Machine Learning: Real life business application
•Download as PPTX, PDF•
0 likes•226 views
We will explore the basic idea behind machine learning, the steps to apply machine learning, and some of the supervised and unsupervised models. We will focus on classification models. We also talk about algorithms: HMM, Deep Neural Networks, Advanced Matrix Factorization, randomized algorithms, NLP/text mining, Ad-auction, web mining, data visualization tools, quantitative investment, marketing analytics, ML Code development. We will go over a real life business application at a major institution and touch on key practical lessons learned for machine learning. The talk will address know how from a practitioner’s point of view.
Report
Share
Report
Share
Recommended
Learning to learn Model Behavior: How to use "human-in-the-loop" to explain d...
Learning to learn Model Behavior: How to use "human-in-the-loop" to explain d...IDEAS - Int'l Data Engineering and Science Association
The document discusses model interpretation and the Skater library. It begins with defining model interpretation and explaining why it is needed, particularly for understanding model behavior and ensuring fairness. It then introduces Skater, an open-source Python library that provides model-agnostic interpretation tools. Skater uses techniques like partial dependence plots and LIME explanations to interpret models globally and locally. The document demonstrates Skater's functionality and discusses its ability to interpret a variety of model types.Module 4: Model Selection and Evaluation
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
Critical Thinking and Design Thinking
Critical thinking and design thinking both involve gathering information and analyzing it to solve problems. They are both higher-order thinking processes that are continuous and iterative. Both approaches involve defining the problem, ideating potential solutions, prototyping ideas, and testing and validating solutions to arrive at better outcomes with each step of the process. The key difference is that design thinking emphasizes empathy for users and their needs.
Module 1.2 data preparation
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
Module 5: Decision Trees
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
Intro to modelling-supervised learning
This document provides an introduction to machine learning concepts. It defines machine learning as allowing computers to learn without being explicitly programmed. Two main types are described: supervised learning, where the goal is to predict known outputs from inputs, and unsupervised learning, where patterns in unknown data are identified. Supervised learning is further divided into classification and regression problems. Example algorithms covered include k-nearest neighbors, decision trees, and linear regression. Key concepts like bias, variance, and dimensionality are also introduced.
Actionable Machine Learning
This document discusses using machine learning to optimize future outcomes rather than just predict them. It explains that randomized studies are needed to accurately predict the effects of actions, but sometimes this is not possible with observational data alone. The document proposes techniques like transfer learning, common support analysis, and generative adversarial networks to help evaluate strategies without randomized trials by expanding the available data.
Introduction to ml
This document provides an introduction to machine learning concepts including supervised and unsupervised learning, regression, classification, features, weights and bias, and linear regression. It defines machine learning as computers learning without being explicitly programmed and discusses common machine learning applications. Key machine learning types are outlined including supervised learning using labeled data for predictions, unsupervised learning with unlabeled data, deep learning using neural networks, and reinforcement learning using rewards. Regression is described as determining relationships among variables to predict quantities, using housing price prediction as an example. Linear regression for fitting a linear model to data is covered in more detail, discussing loss functions, gradient descent, and using Python code examples.
Recommended
Learning to learn Model Behavior: How to use "human-in-the-loop" to explain d...
Learning to learn Model Behavior: How to use "human-in-the-loop" to explain d...IDEAS - Int'l Data Engineering and Science Association
The document discusses model interpretation and the Skater library. It begins with defining model interpretation and explaining why it is needed, particularly for understanding model behavior and ensuring fairness. It then introduces Skater, an open-source Python library that provides model-agnostic interpretation tools. Skater uses techniques like partial dependence plots and LIME explanations to interpret models globally and locally. The document demonstrates Skater's functionality and discusses its ability to interpret a variety of model types.Module 4: Model Selection and Evaluation
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
Critical Thinking and Design Thinking
Critical thinking and design thinking both involve gathering information and analyzing it to solve problems. They are both higher-order thinking processes that are continuous and iterative. Both approaches involve defining the problem, ideating potential solutions, prototyping ideas, and testing and validating solutions to arrive at better outcomes with each step of the process. The key difference is that design thinking emphasizes empathy for users and their needs.
Module 1.2 data preparation
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
Module 5: Decision Trees
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org.
Intro to modelling-supervised learning
This document provides an introduction to machine learning concepts. It defines machine learning as allowing computers to learn without being explicitly programmed. Two main types are described: supervised learning, where the goal is to predict known outputs from inputs, and unsupervised learning, where patterns in unknown data are identified. Supervised learning is further divided into classification and regression problems. Example algorithms covered include k-nearest neighbors, decision trees, and linear regression. Key concepts like bias, variance, and dimensionality are also introduced.
Actionable Machine Learning
This document discusses using machine learning to optimize future outcomes rather than just predict them. It explains that randomized studies are needed to accurately predict the effects of actions, but sometimes this is not possible with observational data alone. The document proposes techniques like transfer learning, common support analysis, and generative adversarial networks to help evaluate strategies without randomized trials by expanding the available data.
Introduction to ml
This document provides an introduction to machine learning concepts including supervised and unsupervised learning, regression, classification, features, weights and bias, and linear regression. It defines machine learning as computers learning without being explicitly programmed and discusses common machine learning applications. Key machine learning types are outlined including supervised learning using labeled data for predictions, unsupervised learning with unlabeled data, deep learning using neural networks, and reinforcement learning using rewards. Regression is described as determining relationships among variables to predict quantities, using housing price prediction as an example. Linear regression for fitting a linear model to data is covered in more detail, discussing loss functions, gradient descent, and using Python code examples.
MachineLlearning introduction
Machine Learning (ML) gives computers the capability to learn without being explicitly programmed. It is a subset of artificial intelligence. We can use Machine Learning with Python Programming for Face Recognition/ Online customer support/ Product recommendations/ Online Fraud Detection/ Video Surveillance/ Social media services/ Refining search-engine results /Automatic Translation /Self-driving cars/ Virtual Personal Assistants/ Predicting Music Choices, etc.
Machine Learning_PPT.pptx
Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed. There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning uses labeled training data to infer a function that maps inputs to outputs, unsupervised learning looks for hidden patterns in unlabeled data, and reinforcement learning allows an agent to learn from interaction with an environment through trial-and-error using feedback in the form of rewards. Some common machine learning algorithms include support vector machines, discriminant analysis, naive Bayes classification, and k-means clustering.
Forms of learning in ai
Three main types of machine learning are supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a model using labeled input/output data where the desired outputs are provided, allowing the model to map inputs to outputs. Unsupervised learning involves discovering hidden patterns in unlabeled data and grouping similar data points together. Reinforcement learning involves an agent learning through trial-and-error interactions with a dynamic environment by receiving rewards or punishments for actions.
Sippin: A Mobile Application Case Study presented at Techfest Louisville
"Sippin: A Mobile Application Case Study," was presented at Techfest Louisville 2017 hosted by the Technology Association of Louisville Kentucky on Aug. 16th-17th.
Slides(ppt)
The document discusses machine learning and different types of learning problems. It begins by explaining that machine learning allows systems to learn knowledge that engineers may not know how to provide. It then describes several types of learning including supervised learning (prediction from labeled examples), clustering (finding natural groupings of unlabeled data), and reinforcement learning (learning from rewards/penalties). The document provides examples of different learning problems and algorithms like decision trees. It emphasizes that the goal of learning is to find patterns in data and make accurate predictions, especially for previously unseen examples.
Introduction to machine learning
In a world of data explosion, the rate of data generation and consumption is on the increasing side,
there comes the buzzword - Big Data.
Big Data is the concept of fast-moving, large-volume data in varying dimensions (sources) and
highly unpredicted sources.
The 4Vs of Big Data
● Volume - Scale of Data
● Velocity - Analysis of Streaming Data
● Variety - Different forms of Data
● Veracity - Uncertainty of Data
With increasing data availability, the new trend in the industry demands not just data collection but making an ample sense of acquired data - thereby, the concept of Data Analytics.
Taking it a step further to further make futuristic prediction and realistic inferences - the concept
of Machine Learning.
A blend of both gives a robust analysis of data for the past, now and the future.
There is a thin line between data analytics and Machine learning which becomes very obvious
when you dig deep.
housepriceprediction-180915174356.pdf
This document describes a machine learning app that predicts housing prices based on home specifications. It uses supervised learning algorithms like multiple linear regression to analyze collected housing data and predict prices. The tools used include Python, TensorFlow, Android Studio, and Anaconda. The model works by collecting data from various sources, training on that data, and then generating price predictions based on user-inputted home details. There is room for improving the model's accuracy by incorporating new data sources over time.
How to implement artificial intelligence solutions
The document provides an overview of how to implement artificial intelligence solutions. It discusses getting started in AI by either creating new techniques as a scientist or implementing existing techniques as an engineer. It then covers various machine learning algorithms like linear regression, decision trees, random forests, naive bayes, k-nearest neighbors, k-means, and support vector machines. Finally, it introduces deep learning concepts like artificial neural networks, neurons, layers, gradients, optimizers, overfitting, and regularization. The document serves as a guide for implementing both machine learning and deep learning techniques for AI applications.
Housing price prediction
A ppt based on predicting prices of houses. Also tells about basics of machine learning and the algorithm used to predict those prices by using regression technique.
Custom Vision.pptx
Introduction to CUSTOM VISION: Learn how to leverage the power of images to train a custom model for image classification using machine learning. This workshop covers the basics of computer vision and deep learning, and walks you through the process of creating a custom image classifier using the Custom Vision service. Discover how to annotate training data, train your model, and perform inference to classify images with high accuracy. Join us for a hands-on session and learn how to build your own custom vision solution!
Module 7: Unsupervised Learning
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org. If you would like to use this material to further our mission of improving access to machine learning. Education please reach out to inquiry@deltanalytics.org.
Brains@Bay Meetup: The Effect of Sensorimotor Learning on the Learned Represe...
- The representations learned through self-supervised exploration in an embodied agent encode meaningful information about concepts without being explicitly taught.
- Concepts can be learned from very few examples (above chance accuracy with one example) by analyzing neuron correlations across examples.
- This concept mapping approach does not require retraining or large labeled datasets, and has no problem with catastrophic forgetting, since it relies on the encoded representations.
AWS Certified Machine Learning Specialty
Amazon AWS Machine Learning Specialty Notes which covers Linear Regression, PCA, K-Means, Algorithms, Machine Learning Concepts, Regularisation, Optimisation, Unsupervised, Supervised, Reinforcement learning, Deep Racer, One Hot Encoding
Machine learning para tertulianos, by javier ramirez at teowaki
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
introduction to machine learning
This document provides an introduction to machine learning and intelligent agents. It defines machine learning as programs that automatically improve performance through experience without human assistance. Examples of machine learning problems include optical character recognition, spam filtering, and medical diagnosis. Learning models define the learning problem formally. Intelligent agents can perceive their environment and act upon it. Rational agents aim to perform useful actions. The document discusses properties of environments including observable vs partially observable. It outlines different types of agents from simple reflex agents to goal-based and utility-based agents.
SIP-REVIEW-3.pptx
This document contains summaries of multiple machine learning courses and modules. It introduces machine learning and the concepts of supervised and unsupervised learning. It also discusses reinforcement learning and the difference between exploration and exploitation in reinforcement learning models. Finally, it covers the basics of model learning, including defining deep network learning as an optimization problem and using gradient descent and stochastic gradient descent to solve this problem.
ML Study Jams - Session 3.pptx
This document summarizes a session on classification models. It discusses classifying data by learning classification rules from labeled input data. Specifically, it covers k-nearest neighbors classification, where the target point is assigned the majority class of its k nearest neighbors. It also discusses extending linear regression to classification by using it to generate real-valued outputs, then thresholding those values to classify instances into classes 0 or 1. However, it notes that logistic regression is a better approach, using a sigmoid function to convert real-valued outputs to a probability between 0 and 1 before thresholding for classification. The document highlights key considerations for classification like dealing with multiple classes and class imbalance. It also discusses evaluating classifiers, ensemble methods, and overfitting/under
Industrial training ppt
Hrjeet Singh completed a 42-day online industrial training from Internshala located in Gurgaon, India. During the training, Singh learned about machine learning concepts including classification, regression, linear regression, logistic regression, decision trees, and K-means clustering. Singh also completed a project using machine learning classifiers to detect breast cancer by analyzing features of breast cancer patient and normal cells.
machinecanthink-160226155704.pdf
This document provides an overview of machine learning. It begins with an introduction and discusses the basics, types (supervised, unsupervised, reinforcement learning), technologies, applications, and vision for the next few years. Key points covered include definitions of machine learning, examples of applications (search engines, spam filters, personalized recommendations), and descriptions of different problem types (classification, regression, clustering) and learning approaches (decision trees, neural networks, Bayesian methods).
Robotic models of active perception
This document discusses active perception in robots and humans. It notes that humans are able to perceive complex, unstructured environments by actively perceiving through mechanisms like saccades and attention. For robots to operate in dangerous environments, they will need to develop similar active perception abilities to cope with perceptual complexity. The document explores issues like where to direct attention, what to remember, and when to stop perceiving and start acting. It also examines how active perception affects learning, representation, and decision-making.
Large Language Model (LLM) and it’s Geospatial Applications
Large Language Model (LLM) and it’s Geospatial Applications.
More Related Content
Similar to Machine Learning: Real life business application
MachineLlearning introduction
Machine Learning (ML) gives computers the capability to learn without being explicitly programmed. It is a subset of artificial intelligence. We can use Machine Learning with Python Programming for Face Recognition/ Online customer support/ Product recommendations/ Online Fraud Detection/ Video Surveillance/ Social media services/ Refining search-engine results /Automatic Translation /Self-driving cars/ Virtual Personal Assistants/ Predicting Music Choices, etc.
Machine Learning_PPT.pptx
Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed. There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning uses labeled training data to infer a function that maps inputs to outputs, unsupervised learning looks for hidden patterns in unlabeled data, and reinforcement learning allows an agent to learn from interaction with an environment through trial-and-error using feedback in the form of rewards. Some common machine learning algorithms include support vector machines, discriminant analysis, naive Bayes classification, and k-means clustering.
Forms of learning in ai
Three main types of machine learning are supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a model using labeled input/output data where the desired outputs are provided, allowing the model to map inputs to outputs. Unsupervised learning involves discovering hidden patterns in unlabeled data and grouping similar data points together. Reinforcement learning involves an agent learning through trial-and-error interactions with a dynamic environment by receiving rewards or punishments for actions.
Sippin: A Mobile Application Case Study presented at Techfest Louisville
"Sippin: A Mobile Application Case Study," was presented at Techfest Louisville 2017 hosted by the Technology Association of Louisville Kentucky on Aug. 16th-17th.
Slides(ppt)
The document discusses machine learning and different types of learning problems. It begins by explaining that machine learning allows systems to learn knowledge that engineers may not know how to provide. It then describes several types of learning including supervised learning (prediction from labeled examples), clustering (finding natural groupings of unlabeled data), and reinforcement learning (learning from rewards/penalties). The document provides examples of different learning problems and algorithms like decision trees. It emphasizes that the goal of learning is to find patterns in data and make accurate predictions, especially for previously unseen examples.
Introduction to machine learning
In a world of data explosion, the rate of data generation and consumption is on the increasing side,
there comes the buzzword - Big Data.
Big Data is the concept of fast-moving, large-volume data in varying dimensions (sources) and
highly unpredicted sources.
The 4Vs of Big Data
● Volume - Scale of Data
● Velocity - Analysis of Streaming Data
● Variety - Different forms of Data
● Veracity - Uncertainty of Data
With increasing data availability, the new trend in the industry demands not just data collection but making an ample sense of acquired data - thereby, the concept of Data Analytics.
Taking it a step further to further make futuristic prediction and realistic inferences - the concept
of Machine Learning.
A blend of both gives a robust analysis of data for the past, now and the future.
There is a thin line between data analytics and Machine learning which becomes very obvious
when you dig deep.
housepriceprediction-180915174356.pdf
This document describes a machine learning app that predicts housing prices based on home specifications. It uses supervised learning algorithms like multiple linear regression to analyze collected housing data and predict prices. The tools used include Python, TensorFlow, Android Studio, and Anaconda. The model works by collecting data from various sources, training on that data, and then generating price predictions based on user-inputted home details. There is room for improving the model's accuracy by incorporating new data sources over time.
How to implement artificial intelligence solutions
The document provides an overview of how to implement artificial intelligence solutions. It discusses getting started in AI by either creating new techniques as a scientist or implementing existing techniques as an engineer. It then covers various machine learning algorithms like linear regression, decision trees, random forests, naive bayes, k-nearest neighbors, k-means, and support vector machines. Finally, it introduces deep learning concepts like artificial neural networks, neurons, layers, gradients, optimizers, overfitting, and regularization. The document serves as a guide for implementing both machine learning and deep learning techniques for AI applications.
Housing price prediction
A ppt based on predicting prices of houses. Also tells about basics of machine learning and the algorithm used to predict those prices by using regression technique.
Custom Vision.pptx
Introduction to CUSTOM VISION: Learn how to leverage the power of images to train a custom model for image classification using machine learning. This workshop covers the basics of computer vision and deep learning, and walks you through the process of creating a custom image classifier using the Custom Vision service. Discover how to annotate training data, train your model, and perform inference to classify images with high accuracy. Join us for a hands-on session and learn how to build your own custom vision solution!
Module 7: Unsupervised Learning
Delta Analytics is a 501(c)3 non-profit in the Bay Area. We believe that data is powerful, and that anybody should be able to harness it for change. Our teaching fellows partner with schools and organizations worldwide to work with students excited about the power of data to do good.
Welcome to the course! These modules will teach you the fundamental building blocks and the theory necessary to be a responsible machine learning practitioner in your own community. Each module focuses on accessible examples designed to teach you about good practices and the powerful (yet surprisingly simple) algorithms we use to model data.
To learn more about our mission or provide feedback, take a look at www.deltanalytics.org. If you would like to use this material to further our mission of improving access to machine learning. Education please reach out to inquiry@deltanalytics.org.
Brains@Bay Meetup: The Effect of Sensorimotor Learning on the Learned Represe...
- The representations learned through self-supervised exploration in an embodied agent encode meaningful information about concepts without being explicitly taught.
- Concepts can be learned from very few examples (above chance accuracy with one example) by analyzing neuron correlations across examples.
- This concept mapping approach does not require retraining or large labeled datasets, and has no problem with catastrophic forgetting, since it relies on the encoded representations.
AWS Certified Machine Learning Specialty
Amazon AWS Machine Learning Specialty Notes which covers Linear Regression, PCA, K-Means, Algorithms, Machine Learning Concepts, Regularisation, Optimisation, Unsupervised, Supervised, Reinforcement learning, Deep Racer, One Hot Encoding
Machine learning para tertulianos, by javier ramirez at teowaki
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
introduction to machine learning
This document provides an introduction to machine learning and intelligent agents. It defines machine learning as programs that automatically improve performance through experience without human assistance. Examples of machine learning problems include optical character recognition, spam filtering, and medical diagnosis. Learning models define the learning problem formally. Intelligent agents can perceive their environment and act upon it. Rational agents aim to perform useful actions. The document discusses properties of environments including observable vs partially observable. It outlines different types of agents from simple reflex agents to goal-based and utility-based agents.
SIP-REVIEW-3.pptx
This document contains summaries of multiple machine learning courses and modules. It introduces machine learning and the concepts of supervised and unsupervised learning. It also discusses reinforcement learning and the difference between exploration and exploitation in reinforcement learning models. Finally, it covers the basics of model learning, including defining deep network learning as an optimization problem and using gradient descent and stochastic gradient descent to solve this problem.
ML Study Jams - Session 3.pptx
This document summarizes a session on classification models. It discusses classifying data by learning classification rules from labeled input data. Specifically, it covers k-nearest neighbors classification, where the target point is assigned the majority class of its k nearest neighbors. It also discusses extending linear regression to classification by using it to generate real-valued outputs, then thresholding those values to classify instances into classes 0 or 1. However, it notes that logistic regression is a better approach, using a sigmoid function to convert real-valued outputs to a probability between 0 and 1 before thresholding for classification. The document highlights key considerations for classification like dealing with multiple classes and class imbalance. It also discusses evaluating classifiers, ensemble methods, and overfitting/under
Industrial training ppt
Hrjeet Singh completed a 42-day online industrial training from Internshala located in Gurgaon, India. During the training, Singh learned about machine learning concepts including classification, regression, linear regression, logistic regression, decision trees, and K-means clustering. Singh also completed a project using machine learning classifiers to detect breast cancer by analyzing features of breast cancer patient and normal cells.
machinecanthink-160226155704.pdf
This document provides an overview of machine learning. It begins with an introduction and discusses the basics, types (supervised, unsupervised, reinforcement learning), technologies, applications, and vision for the next few years. Key points covered include definitions of machine learning, examples of applications (search engines, spam filters, personalized recommendations), and descriptions of different problem types (classification, regression, clustering) and learning approaches (decision trees, neural networks, Bayesian methods).
Robotic models of active perception
This document discusses active perception in robots and humans. It notes that humans are able to perceive complex, unstructured environments by actively perceiving through mechanisms like saccades and attention. For robots to operate in dangerous environments, they will need to develop similar active perception abilities to cope with perceptual complexity. The document explores issues like where to direct attention, what to remember, and when to stop perceiving and start acting. It also examines how active perception affects learning, representation, and decision-making.
Similar to Machine Learning: Real life business application (20)
Sippin: A Mobile Application Case Study presented at Techfest Louisville
Sippin: A Mobile Application Case Study presented at Techfest Louisville
How to implement artificial intelligence solutions
How to implement artificial intelligence solutions
Brains@Bay Meetup: The Effect of Sensorimotor Learning on the Learned Represe...
Brains@Bay Meetup: The Effect of Sensorimotor Learning on the Learned Represe...
Machine learning para tertulianos, by javier ramirez at teowaki
Machine learning para tertulianos, by javier ramirez at teowaki
Recently uploaded
Large Language Model (LLM) and it’s Geospatial Applications
Large Language Model (LLM) and it’s Geospatial Applications.
20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
A tale of scale & speed: How the US Navy is enabling software delivery from l...
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
20240607 QFM018 Elixir Reading List May 2024
Everything I found interesting about the Elixir programming ecosystem in May 2024
20 Comprehensive Checklist of Designing and Developing a Website
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
Communications Mining Series - Zero to Hero - Session 1
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Essentials of Automations: The Art of Triggers and Actions in FME
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
Securing your Kubernetes cluster_ a step-by-step guide to success !
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Recently uploaded (20)
Large Language Model (LLM) and it’s Geospatial Applications
Large Language Model (LLM) and it’s Geospatial Applications
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
A tale of scale & speed: How the US Navy is enabling software delivery from l...
A tale of scale & speed: How the US Navy is enabling software delivery from l...
20 Comprehensive Checklist of Designing and Developing a Website
20 Comprehensive Checklist of Designing and Developing a Website
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...
Securing your Kubernetes cluster_ a step-by-step guide to success !
Securing your Kubernetes cluster_ a step-by-step guide to success !
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Machine Learning: Real life business application
- 1. Machine Learning Real life Business Application
- 2. Introduction Dhruv Chaudhari Entrepreneur & Software Architect(NLP, IR, ML, DL & Cloud)
- 3. All problems in computer science can be solved by another level of indirection. - David Wheeler
- 5. Classification Supervised Defined as a problem of looking for a mapping from objects to a finite set of classes. Usually each object has just one class (but there are generalizations to multiple ones). Real life examples: ● Face recognition (we are given a face and answer who is it) ● Drug discovery (we are given a compound and we answer if it is a drug or not)
- 6. Regression Supervised We are looking for a mapping to a infinite number values, with valid ordering, for example real numbers. Real life examples: ● Predicting for much money a user will spend in our shop based on his characteristic. ● Predicting power consumption in the next month. ● Predicting stock prices
- 7. Clustering Un-Supervised Usually defined as finding a structure in data, without access to any sample of such structure (later on with many modifications such as constrained clustering, weakly supervised clustering) Real life examples: ● Given set of images of stars, do they form some distinguishable types of stars? ● Given users activity on our website - are there distinguishable usage scenarios that we can find?
- 8. Anomaly detection Un-Supervised Given a set of "normal" observations build a model to answer "is new observation normal, or is it an anomaly?" Real life examples: ● We have record of a valid engine parameters and need a method to alarm as that it starts to behave "weird" (even though we do not know from the past what kind of "weird" we are looking for).
- 9. Anomaly detection Un-Supervised Given a set of "normal" observations build a model to answer "is new observation normal, or is it an anomaly?" Real life examples: ● We have recordings from camera of usual people behaviour, we want method to alarm as that "something unusual is happening" (without specifing what)
- 10. Dimension Reduction Un-Supervised This is just a preprocessing step. Given high dimensional data we seek for a lower-dimensional representation which is usable in other tasks. ● We have set of high- dimensional data (like patients records) and want to visualize it (draw on a plane) ● We have a problem of classification and our methods fail - we need to reduce dimensionality to increase scores
- 11. Reinforcment Un-Supervised ● None of the above is reinforcment learning. Reinforcment learning can be applied to any of the above, if we simply have some 'environment' saying that our method is doing 'good' or 'bad' (so instead of saying 'I want this image to be classified as cat' it only says 'I see that you classified this image as a plane, well.. it is not!'). In other words - we do any task, but we do have humans who judge is our method good or bad, but they do not give as correct answers.
- 12. Computer Vision
- 18. Deep Learning
- 22. Data Science Define and Opportunities
- 24. QA Session
- 26. Pune Developer’s Community Facebook: fb/punedevscommunity/ Linkedin: https://www.linkedin.com/groups/10325743