What Will I Learn?
How Machine learning works.
What are some simple applications of Machine learning?
What are the ethics of Machine learning?
How big is the future of Machine learning?
Who is the target audience?
People who are progressing their journey towards machine learning
Where there is data and it needs to be analyzed, Machine learning is the best way to do so.
Benefits
Data Science sector is increasing rapidly, so is the demand of people who can write algorithms to analyze that data.
With the increasing amount of data, the accuracy of the result has to be increased.
The document provides an outline for a course on data structures and algorithms. It includes topics like data types and operations, time-space tradeoffs, algorithm development, asymptotic notations, common data structures, sorting and searching algorithms, and linked lists. The course will use Google Classroom and have assignments, quizzes, and a final exam.
This document summarizes an engineer's career path and offers advice for success. It describes the author's own non-linear career path, which included contracting work and jobs at startups and larger companies like Intuit and Google. It notes that the traditional path of staying at one company for decades is no longer common. The document then provides tips for finding jobs, including networking, attending user groups, and ensuring resumes emphasize coding skills. It emphasizes the importance of continually learning, through activities like reading source code instead of just documentation, blogging, speaking at conferences, and being willing to solve problems and share solutions online. Personal maturity, such as having a soft answer and listening to others, is also advised as key to career success
Data Driven Sales: Building AI That Searches, Learns, and SellsLeadGenius
LeadGenius Co-Founder and Chief Scientist, Anand Kulkarni discusses the future of sales automation, remote work, and outbound email at the SVDE Meetup Group presented by Treasure Data. September 2015.
Full video of presentation available at: http://blog.leadgenius.com/data-driven-sales-that-scale-ai-that-sells/
Video: https://www.facebook.com/foundersas/videos/712970348885532/
The bottleneck in AI is data, not algorithms. But how do we get data and knowledge from humans to ML systems? What will the future of data collection look like? And which skills and strategies do we need to improve the process and make our products useful?
Automated essay scoring: an introduction to grading essays with NLP and AINathan Thompson
Automated essay scoring (AES) refers to the use of natural language processing (NLP), machine learning (ML), and artificial intelligence to grade essay responses from exams.
AES refers to the calibration of a specific model for each rubric and each prompt, based on actual data from human raters. That is, you can't just feed a pile of essays to some bot and tell it to grade them on "growth mindset." Instead, you have to define a very specific grading rubric, score at least a few hundred students by hand, and then use NLP and ML software to fit ML models.
This powerpoint provides a broad introduction to this topic. For more information, visit https://assess.com/smartmarq-ai-essay-scoring/
What Will I Learn?
How Machine learning works.
What are some simple applications of Machine learning?
What are the ethics of Machine learning?
How big is the future of Machine learning?
Who is the target audience?
People who are progressing their journey towards machine learning
Where there is data and it needs to be analyzed, Machine learning is the best way to do so.
Benefits
Data Science sector is increasing rapidly, so is the demand of people who can write algorithms to analyze that data.
With the increasing amount of data, the accuracy of the result has to be increased.
The document provides an outline for a course on data structures and algorithms. It includes topics like data types and operations, time-space tradeoffs, algorithm development, asymptotic notations, common data structures, sorting and searching algorithms, and linked lists. The course will use Google Classroom and have assignments, quizzes, and a final exam.
This document summarizes an engineer's career path and offers advice for success. It describes the author's own non-linear career path, which included contracting work and jobs at startups and larger companies like Intuit and Google. It notes that the traditional path of staying at one company for decades is no longer common. The document then provides tips for finding jobs, including networking, attending user groups, and ensuring resumes emphasize coding skills. It emphasizes the importance of continually learning, through activities like reading source code instead of just documentation, blogging, speaking at conferences, and being willing to solve problems and share solutions online. Personal maturity, such as having a soft answer and listening to others, is also advised as key to career success
Data Driven Sales: Building AI That Searches, Learns, and SellsLeadGenius
LeadGenius Co-Founder and Chief Scientist, Anand Kulkarni discusses the future of sales automation, remote work, and outbound email at the SVDE Meetup Group presented by Treasure Data. September 2015.
Full video of presentation available at: http://blog.leadgenius.com/data-driven-sales-that-scale-ai-that-sells/
Video: https://www.facebook.com/foundersas/videos/712970348885532/
The bottleneck in AI is data, not algorithms. But how do we get data and knowledge from humans to ML systems? What will the future of data collection look like? And which skills and strategies do we need to improve the process and make our products useful?
Automated essay scoring: an introduction to grading essays with NLP and AINathan Thompson
Automated essay scoring (AES) refers to the use of natural language processing (NLP), machine learning (ML), and artificial intelligence to grade essay responses from exams.
AES refers to the calibration of a specific model for each rubric and each prompt, based on actual data from human raters. That is, you can't just feed a pile of essays to some bot and tell it to grade them on "growth mindset." Instead, you have to define a very specific grading rubric, score at least a few hundred students by hand, and then use NLP and ML software to fit ML models.
This powerpoint provides a broad introduction to this topic. For more information, visit https://assess.com/smartmarq-ai-essay-scoring/
This document provides an overview of an algorithms and data structures course. It includes the instructor's name and contact information. It outlines the topics that will be covered in the course, including fundamentals of algorithms, sorting and searching, stacks, queues, linked lists, trees, hashing, and graphs. It provides recommendations for reference books and describes the marking scheme for assignments, quizzes, exams, and projects.
Inside Story on HPC’s Role in Bridges Strategic Reasoning Research Project at...Dana Gardner
Transcript of a discussion on how Carnegie Mellon University researchers are advancing strategic reasoning and machine learning capabilities using the latest in high performance computing.
This document provides an introduction to machine learning concepts. It begins with an overview of the book's organization and topics to be covered, including descriptive statistics, algebra, linear regression, classification, clustering, decision trees, and neural networks. It then discusses requisite skills like basic Python and software needed. The document provides definitions of machine learning and describes common problem types it can solve. It also outlines popular machine learning tools and frameworks.
Presentation at FlowFactor 2019. Another look at the data science hierarchy of needs specifically looking at chasing Artificial Intelligence and Machine Learning.
Machine learning is and should not be the exclusive domain of large commercial companies, data scientists, mathematics, computer scientists or hackers. Our belief is that every business and everyone should be able to take advantage of the machine learning techniques and applications available.
This document provides an introduction to machine learning and ML.Net. It discusses the differences between artificial intelligence and machine learning, as well as between supervised and unsupervised learning. It also gives examples of common machine learning problems like categorization, prediction, and finding relationships. Finally, it provides a brief overview of ML.Net, describing it as a free and open-source machine learning framework for .NET developers that helps with building, training, evaluating, and consuming machine learning models.
Machine learning para tertulianos, by javier ramirez at teowakijavier ramirez
Would you like to use machine learning in your projects but you think you don't know enough? I'll tell you why machine learning is relevant, how machines learn, and which ready-made algorithms you can use if you don't know much maths but you still want to take advantage of ML
1) The document is a presentation about machine learning and artificial intelligence presented through memes.
2) It begins with an introduction of the presenter and their background and then outlines the agenda which is fun memes and learning.
3) The presentation then goes through explanations of key machine learning concepts like what machine learning is, what it can do, the differences between machine learning and artificial intelligence, what deep learning and types of machine learning are, and examples like regression, classification, clustering, neural networks, recurrent neural networks and generative adversarial networks.
This is a general presentation that is appropriate for anyone that is just learning concepts of semantic integration. This presentation covers some of the background concepts underlying semantics (Ogden\'s Semantic Triangle), lexical and conceptual mapping, metadata registries, metadata discovery and semantic thinking. Excellent for an introductory class in business semantics.
This document provides a summary of artificial intelligence including definitions, history, and whether computers can perform certain tasks. It discusses four approaches to defining AI: (1) thinking like humans through cognitive science, (2) thinking rationally using logic, (3) acting like humans as in the Turing test, and (4) acting rationally to achieve the best outcomes. The document also summarizes key events in the history of AI and whether computers can beat humans at games, recognize speech, understand language, learn, see, plan, and more.
A summary of Explainable Artificial Intelligence mostly known as XAI. This answers the question of why we need the explanation in terms of machine learning model predictions. How does XAI work and its importance in the machine learning world. XAI is relatively a new topic in analyzing machine learning output and prediction. The main motive here is to understand the model and moreover trust the model to perform a certain task by providing a proper explanation.
"You Can Do It" by Louis Monier (Altavista Co-Founder & CTO) & Gregory Renard (CTO & Artificial Intelligence Lead Architect at Xbrain) for Deep Learning keynote #0 at Holberton School (http://www.meetup.com/Holberton-School/events/228364522/)
If you want to assist to similar keynote for free, checkout http://www.meetup.com/Holberton-School/
Here are some key terms that are similar to "champagne":
- Sparkling wines
- French champagne
- Cognac
- Rosé
- White wine
- Sparkling wine
- Wine
- Burgundy
- Bordeaux
- Cava
- Prosecco
Some specific champagne brands that are similar terms include Moët, Veuve Clicquot, Dom Pérignon, Taittinger, and Bollinger. Grape varieties used in champagne production like Chardonnay and Pinot Noir could also be considered similar terms.
Data Science. .Net/C# Monte Carlo modeling. The R Programming language. See it all come together in one place in this talk. Presentation date 6/13 at Lake County .NET User Group.
Michael Losee grew up in Layton, Utah with 6 sisters. He enjoys video games, reading fantasy novels, and building custom computers. He has a Bachelor's degree in Computer Science from Weber State University. Professionally, he has 5 years of experience as a Lead Systems Analyst for the Defense Logistics Agency and 2 years as a Security Consultant. He is currently working to help paralyzed people learn to walk again and pursues coding as a hobby and potential career.
Computer Science interviews are a different breed from other interviews and, as such, require specialized skills and techniques. Cracking the Technical Interview will teach you how to prepare for technical interviews, what top companies like Google and Microsoft really look for, and how to tackle the toughest programming and algorithm problems. This talk will include stories from the speaker's extensive interviewing experience as well as a live "demo" of how to tackle a technical problem.
Lecture 1 Slides -Introduction to algorithms.pdfRanvinuHewage
- The document discusses reasons for studying algorithms and their broad impacts.
- Key reasons include solving hard problems, intellectual stimulation, becoming a proficient programmer, unlocking secrets of life and the universe, and fun.
- Algorithms have roots in ancient times but new opportunities in the modern era with computers and large data. They allow addressing problems that could not otherwise be solved.
This talk is a primer to Machine Learning. I will provide a brief introduction what is ML and how it works. I will walk you down the Machine Learning pipeline from data gathering, data normalizing and feature engineering, common supervised and unsupervised algorithms, training models, and delivering results to production. I will also provide recommendations to tools that help you provide the best ML experience, include programming languages and libraries.
If there is time at the end of the talk, I will walk through two coding examples, using the HMS Titanic Passenger List, present with Python scikit-learn using algorithm random-trees to check if ML can correctly predict passenger survival and with R programming for feature engineering of the same dataset
Note to data-scientists and programmers: If you sign up to attend, plan to visit my Github repository! I have many Machine Learning coding examples in Python scikit-learn, GNU Octave, and R Programming.
https://github.com/jefftune/gitw-2017-ml
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
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This document provides an overview of an algorithms and data structures course. It includes the instructor's name and contact information. It outlines the topics that will be covered in the course, including fundamentals of algorithms, sorting and searching, stacks, queues, linked lists, trees, hashing, and graphs. It provides recommendations for reference books and describes the marking scheme for assignments, quizzes, exams, and projects.
Inside Story on HPC’s Role in Bridges Strategic Reasoning Research Project at...Dana Gardner
Transcript of a discussion on how Carnegie Mellon University researchers are advancing strategic reasoning and machine learning capabilities using the latest in high performance computing.
This document provides an introduction to machine learning concepts. It begins with an overview of the book's organization and topics to be covered, including descriptive statistics, algebra, linear regression, classification, clustering, decision trees, and neural networks. It then discusses requisite skills like basic Python and software needed. The document provides definitions of machine learning and describes common problem types it can solve. It also outlines popular machine learning tools and frameworks.
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Machine learning is and should not be the exclusive domain of large commercial companies, data scientists, mathematics, computer scientists or hackers. Our belief is that every business and everyone should be able to take advantage of the machine learning techniques and applications available.
This document provides an introduction to machine learning and ML.Net. It discusses the differences between artificial intelligence and machine learning, as well as between supervised and unsupervised learning. It also gives examples of common machine learning problems like categorization, prediction, and finding relationships. Finally, it provides a brief overview of ML.Net, describing it as a free and open-source machine learning framework for .NET developers that helps with building, training, evaluating, and consuming machine learning models.
Machine learning para tertulianos, by javier ramirez at teowakijavier ramirez
Would you like to use machine learning in your projects but you think you don't know enough? I'll tell you why machine learning is relevant, how machines learn, and which ready-made algorithms you can use if you don't know much maths but you still want to take advantage of ML
1) The document is a presentation about machine learning and artificial intelligence presented through memes.
2) It begins with an introduction of the presenter and their background and then outlines the agenda which is fun memes and learning.
3) The presentation then goes through explanations of key machine learning concepts like what machine learning is, what it can do, the differences between machine learning and artificial intelligence, what deep learning and types of machine learning are, and examples like regression, classification, clustering, neural networks, recurrent neural networks and generative adversarial networks.
This is a general presentation that is appropriate for anyone that is just learning concepts of semantic integration. This presentation covers some of the background concepts underlying semantics (Ogden\'s Semantic Triangle), lexical and conceptual mapping, metadata registries, metadata discovery and semantic thinking. Excellent for an introductory class in business semantics.
This document provides a summary of artificial intelligence including definitions, history, and whether computers can perform certain tasks. It discusses four approaches to defining AI: (1) thinking like humans through cognitive science, (2) thinking rationally using logic, (3) acting like humans as in the Turing test, and (4) acting rationally to achieve the best outcomes. The document also summarizes key events in the history of AI and whether computers can beat humans at games, recognize speech, understand language, learn, see, plan, and more.
A summary of Explainable Artificial Intelligence mostly known as XAI. This answers the question of why we need the explanation in terms of machine learning model predictions. How does XAI work and its importance in the machine learning world. XAI is relatively a new topic in analyzing machine learning output and prediction. The main motive here is to understand the model and moreover trust the model to perform a certain task by providing a proper explanation.
"You Can Do It" by Louis Monier (Altavista Co-Founder & CTO) & Gregory Renard (CTO & Artificial Intelligence Lead Architect at Xbrain) for Deep Learning keynote #0 at Holberton School (http://www.meetup.com/Holberton-School/events/228364522/)
If you want to assist to similar keynote for free, checkout http://www.meetup.com/Holberton-School/
Here are some key terms that are similar to "champagne":
- Sparkling wines
- French champagne
- Cognac
- Rosé
- White wine
- Sparkling wine
- Wine
- Burgundy
- Bordeaux
- Cava
- Prosecco
Some specific champagne brands that are similar terms include Moët, Veuve Clicquot, Dom Pérignon, Taittinger, and Bollinger. Grape varieties used in champagne production like Chardonnay and Pinot Noir could also be considered similar terms.
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Computer Science interviews are a different breed from other interviews and, as such, require specialized skills and techniques. Cracking the Technical Interview will teach you how to prepare for technical interviews, what top companies like Google and Microsoft really look for, and how to tackle the toughest programming and algorithm problems. This talk will include stories from the speaker's extensive interviewing experience as well as a live "demo" of how to tackle a technical problem.
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If there is time at the end of the talk, I will walk through two coding examples, using the HMS Titanic Passenger List, present with Python scikit-learn using algorithm random-trees to check if ML can correctly predict passenger survival and with R programming for feature engineering of the same dataset
Note to data-scientists and programmers: If you sign up to attend, plan to visit my Github repository! I have many Machine Learning coding examples in Python scikit-learn, GNU Octave, and R Programming.
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2. getting started
can ML predict the questions that will come in the test
next week ?? … just asking for a friend …
3. What is Machine Learning?
The art and science of solving ill-understood tasks
RJ23PA2262 Rajasthan
RJ23SM2824 Rajasthan
UP14DJ1422 Uttar Pradesh
UP67AA3601 Uttar Pradesh
DL7CM0750 Delhi
DL8CAE4892 Delhi
“
“
License plate images courtesy oyelecoupons.com and platerecognizer.com
AP Andhra Pradesh
BR Bihar
DL Delhi
GA Goa
KA Karnataka
MH Maharashtra
RJ Rajasthan
UP Uttar Pradesh
A WELL-UNDERSTOOD TASK AN ILL-UNDERSTOOD TASK
4. What is Machine Learning?
The art and science of solving ill-understood tasks
“
“
Bangla font images courtesy fontmeme.com
L
U
P
R
J
D
C
Big deal! humans
can do this just fine!
It is kind of a big deal.
Remember, we want to
write code to do this
নমস্কার
Are you sure
even humans
would be able
to do this for
scripts they
don’t know?
5. What is Machine Learning?
Sorting: given 𝑛 numbers, sort them in
decreasing order of their value
Recommendation: given 𝑛 items, for
each user, sort the items in decreasing
order of how much the user likes them
“
“
The art and science of solving ill-understood tasks
4
1
5
9
3
7
2
INPUT
9
7
5
4
3
2
1
OUTPUT
5
-6
4
-3
-2
1
0
INPUT
5
4
1
0
-2
-3
-6
OUTPUT
A WELL-UNDERSTOOD TASK AN ILL-UNDERSTOOD TASK
6. Exercise
Come up with at least one pair of activities that you do regularly
(lets call them A1 and A2) such that
o For A1 you can specify a very clear procedure to perform that activity
E.g. calling someone on the mobile phone
o For A2 it is difficult for even you to articulate a clear procedure
E.g. choosing between tea and coffee at breakfast
7. Why is it called “Machine” Learning?
4
1
5
9
3
7
2
INPUT
9
7
5
4
3
2
1
OUTPUT
Code
Bubble Sort, Quick Sort, Heap Sort, etc.
Written by a human coder
The “Machine”
The non-ML way to solve a well-understood task
8. Why is it called “Machine” Learning?
Code
ML Algorithm
Written by a human ML expert
The “Machine”
The ML way to solve an ill-understood task
Code
ML model
Produced automatically by the ML algo
TRAINING
TESTING
TRAINING DATA
9. Under the Hood – how ML works
Nature is governed by laws
Humans are sometimes able to discover these
However, it usually takes a lot of effort
More importantly, a lot of time (years/decades)
Others are
… well …
Some are
concise and
elegant
𝐸 = 𝑚𝑐2
businessinsider.com
THE STANDARD MODEL
10. Under the Hood – how ML works
Even ML works by discovering laws
Inspect data and discover laws/patterns that seem to explain data well
FEMALE, 25-30 YRS, HIGH INCOME ⇒
MALE, 18-25 YRS, MID INCOME ⇒
MALE, 30-45 YRS, LOW INCOME ⇒
= ≈ + ⇒ P
= ≈ + ⇒ L
Remember, this is just
an illustrative example.
Not all “laws” learnt by
ML look like this
Indeed! In fact, most
patterns and laws learnt
by ML are too complex to
be interpreted easily
ML is able to discover
more complex laws
more quickly than
humans
12. Summary
Machine learning is most suitable to solve tasks where
Humans cannot specify a clear, concise procedure to solve the task
There is too much diversity or variety in the task
There is a need to automate the task given its volume
Machine learning works by analyzing data to identity laws or patterns
that seem to explain the data well
The laws learnt by ML may be too complex to be interpreted by humans
Machine learning has seen innovative and impactful applications in
several areas but many more are waiting to be discovered
Could be by you!