Artificial intelligence is more and more becoming the core of digital products. Designing for Products based on AI requires Designers to know about Machine Learning.
This talk is an easy walk through the most important elements of Machine Learning. It looks at the fundamental principles of using practical examples. It showcases applications of the different types of Machine Learning. The use-cases range from text categorization to image recognition, on to speech analysis. The goal is to show what is important for designers and why.
Artificial intelligence is more and more becoming the core of digital products. Designing for Products based on AI requires Designers to know about Machine Learning.
This talk is an easy walk through the most important elements of Machine Learning. It looks at the fundamental principles of using practical examples. It showcases applications of the different types of Machine Learning. The use-cases range from text categorization to image recognition, on to speech analysis. The goal is to show what is important for designers and why.
Machine Learning for Designers - UX ScotlandMemi Beltrame
Artificial intelligence is more and more becoming the core of digital products. Designing for Products based on AI requires Designers to know about Machine Learning.
This talk is an easy walk through the most important elements of Machine Learning. It looks at the fundamental principles of using practical examples. It showcases applications of the different types of Machine Learning. The use-cases range from text categorization to image recognition, on to speech analysis. The goal is to show what is important for designers and why.
Artificial intelligence is more and more becoming the core of digital products. Designing for Products based on AI requires Designers to know about Machine Learning.
This talk is an easy walk through the most important elements of Machine Learning. It looks at the fundamental principles of using practical examples. It showcases applications of the different types of Machine Learning. The use-cases range from text categorization to image recognition, on to speech analysis. The goal is to show what is important for designers and why.
Machine Learning for Designers - DX Meetup BaselMemi Beltrame
Artificial intelligence is more and more becoming the core of digital products. Designing for Products based on AI requires Designers to know about Machine Learning.
This talk is an easy walk through the most important elements of Machine Learning. It looks at the fundamental principles of using practical examples. It showcases applications of the different types of Machine Learning. The use-cases range from text categorization to image recognition, on to speech analysis. The goal is to show what is important for designers and why.
This presentation is the part of the webinar conducted by CloudxLab. This was the free session on Machine Learning.
Cloudxlab conducts such webinars very frequently and to make sure you never miss the future webinar update, please see the 'Events' section at CloudxLab.com
Artificial intelligence is more and more becoming the core of digital products. Designing for Products based on AI requires Designers to know about Machine Learning.
This talk is an easy walk through the most important elements of Machine Learning. It looks at the fundamental principles of using practical examples. It showcases applications of the different types of Machine Learning. The use-cases range from text categorization to image recognition, on to speech analysis. The goal is to show what is important for designers and why.
Machine Learning for Designers - UX ScotlandMemi Beltrame
Artificial intelligence is more and more becoming the core of digital products. Designing for Products based on AI requires Designers to know about Machine Learning.
This talk is an easy walk through the most important elements of Machine Learning. It looks at the fundamental principles of using practical examples. It showcases applications of the different types of Machine Learning. The use-cases range from text categorization to image recognition, on to speech analysis. The goal is to show what is important for designers and why.
Artificial intelligence is more and more becoming the core of digital products. Designing for Products based on AI requires Designers to know about Machine Learning.
This talk is an easy walk through the most important elements of Machine Learning. It looks at the fundamental principles of using practical examples. It showcases applications of the different types of Machine Learning. The use-cases range from text categorization to image recognition, on to speech analysis. The goal is to show what is important for designers and why.
Machine Learning for Designers - DX Meetup BaselMemi Beltrame
Artificial intelligence is more and more becoming the core of digital products. Designing for Products based on AI requires Designers to know about Machine Learning.
This talk is an easy walk through the most important elements of Machine Learning. It looks at the fundamental principles of using practical examples. It showcases applications of the different types of Machine Learning. The use-cases range from text categorization to image recognition, on to speech analysis. The goal is to show what is important for designers and why.
This presentation is the part of the webinar conducted by CloudxLab. This was the free session on Machine Learning.
Cloudxlab conducts such webinars very frequently and to make sure you never miss the future webinar update, please see the 'Events' section at CloudxLab.com
Introduction to Deep Learning | CloudxLabCloudxLab
( Machine Learning & Deep Learning Specialization Training: https://goo.gl/goQxnL )
This CloudxLab Deep Learning tutorial helps you to understand Deep Learning in detail. Below are the topics covered in this tutorial:
1) What is Deep Learning
2) Deep Learning Applications
3) Artificial Neural Network
4) Deep Learning Neural Networks
5) Deep Learning Frameworks
6) AI vs Machine Learning
BIG DATA AND MACHINE LEARNING
Big Data is a collection of data that is huge in volume, yet growing exponentially with time. It is a data with so large size and complexity that none of traditional data management tools can store it or process it efficiently. Big data is also a data but with huge size.
The term Machine Learning was coined by Arthur Samuel in 1959, an american pioneer in the field of computer gaming and artificial intelligence and stated that “ it gives computers the ability to learn without being explicitly programmed” And in 1997, Tom Mitchell gave a “ well-Posed” mathematical and relational definition that “ A Computer Program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E”.
Machine learning is needed for tasks that are too complex for humans to code directly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve.
In This Data Science course ( Graduate Program ) I will focus on understanding business intelligence systems and helping future managers use and understand analytics, Business Intelligence emphasizing the applications and implementations behind the concepts. a solid foundation of BI that is reinforced with hands-on practice. The course is also designed as an introduction to programming and statistics for students from many different majors. It teaches practical techniques that apply across many disciplines and also serves as the technical foundation for more advanced courses in data science, statistics, and computer science.
What Is Data Science? | Introduction to Data Science | Data Science For Begin...Simplilearn
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This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
As a speaker at San Jose Hadoop Summit 2015, presented the principles for Self Evolving Models for Dynamic System Accuracy.The theme of the topic is streaming and machine learning.
[moved to my rekhajoshm official slideshare account; with side effects of loss of stats]
Machine learning applications nurturing growth of various business domainsShrutika Oswal
Machine learning is a science in which machines are becoming smarter and helping humans to make the best decisions based on previous data recommended practices. This technique is not new but is occupying fresh momentum. Machine Learning Algorithm learns from the previous records and analyses the data. Without any human interrupt, it will generate its own recommendation. A machine will add that recommendation as experience in its database and use it for further processing. In short, the machine learns from its own experience and gives you better and better output.
Machine learning is an iterative process as the more data added to machines learn from fresh feeds of data and then independently adapt new features to handle new data without constant human intervention. Machine learning was earlier used to predict what’s happing with the business but now the machine learning algorithm will suggest what action needs be taken by moving our business forward.
This PowerPoint presentation presents the results of a literature survey of machine learning applications nurturing the growth of various business domains. More specifically, it gives a brief introduction of Machine Learning, four major types of Machine Learning, enhancement in various business domains by the use of various machine learning algorithms.
If you have heard about machine learning and want to try out some of it, please check this out. In this article I am just trying to jot down few basics and must know stuff to kick start in this field. The objective of this compilation; to trigger the interest in this field of data analytics and to demystify the abstract concept. This article is not for the advanced data scientists, this is for the beginners or those who want a quick refresher.
Zero Adoption: Lessons Learned From Failing at Open SourceMemi Beltrame
I'd love to tell you a story about how the software I created helped my community. Sadly, I can't: nothing I built ever found an audience. This talk is about how I failed to reach a community, about why it doesn't matter - or rather: what I learned from being stuck in an open source team of one.
For years I was convinced that the success of an open source project was determined by the usefulness of the software. My imaginary blueprint of open sourcing was:
Build something useful
Open source it
Everybody wins
It turns out that it is much harder than that.
This talk is about how I built several tools that would help the UX community to deliver awesome products with a great experience, while never finding an audience for the tools. We'll look at all the mistakes one can make and what to do instead to build a thriving community.
And even if you don't find an audience: Zero adoption does not mean zero value. We'll look at how there is great benefit in building and publishing things, if not for others then for yourselves.
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In This Data Science course ( Graduate Program ) I will focus on understanding business intelligence systems and helping future managers use and understand analytics, Business Intelligence emphasizing the applications and implementations behind the concepts. a solid foundation of BI that is reinforced with hands-on practice. The course is also designed as an introduction to programming and statistics for students from many different majors. It teaches practical techniques that apply across many disciplines and also serves as the technical foundation for more advanced courses in data science, statistics, and computer science.
What Is Data Science? | Introduction to Data Science | Data Science For Begin...Simplilearn
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6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
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5. Experienced professionals who would like to harness data science in their fields
As a speaker at San Jose Hadoop Summit 2015, presented the principles for Self Evolving Models for Dynamic System Accuracy.The theme of the topic is streaming and machine learning.
[moved to my rekhajoshm official slideshare account; with side effects of loss of stats]
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Machine learning is a science in which machines are becoming smarter and helping humans to make the best decisions based on previous data recommended practices. This technique is not new but is occupying fresh momentum. Machine Learning Algorithm learns from the previous records and analyses the data. Without any human interrupt, it will generate its own recommendation. A machine will add that recommendation as experience in its database and use it for further processing. In short, the machine learns from its own experience and gives you better and better output.
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Build something useful
Open source it
Everybody wins
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This presentation was made to help designers who work in publishing houses or format books for printing ensure quality.
Quality control is vital to every industry. This is why every department in a company need create a method they use in ensuring quality. This, perhaps, will not only improve the quality of products and bring errors to the barest minimum, but take it to a near perfect finish.
It is beyond a moot point that a good book will somewhat be judged by its cover, but the content of the book remains king. No matter how beautiful the cover, if the quality of writing or presentation is off, that will be a reason for readers not to come back to the book or recommend it.
So, this presentation points designers to some important things that may be missed by an editor that they could eventually discover and call the attention of the editor.
7. otoscope
This is an
It can be used to look at the
eardrum to see if the ear is inflamed.
Because the otoscope is connected
to an iPhone, an image can be taken
of the eardrum.
8.
9. The image is sent to a service that tells me if I should go to a doctor or not.
16. 3 methods how machines learn
Supervised learning You train the machine with data
The machine learns to make predictions
✔ ❌
17. 3 methods how machines learn
Supervised learning You train the machine with data
The machine learns to make predictions
#1 method used in machine learning
18. 3 methods how machines learn
Supervised learning You train the machine with data
The machine learns to make predictions
Unsupervised learning The machine is given a lot of data and it
uses algorithms to find out interesting
patterns.
19. Let's get some pizza data
1 2 3 4 5 6
1
2
3
4
5
6
7
Average # of pizzas per week
Average # of toppings
per pizza
20. Find patterns
1 2 3 4 5 6
1
2
3
4
5
6
7
Average # of toppings
per pizza
Average # of pizzas per week
21. Find patterns
1 2 3 4 5 6
1
2
3
4
5
6
7
Average # of toppings
per pizza
Average # of pizzas per week
You can run this
data through an
algorithm and it
would find groups of
items that are close
together,
22. Take Action
1 2 3 4 5 6
1
2
3
4
5
6
7
Average # of toppings
per pizza
Average # of pizzas per week
23. Take Action
1 2 3 4 5 6
1
2
3
4
5
6
7
Average # of toppings
per pizza
Average # of pizzas per week
With these groups you now can direct address the different groups
The group on the top right probably are big households you can target
specifically
The group on the left are those that order less frequently so you could address
this and offer a super tuesday for those that don't order on that day
The last one is for the people that love boring pizza: give them what they want,
but larger!
The applications of this clustering by unsupervised learning are market
segmentation or fraud detection in banking
24. 3 methods how machines learn
Supervised learning You train the machine with data
The machine learns to make predictions
Unsupervised learning The machine is given a lot of data and it
uses algorithms to find out interesting
patterns.
Reinforcement learning The machine continuously learns from the
environment in an iterative fashion.
It starts dumb and gets smarter.
25. Reinforcement Learning
The machine is given a
set of rules and a goal
• Physics: Gravity etc
• Wheels turn
• Goal get farther than
previous cars
It trains itself by keeping
the features that helped
it reach the goal.
BoxCar 2D: Computation Intelligence Car Evolution (Needs Flash) http://boxcar2d.com/
27. Reinforcement Learning
After a few dozen
generations the
machine has succeded
in creating a vehicle
that looks like a car and
can reliably drive
28. #1 method: supervised learning
Bedrooms m2 Neighbourhood Floors Sale Price
4 96 Hipsterton 2 1’500’000
2 89 Snoringham 3 750’000
3 75 Hipsterton 1 1’200’000
3 79 Snoringham 2 820’000
• Give the machine
a training set with
features
• Give it the target
values
• It figures out how
important each
feature is
• The machine can
make predictions
of target values
Features Target
29. #1 method: supervised learning
Bedrooms m2 Neighbourhood Floors
4 96 Hipsterton 2
2 89 Snoringham 3
3 75 Hipsterton 1
3 79 Snoringham 2
Predictions improve with
• more features
• larger learning sample
Features
34. how machines use algorithms
1. Take a lot of training data
2. Pass it through a generic algorithm
(some mathematical formula)
3. Let the machine figure out its own
logic based on the data.
Emails
Generic Machine
Learning Algorithm
Spam Not Spam
35. how machines use algorithms
500g white flour,
2 tsp salt
7g fast-action yeast
3 tbsp olive oil
300ml water
475g plain flour,
1 tsp salt
10g dried yeast
1 tbsp olive oil
400ml water
The algorithm finds the valid weights of the individual
features of a data-set to make the right prediction
2 cups flour,
1 cup salt
1 tsp olive oil
1 cup water
Bread Bread Salty play dough
36. generic algorithms
There are many generic
algorithms that already exist.
The same generic algorithm
can be used to solve
problems in completely
different areas.
Emails Algorithm
Spam
Not Spam
Articles Algorithm Finance
Politics
Sports
37. 2 types of algorithms
Classification algorithms
Emails Algorithm
Spam
Not Spam
The goal is to predict discrete
values, e.g. {1,0}, {True, False},
{spam, not spam}.
Regression algorithms
House-
Details
Algorithm
Price of
House
The goal is to predict continuous
values, e.g. home prices, weather
temperatures
A big part of ML
is about classification
41. is language like images?
Images can be
recognized
because their data
can be encoded
Can we do the same with language?
42. translation versus conversation
Do you have the time?
Translation goal:
Produce an equivalent
Conversation goal:
Understand the meaning
Avez-vous l’heure? It’s 7pm.Yes
45. statistical translation
I try | to run | at | the prettiest | open space.
I want | to run | per | the more tidy | open space.
I mean | to forget | at | the tidiest | beach.
I try | to go | per | the more tidy | seaside.
I want | to go | to | the prettiest | beach.
The algorithm compares the possible translations against existing ones.
The algorithm picks the translation with the highest probability.
49. new challenges and disciplines
• recognizing intent
• understanding context
• voice and tone
• shaping conversations in a
humane and ethical way
}Linguistics
Ethics
50. intent - what does it all mean?
types of meaning
understand the wordsliteral:
understand the actual meaningimplied:
Do you have the time?
metaphors & metonymiesreferenced:
Wall Street is in crisis
51.
52. Elements that make
this artificial:
• Not picking up intent
„give me a spot on saturday“
• Literal repetition
53. context
context is even harder than intent
• the sequence in time
• understanding the surroundings
• semantic context
homonymy: " is not a #
54. voice and tone: change registers
we adapt the way we speak to the
situation we’re in
Depending on:
• how serious the situation is
• how formal it is
• how we are connected to the person
Conversational interfaces need to take
this into account.
This is a design task
Yes
Sporty
Neutral
Date Night
Ready for your style?
How would you describe your style?
I'd totally raid your closet...
Sporty is my style!
Do you wear colors or nah?
Fab, I bet you look great in everything!
Where are you going in your hot new
outfit?
56. Designers are content experts
Icons by Sarah Rudkin
Developers
Build the machine
Domain experts
Have the domain
specific knowledge
Designers
• Content oversight for training:
What makes good training data?
• Mediator between engineering and domain
experts
• Ethical considerations
57. ethics matter
Machines learn from us
We choose what to teach
We need to
• challenge and stress test from a diverse
point of view
• put humans before technology
(once again)
• bring our principles of what good
design is to the AI world
This is a design task
58. Machine Learning is
everywhere
Learn to see its opportunities
Get a seat at the table now
Understand the implications
of using machine learning
Bring Design principles into the
mix to make empowering and
ethical products
60. Resources
A visual introduction to machine learning
http://www.r2d3.us
Machine Learning is Fun!
(the perfect series of articles to get you started)
https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471
30 Free Courses: Neural Networks, Machine Learning, AI
https://www.datasciencecentral.com/profiles/blogs/neural-networks-for-machine-learning
Watson Knowledge Studio
https://www.ibm.com/watson/developercloud/doc/wks/wks_overview_full.shtml
2 Minutes Papers: a youtube channel dedicated to condensing the results of scientific papers on artificial intelligence.
https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg
Realtime Multi-Person 2D Human Pose Estimation
https://www.youtube.com/watch?v=pW6nZXeWlGM
BoxCar 2D: Computation Intelligence Car Evolution (Needs Flash)
http://boxcar2d.com/
Google AI Experiments
https://experiments.withgoogle.com/collection/ai
Differences Between AI and Machine Learning, and Why it Matters
https://medium.com/datadriveninvestor/differences-between-ai-and-machine-learning-and-why-it-matters-1255b182fc6