,000-person division and making AI pervasive across its products. The document also discusses challenges around developing machine learning programs and ensuring AI is developed in a responsible, trustworthy manner.
An Elementary Introduction to Artificial Intelligence, Data Science and Machi...Dozie Agbo
This presentation is a friendly introduction to Artificial Intelligence, Data Science and Machine Learning. It touches on the beginnings of AI, the steps involved in Data Science, the roles involving operations on data, and the buzz around "Technology Singularity".
It ends by looking at tools and system requirements for people who might want to start a career in AI.
Have fun exploring Artificial Intelligence!
The document discusses the key steps in an AI project cycle:
1) Problem scoping involves understanding the problem, stakeholders, location, and reasons for solving it.
2) Data acquisition collects accurate and reliable structured or unstructured data from various sources.
3) Data exploration arranges and visualizes the data to understand trends and patterns using tools like charts and graphs.
4) Modelling creates algorithms and models by training them on large datasets to perform tasks intelligently.
5) Evaluation tests the project by comparing outputs to actual answers to identify areas for improvement.
What your employees need to learn to work with data in the 21 st century Human Capital Media
The data revolution is well underway. Regardless of the industry or department you manage, working with data will soon be an essential part of all of our jobs, if it isn’t already. This could take the form of basic data analytics, data science, machine learning or artificial intelligence. This can be overwhelming: what do all these terms mean and how can they be leveraged to impact your employees’ work, whether that be in finance, healthcare, tech or the public sector, among many others? This webinar will give you a primer for understanding how data can impact your employees’ work, what they need to know and how to go about educating them on it.
This document contains an agenda and information about an upcoming TechData MeetUp on artificial intelligence research. The agenda includes an introduction, discussions on the benefits of a Ph.D. in computer science, hot topics in AI, and must-have skills for AI research. It also covers how to publish research results. The document provides background on the speaker, Nigar Alışzadə, and her education and work experience in computer science and AI research. It also answers common questions about getting a Ph.D. in computer science and the job opportunities after. Additional sections define artificial intelligence and various AI fields like machine learning, neural networks, computer vision, natural language processing, the internet of things, and recommendation engines.
Brochure data science learning path board-infinity (1)NirupamNishant2
Board Infinity is a best digital marketing and data science institute in mumbai, which is a full-stack career platform for students and jobseekers enabled by personalised learning paths,career coaches and access to various job oppurtunities. We provide online and offline training in Data Science, Digital Marketing, Full stack Web Development,Product management< machine learning and Atrificial Intelligence,Online career counselling and other career solutions
The purpose of this workshop was to highlight the the significance of AI, IoT and their integration under the light of scientific research. The presentation of the workshop can be found below.
This document provides an overview of deep learning and neural networks. It begins with definitions of machine learning, artificial intelligence, and the different types of machine learning problems. It then introduces deep learning, explaining that it uses neural networks with multiple layers to learn representations of data. The document discusses why deep learning works better than traditional machine learning for complex problems. It covers key concepts like activation functions, gradient descent, backpropagation, and overfitting. It also provides examples of applications of deep learning and popular deep learning frameworks like TensorFlow. Overall, the document gives a high-level introduction to deep learning concepts and techniques.
HOW TO START UP A COMPANY A STEP-BY-STEP GUIDE.pdf46adnanshahzad
How to Start Up a Company: A Step-by-Step Guide Starting a company is an exciting adventure that combines creativity, strategy, and hard work. It can seem overwhelming at first, but with the right guidance, anyone can transform a great idea into a successful business. Let's dive into how to start up a company, from the initial spark of an idea to securing funding and launching your startup.
Introduction
Have you ever dreamed of turning your innovative idea into a thriving business? Starting a company involves numerous steps and decisions, but don't worry—we're here to help. Whether you're exploring how to start a startup company or wondering how to start up a small business, this guide will walk you through the process, step by step.
This document provides an overview of getting started with data science using Python. It discusses what data science is, why it is in high demand, and the typical skills and backgrounds of data scientists. It then covers popular Python libraries for data science like NumPy, Pandas, Scikit-Learn, TensorFlow, and Keras. Common data science steps are outlined including data gathering, preparation, exploration, model building, validation, and deployment. Example applications and case studies are discussed along with resources for learning including podcasts, websites, communities, books, and TV shows.
[The full content of this talk can be found in this article: https://medium.com/@clarecorthell/hybrid-artificial-intelligence-how-artificial-assistants-work-eefbafbd5334]
When we think about automated, learning systems, we often think of a world without humans - but there are many signs and limitations that show we’re not moving toward a human-free world. We’ll explore the strengths and weaknesses of humans and computers, and explore the new design paradigm that’s making artificial intelligence more powerful than it’s ever been before.
Thinking Machines Conference, February 2016, Manila
http://thinkingmachin.es/events/
Future of data science as a professionJose Quesada
How can you thrive in a future where machine learning has been popular for a few years already?
In this talk, I will give you actionable advice from my experience training serious data scientists at our retreat center in Berlin. You are going to face these pointy, hard questions:
- What is the promise of machine learning? Has it happened yet?
- Is it easy to take advance of machine learning, now that most algorithms are nicely packaged in APIs and libraries?
- How much time should I spend getting good at machine learning? Am I good enough now?
- Are data scientists going to be replaced by algorithms? Are we all?
- Is it easy to hire talent in machine learning after the explosion of MOOCs?
A Comprehensive Learning Path to Become a Data Science 2021.pptxRajSingh512965
The 2021 data science learning path provides a comprehensive curriculum to become a data scientist. It includes extended skills in storytelling, model deployment, unsupervised learning, exercises, and projects. The path covers key skills and tools like Python, R, machine learning algorithms, deep learning, natural language processing, and model deployment. It consists of monthly modules that progress from the data science toolkit to advanced topics, with hands-on training and real-world projects.
This document summarizes an introductory presentation on data science. It introduces the presenter and their background in data and analytics. The goals of the presentation are to define what a data scientist is, how the field has emerged, and how to become one. It discusses the growing demand and salaries for data scientists. Examples are given of how data science has been applied at companies like LinkedIn and Netflix. The presentation covers big data, Hadoop, data processing techniques, machine learning algorithms, and tools used in data science. Finally, attendees are encouraged to consider Thinkful's data science bootcamp program.
The document discusses onboarding artificial intelligence and machine learning. It begins by outlining some key questions to consider when introducing advanced technology like AI into an organization, such as how to introduce it to staff and address concerns about jobs being replaced. It then provides definitions and examples of artificial intelligence and machine learning. The rest of the document covers topics like how AI is used in everyday life via applications like voice assistants and image recognition. It also discusses challenges of AI and ways libraries can embrace and apply AI concepts.
AI - Artificial Intelligence - Implications for LibrariesBrian Pichman
What does the world of AI (artificial intelligence) mean for libraries? Can AI replace library services or how can libraries leverage the technology for more streamlined services. From Smart Houses, to Robots, to technology yet to be mainstreamed, this session will cover it all to help you better prepare and plan for the future.
This document discusses Microsoft's efforts in artificial intelligence and machine learning. It provides context on the current state of AI, highlighting how machine learning has progressed from addressing specific tasks to becoming more general. It outlines Microsoft's investments in AI, including forming a new 5,000-person division and making AI pervasive across its products. The document also discusses challenges around developing machine learning programs and ensuring AI is developed in a responsible, trustworthy manner.
An Elementary Introduction to Artificial Intelligence, Data Science and Machi...Dozie Agbo
This presentation is a friendly introduction to Artificial Intelligence, Data Science and Machine Learning. It touches on the beginnings of AI, the steps involved in Data Science, the roles involving operations on data, and the buzz around "Technology Singularity".
It ends by looking at tools and system requirements for people who might want to start a career in AI.
Have fun exploring Artificial Intelligence!
The document discusses the key steps in an AI project cycle:
1) Problem scoping involves understanding the problem, stakeholders, location, and reasons for solving it.
2) Data acquisition collects accurate and reliable structured or unstructured data from various sources.
3) Data exploration arranges and visualizes the data to understand trends and patterns using tools like charts and graphs.
4) Modelling creates algorithms and models by training them on large datasets to perform tasks intelligently.
5) Evaluation tests the project by comparing outputs to actual answers to identify areas for improvement.
What your employees need to learn to work with data in the 21 st century Human Capital Media
The data revolution is well underway. Regardless of the industry or department you manage, working with data will soon be an essential part of all of our jobs, if it isn’t already. This could take the form of basic data analytics, data science, machine learning or artificial intelligence. This can be overwhelming: what do all these terms mean and how can they be leveraged to impact your employees’ work, whether that be in finance, healthcare, tech or the public sector, among many others? This webinar will give you a primer for understanding how data can impact your employees’ work, what they need to know and how to go about educating them on it.
This document contains an agenda and information about an upcoming TechData MeetUp on artificial intelligence research. The agenda includes an introduction, discussions on the benefits of a Ph.D. in computer science, hot topics in AI, and must-have skills for AI research. It also covers how to publish research results. The document provides background on the speaker, Nigar Alışzadə, and her education and work experience in computer science and AI research. It also answers common questions about getting a Ph.D. in computer science and the job opportunities after. Additional sections define artificial intelligence and various AI fields like machine learning, neural networks, computer vision, natural language processing, the internet of things, and recommendation engines.
Brochure data science learning path board-infinity (1)NirupamNishant2
Board Infinity is a best digital marketing and data science institute in mumbai, which is a full-stack career platform for students and jobseekers enabled by personalised learning paths,career coaches and access to various job oppurtunities. We provide online and offline training in Data Science, Digital Marketing, Full stack Web Development,Product management< machine learning and Atrificial Intelligence,Online career counselling and other career solutions
The purpose of this workshop was to highlight the the significance of AI, IoT and their integration under the light of scientific research. The presentation of the workshop can be found below.
This document provides an overview of deep learning and neural networks. It begins with definitions of machine learning, artificial intelligence, and the different types of machine learning problems. It then introduces deep learning, explaining that it uses neural networks with multiple layers to learn representations of data. The document discusses why deep learning works better than traditional machine learning for complex problems. It covers key concepts like activation functions, gradient descent, backpropagation, and overfitting. It also provides examples of applications of deep learning and popular deep learning frameworks like TensorFlow. Overall, the document gives a high-level introduction to deep learning concepts and techniques.
HOW TO START UP A COMPANY A STEP-BY-STEP GUIDE.pdf46adnanshahzad
How to Start Up a Company: A Step-by-Step Guide Starting a company is an exciting adventure that combines creativity, strategy, and hard work. It can seem overwhelming at first, but with the right guidance, anyone can transform a great idea into a successful business. Let's dive into how to start up a company, from the initial spark of an idea to securing funding and launching your startup.
Introduction
Have you ever dreamed of turning your innovative idea into a thriving business? Starting a company involves numerous steps and decisions, but don't worry—we're here to help. Whether you're exploring how to start a startup company or wondering how to start up a small business, this guide will walk you through the process, step by step.
How MJ Global Leads the Packaging Industry.pdfMJ Global
MJ Global's success in staying ahead of the curve in the packaging industry is a testament to its dedication to innovation, sustainability, and customer-centricity. By embracing technological advancements, leading in eco-friendly solutions, collaborating with industry leaders, and adapting to evolving consumer preferences, MJ Global continues to set new standards in the packaging sector.
Best practices for project execution and deliveryCLIVE MINCHIN
A select set of project management best practices to keep your project on-track, on-cost and aligned to scope. Many firms have don't have the necessary skills, diligence, methods and oversight of their projects; this leads to slippage, higher costs and longer timeframes. Often firms have a history of projects that simply failed to move the needle. These best practices will help your firm avoid these pitfalls but they require fortitude to apply.
Understanding User Needs and Satisfying ThemAggregage
https://www.productmanagementtoday.com/frs/26903918/understanding-user-needs-and-satisfying-them
We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. There are three challenges we face when doing this. The obvious challenge is figuring out what our users need; the non-obvious challenges are in creating a shared understanding of those needs and in sensing if what we're doing is meeting those needs.
In this webinar, we won't focus on the research methods for discovering user-needs. We will focus on synthesis of the needs we discover, communication and alignment tools, and how we operationalize addressing those needs.
Industry expert Scott Sehlhorst will:
• Introduce a taxonomy for user goals with real world examples
• Present the Onion Diagram, a tool for contextualizing task-level goals
• Illustrate how customer journey maps capture activity-level and task-level goals
• Demonstrate the best approach to selection and prioritization of user-goals to address
• Highlight the crucial benchmarks, observable changes, in ensuring fulfillment of customer needs
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[To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This PowerPoint compilation offers a comprehensive overview of 20 leading innovation management frameworks and methodologies, selected for their broad applicability across various industries and organizational contexts. These frameworks are valuable resources for a wide range of users, including business professionals, educators, and consultants.
Each framework is presented with visually engaging diagrams and templates, ensuring the content is both informative and appealing. While this compilation is thorough, please note that the slides are intended as supplementary resources and may not be sufficient for standalone instructional purposes.
This compilation is ideal for anyone looking to enhance their understanding of innovation management and drive meaningful change within their organization. Whether you aim to improve product development processes, enhance customer experiences, or drive digital transformation, these frameworks offer valuable insights and tools to help you achieve your goals.
INCLUDED FRAMEWORKS/MODELS:
1. Stanford’s Design Thinking
2. IDEO’s Human-Centered Design
3. Strategyzer’s Business Model Innovation
4. Lean Startup Methodology
5. Agile Innovation Framework
6. Doblin’s Ten Types of Innovation
7. McKinsey’s Three Horizons of Growth
8. Customer Journey Map
9. Christensen’s Disruptive Innovation Theory
10. Blue Ocean Strategy
11. Strategyn’s Jobs-To-Be-Done (JTBD) Framework with Job Map
12. Design Sprint Framework
13. The Double Diamond
14. Lean Six Sigma DMAIC
15. TRIZ Problem-Solving Framework
16. Edward de Bono’s Six Thinking Hats
17. Stage-Gate Model
18. Toyota’s Six Steps of Kaizen
19. Microsoft’s Digital Transformation Framework
20. Design for Six Sigma (DFSS)
To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations
Company Valuation webinar series - Tuesday, 4 June 2024FelixPerez547899
This session provided an update as to the latest valuation data in the UK and then delved into a discussion on the upcoming election and the impacts on valuation. We finished, as always with a Q&A
[To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
This presentation is a curated compilation of PowerPoint diagrams and templates designed to illustrate 20 different digital transformation frameworks and models. These frameworks are based on recent industry trends and best practices, ensuring that the content remains relevant and up-to-date.
Key highlights include Microsoft's Digital Transformation Framework, which focuses on driving innovation and efficiency, and McKinsey's Ten Guiding Principles, which provide strategic insights for successful digital transformation. Additionally, Forrester's framework emphasizes enhancing customer experiences and modernizing IT infrastructure, while IDC's MaturityScape helps assess and develop organizational digital maturity. MIT's framework explores cutting-edge strategies for achieving digital success.
These materials are perfect for enhancing your business or classroom presentations, offering visual aids to supplement your insights. Please note that while comprehensive, these slides are intended as supplementary resources and may not be complete for standalone instructional purposes.
Frameworks/Models included:
Microsoft’s Digital Transformation Framework
McKinsey’s Ten Guiding Principles of Digital Transformation
Forrester’s Digital Transformation Framework
IDC’s Digital Transformation MaturityScape
MIT’s Digital Transformation Framework
Gartner’s Digital Transformation Framework
Accenture’s Digital Strategy & Enterprise Frameworks
Deloitte’s Digital Industrial Transformation Framework
Capgemini’s Digital Transformation Framework
PwC’s Digital Transformation Framework
Cisco’s Digital Transformation Framework
Cognizant’s Digital Transformation Framework
DXC Technology’s Digital Transformation Framework
The BCG Strategy Palette
McKinsey’s Digital Transformation Framework
Digital Transformation Compass
Four Levels of Digital Maturity
Design Thinking Framework
Business Model Canvas
Customer Journey Map
Taurus Zodiac Sign: Unveiling the Traits, Dates, and Horoscope Insights of th...my Pandit
Dive into the steadfast world of the Taurus Zodiac Sign. Discover the grounded, stable, and logical nature of Taurus individuals, and explore their key personality traits, important dates, and horoscope insights. Learn how the determination and patience of the Taurus sign make them the rock-steady achievers and anchors of the zodiac.
Easily Verify Compliance and Security with Binance KYCAny kyc Account
Use our simple KYC verification guide to make sure your Binance account is safe and compliant. Discover the fundamentals, appreciate the significance of KYC, and trade on one of the biggest cryptocurrency exchanges with confidence.
3 Simple Steps To Buy Verified Payoneer Account In 2024SEOSMMEARTH
Buy Verified Payoneer Account: Quick and Secure Way to Receive Payments
Buy Verified Payoneer Account With 100% secure documents, [ USA, UK, CA ]. Are you looking for a reliable and safe way to receive payments online? Then you need buy verified Payoneer account ! Payoneer is a global payment platform that allows businesses and individuals to send and receive money in over 200 countries.
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Discover timeless style with the 2022 Vintage Roman Numerals Men's Ring. Crafted from premium stainless steel, this 6mm wide ring embodies elegance and durability. Perfect as a gift, it seamlessly blends classic Roman numeral detailing with modern sophistication, making it an ideal accessory for any occasion.
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How are Lilac French Bulldogs Beauty Charming the World and Capturing Hearts....Lacey Max
“After being the most listed dog breed in the United States for 31
years in a row, the Labrador Retriever has dropped to second place
in the American Kennel Club's annual survey of the country's most
popular canines. The French Bulldog is the new top dog in the
United States as of 2022. The stylish puppy has ascended the
rankings in rapid time despite having health concerns and limited
color choices.”
Brian Fitzsimmons on the Business Strategy and Content Flywheel of Barstool S...Neil Horowitz
On episode 272 of the Digital and Social Media Sports Podcast, Neil chatted with Brian Fitzsimmons, Director of Licensing and Business Development for Barstool Sports.
What follows is a collection of snippets from the podcast. To hear the full interview and more, check out the podcast on all podcast platforms and at www.dsmsports.net
Storytelling is an incredibly valuable tool to share data and information. To get the most impact from stories there are a number of key ingredients. These are based on science and human nature. Using these elements in a story you can deliver information impactfully, ensure action and drive change.
7. davio@brainsparks.io | @daviolarnout
Machine Learning
The use of data and
algorithms to achieve
(currently narrow) AI
Deep Learning
The resurgence of a
class of ML models
called neural nets
Artificial Intelligence
The concept of machines
exhibiting human
intelligence
⊂ ⊂
8. davio@brainsparks.io | @daviolarnout
Machine Learning - data & algorithms
Old way:
Write software with explicit rules to
follow:
if email contains V!agrå
then mark is-spam;
if email contains …
if email contains …
New way:
Write software to learn from examples
(data):
try to classify some emails;
change self to reduce errors
repeat;