Mushroom is one of the fungi types’ food that has the most potent nutrients on the plant.
Mushrooms have major medical advantages such as killing cancer cells.
How Python can be used for machine learning?NexSoftsys
I would suggest you can use the python code for machine learning algorithms, in this presentation to easily implement and explore code in your projects.
Read more https://www.slideshare.net/nexsoftsys/why-do-we-use-python-and-ml-ai
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.
Meetup sthlm - introduction to Machine Learning with demo casesZenodia Charpy
Data science and Machine Learning
Machine Learning vs Artificial Intelligence
Machine Learning Algorithms
How to choose ML algorithm mindmap
Supervised Learning generic flow
Unsupervised Learning generic flow
Example cases for supervised and unsupervised learning
Mushroom is one of the fungi types’ food that has the most potent nutrients on the plant.
Mushrooms have major medical advantages such as killing cancer cells.
How Python can be used for machine learning?NexSoftsys
I would suggest you can use the python code for machine learning algorithms, in this presentation to easily implement and explore code in your projects.
Read more https://www.slideshare.net/nexsoftsys/why-do-we-use-python-and-ml-ai
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.
Meetup sthlm - introduction to Machine Learning with demo casesZenodia Charpy
Data science and Machine Learning
Machine Learning vs Artificial Intelligence
Machine Learning Algorithms
How to choose ML algorithm mindmap
Supervised Learning generic flow
Unsupervised Learning generic flow
Example cases for supervised and unsupervised learning
This presentation covers an overview of Analytics and Machine learning. It also covers the Microsoft's contribution in Machine learning space. Azure ML Studio, a SaaS based portal to create, experiment and share Machine Learning Solutions to the external world.
This knolx is about an introduction to machine learning, wherein we see the basics of various different algorithms. This knolx isn't a complete intro to ML but can be a good starting point for anyone who wants to start in ML. In the end, we will take a look at the demo wherein we will analyze the FIFA dataset going through the understanding of various data analysis techniques and use an ML algorithm to derive 5 players that are similar to each other.
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
Expert Session delivered during Workshop on
Image Processing and Machine Learning for Pattern Recoginition on 11th July 2016 at
University Institute of Engineering and Technology, Chandigarh
List of top Machine Learning algorithms are making headway in the world of data science. Explained here are the top 10 of these machine learning algorithms - https://www.dezyre.com/article/top-10-machine-learning-algorithms/202
In the past few years, India has witnessed exponential growth in the sector of Data Science. With the advent of digital transformation in businesses, the demand for data scientists is boosting every day with a ton of job opportunities machine learning course in mumbai’machine learning course in mumbais lying in their path. Boston Institute of Analytics provides data science courses in Mumbai. They train students under experienced industry professionals and make them industry ready. To know more about their courses check out their website https://www.biaclassroom.com/courses.
This presentation covers an overview of Analytics and Machine learning. It also covers the Microsoft's contribution in Machine learning space. Azure ML Studio, a SaaS based portal to create, experiment and share Machine Learning Solutions to the external world.
This knolx is about an introduction to machine learning, wherein we see the basics of various different algorithms. This knolx isn't a complete intro to ML but can be a good starting point for anyone who wants to start in ML. In the end, we will take a look at the demo wherein we will analyze the FIFA dataset going through the understanding of various data analysis techniques and use an ML algorithm to derive 5 players that are similar to each other.
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
Expert Session delivered during Workshop on
Image Processing and Machine Learning for Pattern Recoginition on 11th July 2016 at
University Institute of Engineering and Technology, Chandigarh
List of top Machine Learning algorithms are making headway in the world of data science. Explained here are the top 10 of these machine learning algorithms - https://www.dezyre.com/article/top-10-machine-learning-algorithms/202
In the past few years, India has witnessed exponential growth in the sector of Data Science. With the advent of digital transformation in businesses, the demand for data scientists is boosting every day with a ton of job opportunities machine learning course in mumbai’machine learning course in mumbais lying in their path. Boston Institute of Analytics provides data science courses in Mumbai. They train students under experienced industry professionals and make them industry ready. To know more about their courses check out their website https://www.biaclassroom.com/courses.
Ethical Considerations in the Design of Artificial IntelligenceJohn C. Havens
A presentation for IEEE's Ethics Symposium happening in Vancouver, May 2016. Featuring presentations from John C. Havens, Mike Van der Loos, John P. Sullins, and Alan Mackworth.
"AI is “our greatest existential threat…”
“I’m increasingly inclined to think that there should be some regulatory oversight, maybe at the national and international level, just to make sure that we don’t do something very foolish.”
“I think there is potentially a dangerous outcome there.” (referring to Google’s Deep Mind which he invested in to keep an eye on things)."
Elon Musk
The Ethics of Machine Learning/AI - Brent M. EastwoodWithTheBest
Artificial Intelligence and machine learning has the potential for greater good and danger itself. Eastwood questions the ethics behind machine learning and artificial intelligence.
Brent M. Eastwood, PhD
Quem está estudando para concursos sabe o quanto é difícil decorar fórmulas, então aqui vai um resumo das fórmulas de Geometria Espacial. Aproveitem e bons estudos a todos!
The session is about creating, training, evaluating and deploying machine learning with no-code approach using Azure AutoML.
* NO MACHINE LEARNING EXPERIENCE REQUIRED *
Agenda:
1. Introduction to Machine Learning
2. What is AutoML (Automated Machine Learning) ?
3. AutoML versus Conventional ML practices
4. Intro to Azure Automated Machine Learning
5. Hands-on demo
6 Contest
6. Learning resources
7. Conclusion
Xavier Amatriain, VP of Engineering, Quora at MLconf SF - 11/13/15MLconf
10 More Lessons Learned from Building Real-Life ML Systems: A year ago I presented a collection of 10 lessons in MLConf. These goal of the presentation was to highlight some of the practical issues that ML practitioners encounter in the field, many of which are not included in traditional textbooks and courses. The original 10 lessons included some related to issues such as feature complexity, sampling, regularization, distributing/parallelizing algorithms, or how to think about offline vs. online computation.
Since that presentation and associated material was published, I have been asked to complement it with more/newer material. In this talk I will present 10 new lessons that not only build upon the original ones, but also relate to my recent experiences at Quora. I will talk about the importance of metrics, training data, and debuggability of ML systems. I will also describe how to combine supervised and non-supervised approaches or the role of ensembles in practical ML systems.
Machine learning: A Walk Through School ExamsRamsha Ijaz
When it comes to studying, Machines and Students have one thing in common: Examinations. To perform well on their final evaluations, humans require taking classes, reading books and solving practice quizzes. Similarly, machines need artificial intelligence to memorize data, infer feature correlations, and pass validation standards in order to solve almost any problem. In this quick introductory session, we'll walk through these analogies to learn the core concepts behind Machine Learning, and why it works so well!
The talk is on How to become a data scientist. This was at 2ns Annual event of Pune Developer's Community. It focuses on Skill Set required to become data scientist. And also based on who you are what you can be.
How to use Artificial Intelligence with Python? EdurekaEdureka!
YouTube Link: https://youtu.be/7O60HOZRLng
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
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Similar to Machine Learning: Inteligencia Artificial no es sólo un tema de Ciencia Ficción by Raul Garreta (20)
Azure: un parque de diversiones en la nube para el desarrollador moderno by A...
Machine Learning: Inteligencia Artificial no es sólo un tema de Ciencia Ficción by Raul Garreta
1. Machine Learning: Artificial
Intelligence isn't just a Science
Fiction topic
Raul Garreta - Tryolabs / MonkeyLearn
2. My Credentials
● Computer Science Engineer from Udelar,
Msc in Machine Learning + NLP
● Co-Founder, CTO & Product Manager at
Tryolabs.
● Co-Founder at MonkeyLearn.
● Professor in ML at InCo, Udelar.
● Co-authored "Learning Scikit-learn:
Machine Learning in Python"
3. Contents
● Brief intro to AI & Machine Learning (ML)
● ML Applications
● Cloud ML tools
4. What is AI?
From a behavioral point of view, is an artificial
agent that shows certain characteristics of
intelligence like:
● Reasoning
● Knowledge representation
● Learning
● Planning
● Perception
5. What is AI?
Behavioral test = Turing Test
If I write an enough complex If-then-
else structure, could it
pass the test?
Random behavior?
6. Different fields within AI
Artificial Intelligence
● General Artificial Intelligence
● Expert Systems
○ Natural Language Processing
○ Computer Vision
○ Machine Learning
○ ...
7. Machine Learning
Algorithms that allow computers
to automatically learn to perform
a task from data.
Can improve their performance
over time, by adding more data.
8. Machine Learning Definitions
Arthur Samuel (1959): "Field of study that gives computers
the ability to learn without being explicitly programmed"
Tom Mitchell (1997): "A computer program is said to learn
if its performance at a task T, as measured by a
performance P, improves with experience E"
9. Machine Learning Algorithms
● Learn to associate a particular input (set of
features) to a particular output (class,
number or group of instances)
● That is the process of training a ML model.
● And use the learned model to predict the
outcome on new instances
10. Inputs: Instances
Usually we have instances of data that
represent objects: documents, images, users,
etc.
And can be represented by a set of features:
● A document is represented by a set of words.
● An image is represented by a set of pixels.
● A user can be represented by the age, level of
education, gender, interests, etc.
11. Machine Learning Problems
Classification: assign a label (class)
to a set of items.
Regression: assign a number
(evaluation) to a set of items
Clustering: group items into clusters
according to a similarity measure
12. Type of Machine Learning
Algorithms
Linear Models Decision Trees
13. Type of Machine Learning
Algorithms
Probabilistic /
Statistical Models
Neural Networks /
Deep Learning
14.
15. Important Concepts in ML
Besides the Machine Learning…
● Data gathering / importation
● Data preprocessing
● Feature extraction
● Feature selection
● Performance evaluation (testing)
20. Why use Machine Learning?
● Solve problems that manually would be extremely
difficult or impossible.
● Make predictions.
● Automatically process huge amounts of information and
sources: big data.
● Intelligent apps => improve UX => improve conversion
rates => $$$
● Great companies use it...
21. Why use a Cloud Saas ML platform?
● Avoid to deploy and maintain the full stack.
● Be cross platform.
● Not all programming languages have ML
tools.
● ML requires huge amounts of computer
power.
● Just solve it: good, fast, easy.
22. Machine Learning Platforms
As with other problems (eg: payments,
communications) is a trend to go SaaS.
Machine Learning
23. Microsoft Azure ML
● http://azure.microsoft.com/en-us/
services/machine-learning/
● Launched preview version on June 2014.
● Cloud based ML platform to build predictive
numerical applications.
● Technologies used in Xbox and Bing.
Machine Learning
24. Microsoft Azure ML
● Easy to scale, Azure infrastructure.
● Users can build custom R modules.
● GUI and APIs.
● More oriented to Data Scientists.
● Pricing: pay as you go.
Machine Learning
25.
26. MonkeyLearn
● http://monkeylearn.com/
● Launched private alpha on April 2014
● Cloud based, focused on Text Mining:
extract and classify information from text.
27. MonkeyLearn
● Easy to use.
● Pre-trained modules for different
applications.
● GUI and APIs.
● More oriented to developers.
● Pricing: freemium, pay as you go.
28.
29. Conclusions
● Machine Learning can allow
us to make intelligent apps.
● It's a trendy topic…
● New ML platforms are
emerging, allowing any
developer to incorporate ML
technologies.