AI_Python_Jupyter.pdf
•
0 likes•7 views
This document discusses building a machine learning application with Python to predict diabetes using a dataset of Pima Indian patients in Arizona. It provides an overview of machine learning workflows, including getting and cleaning data, choosing algorithms, training models, and evaluating performance. The goal is to predict diabetes using the Pima Indian Diabetes dataset from the UCI Machine Learning Repository and achieve a target accuracy of 70% or higher.
Report
Share
Report
Share
Download to read offline
Recommended
Model Risk Management for Machine Learning
The document discusses model risk management considerations for machine learning models. It begins with an overview of machine learning and artificial intelligence applications in finance. It then covers key elements of model risk management for machine learning such as model governance structure, model lifecycle management, tracking, metadata management, scaling, reproducibility, interpretability, and testing. The presentation concludes with a discussion on quantifying model risk.
Machine Learning in Modern Medicine with Erin LeDell at Stanford Med
Machine learning and AI company H2O.ai presented on machine learning applications in modern medicine. They discussed how electronic health records, genomics, wearables, and other data sources can be used with machine learning for personalized healthcare, disease prediction and prevention. H2O's software platform allows building models at scale from large datasets using algorithms like random forests, deep learning and ensembles. Demonstrations showed predicting HIV treatment failure and classifying breast cancer malignancy from medical images, achieving high accuracy. H2O aims to make machine learning accessible and scalable for improving medical research and care.
Lec 1 integrating data science and data analytics in various research thrust
Lec 1 integrating data science and data analytics in various research thrustMenchita Falcutila Dumlao
This slide is an introductory lesson on how to apply data science as a research strategy in an educational institutionBuild a Next-Generation Clinical Operational Metrics Solution
Srinivasan Anandakumar spoke at ExL's Clinical Performance Metrics Summit in Philadelphia on December 5, 2017. His talk includes discussion around how to manage and control both trial effectiveness and submission pathway through clinical metrics collected from operational data and patient data, how to get a 360-degree view of trial operations with a next-generation clinical operations repository, that contains all clinical domains—based on a Big Data platform with multiple varieties of data– sponsor, syndicated, and external data, and how to enable the AI to allow high levels of self-service and to provide predictive analytics on clinical operations data.
Data Science Course in Pune
This document outlines the curriculum for a Data Science course in Pune, India. The course covers all aspects of data science including data collection, cleaning, exploration, machine learning algorithms, deep learning, big data processing, and ethics. Students gain hands-on experience through real-world projects and case studies. The goal is to prepare students for careers as data scientists, a field with growing demand across industries.
A New Paradigm on Analytic-Driven Information and Automation V2.pdf
The document proposes an end-to-end methodology for developing analytic-driven information and automation systems based on big data, data science, and artificial intelligence. The methodology involves 6 steps: 1) collecting data from multiple sources, 2) preprocessing the data, 3) extracting features from the data, 4) clustering and interpreting the data, 5) designing applications, and 6) implementing and evaluating the systems. It then provides an example of applying this methodology to develop an early warning system for monitoring higher education institutions in Indonesia. The system would collect data from various sources, analyze it using machine learning techniques, predict and prescribe interventions for student groups.
isms presesntation.pptx
The document outlines an information security management system project. It includes introductions to the existing system, problem statement, proposed solution, project scope, objectives, diagrams, tools and technologies used, task distribution, screenshots, and future project schedule. The proposed system aims to provide data security, confidentiality, integrity and accessibility for an organization through automated policies, controls, and risk assessment. It will help identify threats, assign asset owners and manage security risks systematically.
MongoDB World 2019: A Real-time Clinical Decision Support System: Building A ...
We will present the application of MongoDB to build ML platform at one of the largest health systems in the country. We will discuss:
a) how we used MongoDB as a clinical data lake
b) how we are creating a single view of data
c) use of Spark-Mongo connector
d) how we used change-stream.
Recommended
Model Risk Management for Machine Learning
The document discusses model risk management considerations for machine learning models. It begins with an overview of machine learning and artificial intelligence applications in finance. It then covers key elements of model risk management for machine learning such as model governance structure, model lifecycle management, tracking, metadata management, scaling, reproducibility, interpretability, and testing. The presentation concludes with a discussion on quantifying model risk.
Machine Learning in Modern Medicine with Erin LeDell at Stanford Med
Machine learning and AI company H2O.ai presented on machine learning applications in modern medicine. They discussed how electronic health records, genomics, wearables, and other data sources can be used with machine learning for personalized healthcare, disease prediction and prevention. H2O's software platform allows building models at scale from large datasets using algorithms like random forests, deep learning and ensembles. Demonstrations showed predicting HIV treatment failure and classifying breast cancer malignancy from medical images, achieving high accuracy. H2O aims to make machine learning accessible and scalable for improving medical research and care.
Lec 1 integrating data science and data analytics in various research thrust
Lec 1 integrating data science and data analytics in various research thrustMenchita Falcutila Dumlao
This slide is an introductory lesson on how to apply data science as a research strategy in an educational institutionBuild a Next-Generation Clinical Operational Metrics Solution
Srinivasan Anandakumar spoke at ExL's Clinical Performance Metrics Summit in Philadelphia on December 5, 2017. His talk includes discussion around how to manage and control both trial effectiveness and submission pathway through clinical metrics collected from operational data and patient data, how to get a 360-degree view of trial operations with a next-generation clinical operations repository, that contains all clinical domains—based on a Big Data platform with multiple varieties of data– sponsor, syndicated, and external data, and how to enable the AI to allow high levels of self-service and to provide predictive analytics on clinical operations data.
Data Science Course in Pune
This document outlines the curriculum for a Data Science course in Pune, India. The course covers all aspects of data science including data collection, cleaning, exploration, machine learning algorithms, deep learning, big data processing, and ethics. Students gain hands-on experience through real-world projects and case studies. The goal is to prepare students for careers as data scientists, a field with growing demand across industries.
A New Paradigm on Analytic-Driven Information and Automation V2.pdf
The document proposes an end-to-end methodology for developing analytic-driven information and automation systems based on big data, data science, and artificial intelligence. The methodology involves 6 steps: 1) collecting data from multiple sources, 2) preprocessing the data, 3) extracting features from the data, 4) clustering and interpreting the data, 5) designing applications, and 6) implementing and evaluating the systems. It then provides an example of applying this methodology to develop an early warning system for monitoring higher education institutions in Indonesia. The system would collect data from various sources, analyze it using machine learning techniques, predict and prescribe interventions for student groups.
isms presesntation.pptx
The document outlines an information security management system project. It includes introductions to the existing system, problem statement, proposed solution, project scope, objectives, diagrams, tools and technologies used, task distribution, screenshots, and future project schedule. The proposed system aims to provide data security, confidentiality, integrity and accessibility for an organization through automated policies, controls, and risk assessment. It will help identify threats, assign asset owners and manage security risks systematically.
MongoDB World 2019: A Real-time Clinical Decision Support System: Building A ...
We will present the application of MongoDB to build ML platform at one of the largest health systems in the country. We will discuss:
a) how we used MongoDB as a clinical data lake
b) how we are creating a single view of data
c) use of Spark-Mongo connector
d) how we used change-stream.
Manoj Shanmugasundaram - Agile Machine Learning Development
The document discusses agile machine learning development and the Cloudera Data Science Workbench. It introduces the key differences between traditional programming and machine learning. It then outlines the typical data science workflow and what data science teams do. It also discusses the new requirements for supporting agile ML development. Finally, it introduces the key features of the Cloudera Data Science Workbench for accelerating machine learning from research to production.
Models in Minutes using AutoML
The document discusses building machine learning models and deploying them in production environments. It describes the typical journey a data scientist takes to build a model, including accessing and exploring data, engineering features, building models, and interpreting results. It notes that bringing a model to production requires a team including data engineers, front-end developers, data scientists, and platform engineers. The document then discusses how the Salesforce Einstein platform supports data scientists by enabling them to deploy, monitor, and iterate on models in one location through a suite of services including data, modeling, monitoring, and provisioning services.
Enabling Better Clinical Operations through a Clinical Operations Store
Srini Anandakumar, Senior Director of Clinical Analytics Innovation for Saama, presented at the Big Data and Analytics in Pharma in Philadelphia, November 1, 2017.
Bn1031 demo sap ehs
Visit Us: http://www.conlinetraining.com/
Facebook : https://www.facebook.com/conlinetraining/
Google+ : https://plus.google.com/u/0/collections?arp=1&cfem=111
Twitter : https://twitter.com/conlinetrainin9
Drop your Response :http://www.conlinetraining.com/educationwp/
Predictive Analytics Using R | Edureka
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka PPT on "Predictive Analytics Using R", will help you learn about how predictive analytics works and how it can be implemented using R to solve real-world problems. Below are the topics covered in this module:
What is Predictive Analytics?
Stages of Predictive Analytics
Predictive Analytics Using R
Predictive Analytics Use case
Demo
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
H2O with Erin LeDell at Portland R User Group
Erin LeDell's presentation on scalable machine learning in R with H2O from the Portland R User Group Meetup in Portland, 08.17.15
Ml conference slides boston june 2019
A Master Class for Financial Professionals for AI and Machine Learning
featuring Sri Krishnamurthy, CFA, CAP, QuantUniversity
Summary
The use of Data Science and Machine learning in the investment industry is increasing and investment professionals both fundamental and quantitative, are taking notice. Financial firms are taking AI and machine learning seriously to augment traditional investment decision making. Alternative datasets including text analytics, cloud computing, algorithmic trading are game changers for many firms who are adopting technology at a rapid pace. As more and more technologies penetrate enterprises, financial professionals are enthusiastic about the upcoming revolution and are looking for direction and education on data science and machine learning topics.
In this workshop, we aim to bring clarity on how AI and machine learning is revolutionizing financial services. We will introduce key concepts and through examples and case studies, we will illustrate the role of machine learning, data science techniques and AI in the investment industry. At the end of this workshop, participants can see a concrete picture on how to machine learning and AI techniques are fueling the Fintech wave!
SaurabhJain_Resume
Saurabh Jain is seeking a full-time position in supply chain management or manufacturing systems. He has a Master's in Industrial Engineering from the University of Arizona and a Bachelor's in Mechanical Engineering. His technical skills include supply chain management, data analytics, and programming languages like Python and R. He has research experience in distributed manufacturing systems and sensor integration.
Data Science and Business Analytics PG Program
Calling all data science aspirants! Here is a Data Science and Business Analytics course you could consider to advance your career.
Labmatrix
Labmatrix is a software application that manages the operational aspects of collaborative clinical and translational research programs, including patient recruiting, consenting, sample management (biobanking), experimental characterization of the samples and tracking of patient clinical profiles.
Adoption of modern breeding tools in developing countries: challenges and opp...
Adoption of modern breeding tools in developing countries: challenges and opp...CGIAR Generation Challenge Programme
This document discusses challenges and opportunities for adoption of modern breeding tools in developing countries. It covers several topics: human expertise and training needs, infrastructure requirements, access to information and technology, data management challenges, availability of analytical tools and support services, importance of effective partnerships, and changing mindsets. Supporting adoption will require a coordinated, multi-pronged approach including training, infrastructure investments, improved data management practices, access to analytical tools and genotyping services, and strong support networks.The Age of Big Data: A New Class of Economic Asset
This document discusses the rise of big data and data-driven economies. It notes that data has become a new class of economic asset and that many governments and organizations have recognized the importance of harnessing big data. It then describes some of the key characteristics of big data and drivers that are generating large volumes of data such as mobile devices, the internet of things, user-generated content, and cloud computing. The remainder of the document discusses concepts such as the data value chain, different types of data analytics, and various use cases and case studies to illustrate how big data is being applied.
Sai Teja K Resume.pdf
Sai Teja K has over 2 years of experience in mentoring team members, performing code reviews, and conducting technical queries solving sessions. Some of their skills and experiences include developing ETL data pipelines and machine learning models in Python, performing data analysis and visualization in Tableau, and working with big data technologies like Spark, Hadoop, and AWS. They have a Master's in Data Analytics from Clark University and were a Business Technology Solutions Associate at ZS Associates.
DATA MINING DC Presentation.pptx
Data mining involves extracting useful patterns from large amounts of data. It uses software tools to analyze raw data and discover hidden patterns that can help businesses gain insights into customer behavior and develop effective marketing strategies. The key processes involved in data mining are data cleaning, integration, transformation, mining, pattern evaluation, and presentation. Data mining is commonly used for applications like classification, association, prediction, clustering, and regression. It has proven useful in domains such as healthcare for improving treatment and education for enhancing learning outcomes. Popular tools for data mining include Oracle Data Mining, IBM SPSS Modeler, RapidMiner, Apache Mahout, Weka, and KNIME.
Alakesh mani resume
Alakesh Mani has a Master's degree in Data Analytics from Penn State and a Bachelor's degree in Electronics and Communication Engineering from Madras Institute of Technology. He has over 2 years of experience as a Software Analyst at Aspire Systems where he worked on automating payroll tax calculations using Apache Spark and Python, building a recommendation engine using Spark MLlib, and scraping Twitter data using Spark. He has strong skills in languages like Java, Scala, Python and tools like Spark, Hadoop and Hive.
Alakesh mani resume
Alakesh Mani has experience in data analytics and software development. He has a Master's degree in Data Analytics from Penn State and a Bachelor's degree in Electronics and Communication Engineering. His skills include programming languages like Python, Scala, Java, and querying languages like SQL, HiveQL. He has worked on projects involving building recommendation engines using Spark MLlib, automating payroll tax calculations using Spark and HDInsight, and scraping Twitter data using Spark. He received scholarships from Penn State and awards for his work in programming and business plan competitions.
AI Class Topic 3: Building Machine Learning Predictive Systems (Predictive Ma...
AI Class Topic 3: Building Machine Learning Predictive Systems (Predictive Ma...Value Amplify Consulting
This document discusses building a predictive system using machine learning. It describes predicting income using census data with four machine learning algorithms: Two-Class Decision Jungle, Two-Class Averaged Perceptron, Two-Class Bayes Point Machine, and Two-Class Locally-Deep Support Vector Machine. It also discusses tuning hyperparameters, combining results, and benchmarking performance. Additional sections cover predictive analytics processes, digital transformation, and predictive maintenance maturity models.Quick presentation for the OpenML workshop in Eindhoven 2014
This document summarizes Manuel Martín Salvador's background and research interests in automated and adaptive data pre-processing for building predictive models. It discusses how data pre-processing makes up a large portion of the data mining process but is labor intensive. The document also outlines OpenML, a scientific workflow platform and repository for machine learning experiments, and highlights opportunities to increase the number and types of pre-processing methods available on the platform as well as improve flow representation and recommendation.
Development of computerlized livestock management information system.pptx
This presentation outlines the development of a computerized livestock management information system. It will provide an overview of the system's potential benefits for farmers in managing livestock operations more efficiently. The system aims to address challenges with traditional management methods by collecting, storing, analyzing and reporting data to help farmers make informed decisions that can improve profitability and prevent losses. The presentation will cover requirements gathering, design, implementation, and evaluation of the system.
Intelligent Clinical Supply Forecasting and Simulations through IRT-March02-2...
This document discusses using intelligent clinical supply forecasting and simulations through an interactive response technology (IRT) system. It highlights trends and challenges in clinical trials that increase pressure on sponsors and CROs. Simulation and forecasting tools that integrate with an IRT system can help optimize drug supply planning, reduce costs from overages, and improve efficiency. Real-time data from the IRT on enrollments, visits, and inventory levels allows frequent reforecasting to reduce supply chain risks and costs. The presentation provides an overview of how analytics and simulations using IRT data can help various stakeholders with challenges around forecasting demand, optimizing distribution, and efficient planning of drug supplies.
"Scaling RAG Applications to serve millions of users", Kevin Goedecke
How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
More Related Content
Similar to AI_Python_Jupyter.pdf
Manoj Shanmugasundaram - Agile Machine Learning Development
The document discusses agile machine learning development and the Cloudera Data Science Workbench. It introduces the key differences between traditional programming and machine learning. It then outlines the typical data science workflow and what data science teams do. It also discusses the new requirements for supporting agile ML development. Finally, it introduces the key features of the Cloudera Data Science Workbench for accelerating machine learning from research to production.
Models in Minutes using AutoML
The document discusses building machine learning models and deploying them in production environments. It describes the typical journey a data scientist takes to build a model, including accessing and exploring data, engineering features, building models, and interpreting results. It notes that bringing a model to production requires a team including data engineers, front-end developers, data scientists, and platform engineers. The document then discusses how the Salesforce Einstein platform supports data scientists by enabling them to deploy, monitor, and iterate on models in one location through a suite of services including data, modeling, monitoring, and provisioning services.
Enabling Better Clinical Operations through a Clinical Operations Store
Srini Anandakumar, Senior Director of Clinical Analytics Innovation for Saama, presented at the Big Data and Analytics in Pharma in Philadelphia, November 1, 2017.
Bn1031 demo sap ehs
Visit Us: http://www.conlinetraining.com/
Facebook : https://www.facebook.com/conlinetraining/
Google+ : https://plus.google.com/u/0/collections?arp=1&cfem=111
Twitter : https://twitter.com/conlinetrainin9
Drop your Response :http://www.conlinetraining.com/educationwp/
Predictive Analytics Using R | Edureka
** Data Science Certification using R: https://www.edureka.co/data-science **
This Edureka PPT on "Predictive Analytics Using R", will help you learn about how predictive analytics works and how it can be implemented using R to solve real-world problems. Below are the topics covered in this module:
What is Predictive Analytics?
Stages of Predictive Analytics
Predictive Analytics Using R
Predictive Analytics Use case
Demo
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
H2O with Erin LeDell at Portland R User Group
Erin LeDell's presentation on scalable machine learning in R with H2O from the Portland R User Group Meetup in Portland, 08.17.15
Ml conference slides boston june 2019
A Master Class for Financial Professionals for AI and Machine Learning
featuring Sri Krishnamurthy, CFA, CAP, QuantUniversity
Summary
The use of Data Science and Machine learning in the investment industry is increasing and investment professionals both fundamental and quantitative, are taking notice. Financial firms are taking AI and machine learning seriously to augment traditional investment decision making. Alternative datasets including text analytics, cloud computing, algorithmic trading are game changers for many firms who are adopting technology at a rapid pace. As more and more technologies penetrate enterprises, financial professionals are enthusiastic about the upcoming revolution and are looking for direction and education on data science and machine learning topics.
In this workshop, we aim to bring clarity on how AI and machine learning is revolutionizing financial services. We will introduce key concepts and through examples and case studies, we will illustrate the role of machine learning, data science techniques and AI in the investment industry. At the end of this workshop, participants can see a concrete picture on how to machine learning and AI techniques are fueling the Fintech wave!
SaurabhJain_Resume
Saurabh Jain is seeking a full-time position in supply chain management or manufacturing systems. He has a Master's in Industrial Engineering from the University of Arizona and a Bachelor's in Mechanical Engineering. His technical skills include supply chain management, data analytics, and programming languages like Python and R. He has research experience in distributed manufacturing systems and sensor integration.
Data Science and Business Analytics PG Program
Calling all data science aspirants! Here is a Data Science and Business Analytics course you could consider to advance your career.
Labmatrix
Labmatrix is a software application that manages the operational aspects of collaborative clinical and translational research programs, including patient recruiting, consenting, sample management (biobanking), experimental characterization of the samples and tracking of patient clinical profiles.
Adoption of modern breeding tools in developing countries: challenges and opp...
Adoption of modern breeding tools in developing countries: challenges and opp...CGIAR Generation Challenge Programme
This document discusses challenges and opportunities for adoption of modern breeding tools in developing countries. It covers several topics: human expertise and training needs, infrastructure requirements, access to information and technology, data management challenges, availability of analytical tools and support services, importance of effective partnerships, and changing mindsets. Supporting adoption will require a coordinated, multi-pronged approach including training, infrastructure investments, improved data management practices, access to analytical tools and genotyping services, and strong support networks.The Age of Big Data: A New Class of Economic Asset
This document discusses the rise of big data and data-driven economies. It notes that data has become a new class of economic asset and that many governments and organizations have recognized the importance of harnessing big data. It then describes some of the key characteristics of big data and drivers that are generating large volumes of data such as mobile devices, the internet of things, user-generated content, and cloud computing. The remainder of the document discusses concepts such as the data value chain, different types of data analytics, and various use cases and case studies to illustrate how big data is being applied.
Sai Teja K Resume.pdf
Sai Teja K has over 2 years of experience in mentoring team members, performing code reviews, and conducting technical queries solving sessions. Some of their skills and experiences include developing ETL data pipelines and machine learning models in Python, performing data analysis and visualization in Tableau, and working with big data technologies like Spark, Hadoop, and AWS. They have a Master's in Data Analytics from Clark University and were a Business Technology Solutions Associate at ZS Associates.
DATA MINING DC Presentation.pptx
Data mining involves extracting useful patterns from large amounts of data. It uses software tools to analyze raw data and discover hidden patterns that can help businesses gain insights into customer behavior and develop effective marketing strategies. The key processes involved in data mining are data cleaning, integration, transformation, mining, pattern evaluation, and presentation. Data mining is commonly used for applications like classification, association, prediction, clustering, and regression. It has proven useful in domains such as healthcare for improving treatment and education for enhancing learning outcomes. Popular tools for data mining include Oracle Data Mining, IBM SPSS Modeler, RapidMiner, Apache Mahout, Weka, and KNIME.
Alakesh mani resume
Alakesh Mani has a Master's degree in Data Analytics from Penn State and a Bachelor's degree in Electronics and Communication Engineering from Madras Institute of Technology. He has over 2 years of experience as a Software Analyst at Aspire Systems where he worked on automating payroll tax calculations using Apache Spark and Python, building a recommendation engine using Spark MLlib, and scraping Twitter data using Spark. He has strong skills in languages like Java, Scala, Python and tools like Spark, Hadoop and Hive.
Alakesh mani resume
Alakesh Mani has experience in data analytics and software development. He has a Master's degree in Data Analytics from Penn State and a Bachelor's degree in Electronics and Communication Engineering. His skills include programming languages like Python, Scala, Java, and querying languages like SQL, HiveQL. He has worked on projects involving building recommendation engines using Spark MLlib, automating payroll tax calculations using Spark and HDInsight, and scraping Twitter data using Spark. He received scholarships from Penn State and awards for his work in programming and business plan competitions.
AI Class Topic 3: Building Machine Learning Predictive Systems (Predictive Ma...
AI Class Topic 3: Building Machine Learning Predictive Systems (Predictive Ma...Value Amplify Consulting
This document discusses building a predictive system using machine learning. It describes predicting income using census data with four machine learning algorithms: Two-Class Decision Jungle, Two-Class Averaged Perceptron, Two-Class Bayes Point Machine, and Two-Class Locally-Deep Support Vector Machine. It also discusses tuning hyperparameters, combining results, and benchmarking performance. Additional sections cover predictive analytics processes, digital transformation, and predictive maintenance maturity models.Quick presentation for the OpenML workshop in Eindhoven 2014
This document summarizes Manuel Martín Salvador's background and research interests in automated and adaptive data pre-processing for building predictive models. It discusses how data pre-processing makes up a large portion of the data mining process but is labor intensive. The document also outlines OpenML, a scientific workflow platform and repository for machine learning experiments, and highlights opportunities to increase the number and types of pre-processing methods available on the platform as well as improve flow representation and recommendation.
Development of computerlized livestock management information system.pptx
This presentation outlines the development of a computerized livestock management information system. It will provide an overview of the system's potential benefits for farmers in managing livestock operations more efficiently. The system aims to address challenges with traditional management methods by collecting, storing, analyzing and reporting data to help farmers make informed decisions that can improve profitability and prevent losses. The presentation will cover requirements gathering, design, implementation, and evaluation of the system.
Intelligent Clinical Supply Forecasting and Simulations through IRT-March02-2...
This document discusses using intelligent clinical supply forecasting and simulations through an interactive response technology (IRT) system. It highlights trends and challenges in clinical trials that increase pressure on sponsors and CROs. Simulation and forecasting tools that integrate with an IRT system can help optimize drug supply planning, reduce costs from overages, and improve efficiency. Real-time data from the IRT on enrollments, visits, and inventory levels allows frequent reforecasting to reduce supply chain risks and costs. The presentation provides an overview of how analytics and simulations using IRT data can help various stakeholders with challenges around forecasting demand, optimizing distribution, and efficient planning of drug supplies.
Similar to AI_Python_Jupyter.pdf (20)
Manoj Shanmugasundaram - Agile Machine Learning Development
Manoj Shanmugasundaram - Agile Machine Learning Development
Enabling Better Clinical Operations through a Clinical Operations Store
Enabling Better Clinical Operations through a Clinical Operations Store
Adoption of modern breeding tools in developing countries: challenges and opp...
Adoption of modern breeding tools in developing countries: challenges and opp...
The Age of Big Data: A New Class of Economic Asset
The Age of Big Data: A New Class of Economic Asset
AI Class Topic 3: Building Machine Learning Predictive Systems (Predictive Ma...
AI Class Topic 3: Building Machine Learning Predictive Systems (Predictive Ma...
Quick presentation for the OpenML workshop in Eindhoven 2014
Quick presentation for the OpenML workshop in Eindhoven 2014
Development of computerlized livestock management information system.pptx
Development of computerlized livestock management information system.pptx
Intelligent Clinical Supply Forecasting and Simulations through IRT-March02-2...
Intelligent Clinical Supply Forecasting and Simulations through IRT-March02-2...
Recently uploaded
"Scaling RAG Applications to serve millions of users", Kevin Goedecke
How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
Northern Engraving | Nameplate Manufacturing Process - 2024
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
What is an RPA CoE? Session 1 – CoE Vision
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdf
So… you want to become a Test Automation Engineer (or hire and develop one)? While there’s quite a bit of information available about important technical and tool skills to master, there’s not enough discussion around the path to becoming an effective Test Automation Engineer that knows how to add VALUE. In my experience this had led to a proliferation of engineers who are proficient with tools and building frameworks but have skill and knowledge gaps, especially in software testing, that reduce the value they deliver with test automation.
In this talk, Lee will share his lessons learned from over 30 years of working with, and mentoring, hundreds of Test Automation Engineers. Whether you’re looking to get started in test automation or just want to improve your trade, this talk will give you a solid foundation and roadmap for ensuring your test automation efforts continuously add value. This talk is equally valuable for both aspiring Test Automation Engineers and those managing them! All attendees will take away a set of key foundational knowledge and a high-level learning path for leveling up test automation skills and ensuring they add value to their organizations.
Must Know Postgres Extension for DBA and Developer during Migration
Mydbops Opensource Database Meetup 16
Topic: Must-Know PostgreSQL Extensions for Developers and DBAs During Migration
Speaker: Deepak Mahto, Founder of DataCloudGaze Consulting
Date & Time: 8th June | 10 AM - 1 PM IST
Venue: Bangalore International Centre, Bangalore
Abstract: Discover how PostgreSQL extensions can be your secret weapon! This talk explores how key extensions enhance database capabilities and streamline the migration process for users moving from other relational databases like Oracle.
Key Takeaways:
* Learn about crucial extensions like oracle_fdw, pgtt, and pg_audit that ease migration complexities.
* Gain valuable strategies for implementing these extensions in PostgreSQL to achieve license freedom.
* Discover how these key extensions can empower both developers and DBAs during the migration process.
* Don't miss this chance to gain practical knowledge from an industry expert and stay updated on the latest open-source database trends.
Mydbops Managed Services specializes in taking the pain out of database management while optimizing performance. Since 2015, we have been providing top-notch support and assistance for the top three open-source databases: MySQL, MongoDB, and PostgreSQL.
Our team offers a wide range of services, including assistance, support, consulting, 24/7 operations, and expertise in all relevant technologies. We help organizations improve their database's performance, scalability, efficiency, and availability.
Contact us: info@mydbops.com
Visit: https://www.mydbops.com/
Follow us on LinkedIn: https://in.linkedin.com/company/mydbops
For more details and updates, please follow up the below links.
Meetup Page : https://www.meetup.com/mydbops-databa...
Twitter: https://twitter.com/mydbopsofficial
Blogs: https://www.mydbops.com/blog/
Facebook(Meta): https://www.facebook.com/mydbops/
Apps Break Data
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Getting the Most Out of ScyllaDB Monitoring: ShareChat's Tips
ScyllaDB monitoring provides a lot of useful information. But sometimes it’s not easy to find the root of the problem if something is wrong or even estimate the remaining capacity by the load on the cluster. This talk shares our team's practical tips on: 1) How to find the root of the problem by metrics if ScyllaDB is slow 2) How to interpret the load and plan capacity for the future 3) Compaction strategies and how to choose the right one 4) Important metrics which aren’t available in the default monitoring setup.
Dandelion Hashtable: beyond billion requests per second on a commodity server
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
Tomaz Bratanic
Graph ML and GenAI Expert - Neo4j
Principle of conventional tomography-Bibash Shahi ppt..pptx
before the computed tomography, it had been widely used.
ScyllaDB Tablets: Rethinking Replication
ScyllaDB is making a major architecture shift. We’re moving from vNode replication to tablets – fragments of tables that are distributed independently, enabling dynamic data distribution and extreme elasticity. In this keynote, ScyllaDB co-founder and CTO Avi Kivity explains the reason for this shift, provides a look at the implementation and roadmap, and shares how this shift benefits ScyllaDB users.
Introducing BoxLang : A new JVM language for productivity and modularity!
Just like life, our code must adapt to the ever changing world we live in. From one day coding for the web, to the next for our tablets or APIs or for running serverless applications. Multi-runtime development is the future of coding, the future is to be dynamic. Let us introduce you to BoxLang.
Dynamic. Modular. Productive.
BoxLang redefines development with its dynamic nature, empowering developers to craft expressive and functional code effortlessly. Its modular architecture prioritizes flexibility, allowing for seamless integration into existing ecosystems.
Interoperability at its Core
With 100% interoperability with Java, BoxLang seamlessly bridges the gap between traditional and modern development paradigms, unlocking new possibilities for innovation and collaboration.
Multi-Runtime
From the tiny 2m operating system binary to running on our pure Java web server, CommandBox, Jakarta EE, AWS Lambda, Microsoft Functions, Web Assembly, Android and more. BoxLang has been designed to enhance and adapt according to it's runnable runtime.
The Fusion of Modernity and Tradition
Experience the fusion of modern features inspired by CFML, Node, Ruby, Kotlin, Java, and Clojure, combined with the familiarity of Java bytecode compilation, making BoxLang a language of choice for forward-thinking developers.
Empowering Transition with Transpiler Support
Transitioning from CFML to BoxLang is seamless with our JIT transpiler, facilitating smooth migration and preserving existing code investments.
Unlocking Creativity with IDE Tools
Unleash your creativity with powerful IDE tools tailored for BoxLang, providing an intuitive development experience and streamlining your workflow. Join us as we embark on a journey to redefine JVM development. Welcome to the era of BoxLang.
AI in the Workplace Reskilling, Upskilling, and Future Work.pptx
Discover how AI is transforming the workplace and learn strategies for reskilling and upskilling employees to stay ahead. This comprehensive guide covers the impact of AI on jobs, essential skills for the future, and successful case studies from industry leaders. Embrace AI-driven changes, foster continuous learning, and build a future-ready workforce.
Read More - https://bit.ly/3VKly70
AWS Certified Solutions Architect Associate (SAA-C03)
AWS Certified Solutions Architect Associate (SAA-C03)
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Dmitrii Kamaev, PhD
Senior Product Owner - QIAGEN
"Choosing proper type of scaling", Olena Syrota
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
GNSS spoofing via SDR (Criptored Talks 2024)
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Recently uploaded (20)
"Scaling RAG Applications to serve millions of users", Kevin Goedecke
"Scaling RAG Applications to serve millions of users", Kevin Goedecke
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
Northern Engraving | Nameplate Manufacturing Process - 2024
Northern Engraving | Nameplate Manufacturing Process - 2024
Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdf
Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdf
Must Know Postgres Extension for DBA and Developer during Migration
Must Know Postgres Extension for DBA and Developer during Migration
Getting the Most Out of ScyllaDB Monitoring: ShareChat's Tips
Getting the Most Out of ScyllaDB Monitoring: ShareChat's Tips
Dandelion Hashtable: beyond billion requests per second on a commodity server
Dandelion Hashtable: beyond billion requests per second on a commodity server
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
Principle of conventional tomography-Bibash Shahi ppt..pptx
Principle of conventional tomography-Bibash Shahi ppt..pptx
Introducing BoxLang : A new JVM language for productivity and modularity!
Introducing BoxLang : A new JVM language for productivity and modularity!
AI in the Workplace Reskilling, Upskilling, and Future Work.pptx
AI in the Workplace Reskilling, Upskilling, and Future Work.pptx
AWS Certified Solutions Architect Associate (SAA-C03)
AWS Certified Solutions Architect Associate (SAA-C03)
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
Biomedical Knowledge Graphs for Data Scientists and Bioinformaticians
AI_Python_Jupyter.pdf
- 1. Membangun Aplikasi Machine Learning dengan Python: Memprediksi Penyakit Diabetes Dataset: Data Pasien Suku Pima Indian di Arizona, AS Sumber Data: University of California Irvine (Diabetes Patient Records) Dr. Fal Sadikin
- 2. AGENDA
- 4. INTRODUCTION TO MACHINE LEARNING (TRADITIONAL CONTROL LOGIC (RULE-BASED APPROACH )VS MACHINE LEARNING)
- 8. TYPE OF MACHINE LEARNING
- 9. SUPERVISED MACHINE LEARNING Dataset of Housing and Price
- 10. UNSUPERVISED MACHINE LEARNING Dataset of Mixing Voice
- 11. BIG PICTURE OF MACHINE LEARNING
- 18. MACHINE LEARNING WORKFLOW • Memprediksi Kemungkinan Diabetes • Cakupan dan Sumber Data • Pima Indian Diabetes Data • UCI Machine Learning Repository • Mengenali dan memahami Fitur fitur data, memahami fitur fitur penting • Target akurasi • Misal: 70% Accuracy
- 19. MACHINE LEARNING WORKFLOW • 60-80% of a ML project is spent getting, cleaning, and organizing data
- 33. CHOOSING MACHINE LEARNING ALGORITHMS http://aka.ms/MLCheatSheet
- 38. MACHINE LEARNING WORKFLOW Training: Menggunakan specific data untuk mengajarkan machine learning (algorithm) untuk membuat specific prediction model
- 43. CROSS VALIDATION
- 46. Akurasi
- 58. SUPERVISED MACHINE LEARNING Dataset of Housing and Price
- 59. MACHINE LEARNING WORKFLOW • Options for Improving Performance: ➢ Adjust training settings ➢ Retrain with better dataset ➢ Select a better algorithm ➢ Rethink the problem