A session on Artificial Intelligence and Machine Learning for anyone and everyone.
Demystify the world of Artifical Intelligence and Machine Learning in a simple and fun way so that everyone can understand and use Machine learning.
Ofer Ron, senior data scientist at LivePerson.
Recently, I've had the pleasure of presenting an introduction to Data Science and data driven products at DevconTLV
I focused this talk around the basic ideas of data science, not the technology used, since I thought that far too many times companies and developers rush to play around with "big data" related technologies, instead of figuring out what questions they want to answer, and whether these answers form a successful product.
This talk discusses techniques for dealing with messy and imperfect data. Three examples are provided: (1) commercial real estate data with validity issues that were addressed through consensus, imputation, outlier removal and OCR; (2) click data with cookie issues that required transformations like sampling and clustering after preprocessing; (3) digital media data with access issues that necessitated working with insufficient data using decomposition and seeking more data. The overall message is to focus on the problem, identify data limitations, and apply creative solutions like reworking the data.
These are slides for a guest talk I gave for course 15.S14: Global Business of Artificial Intelligence and Robotics (GBAIR) taught in Spring 2017. Here is the YouTube video (filmed in 360/VR): https://youtu.be/s3MuSOl1Rog
Slides for the course Big Data and Automated Content Analysis, in which students of the social sciences (communication science) learn how to conduct analyses using Python. First Meeting.
Introduction to Data Science and Large-scale Machine LearningNik Spirin
This document is a presentation about data science and artificial intelligence given by James G. Shanahan. It provides an outline that covers topics such as machine learning, data science applications, architecture, and future directions. Shanahan has over 25 years of experience in data science and currently works as an independent consultant and teaches at UC Berkeley. The presentation provides background on artificial intelligence and machine learning techniques as well as examples of their successful applications.
Agile Data Science is a lean methodology that is adopted from Agile Software Development. At the core it centers around people, interactions, and building minimally viable products to ship fast and often to solicit customer feedback. In this presentation, I describe how this work was done in the past with examples. Get started today with our help by visiting http://www.alpinenow.com
Is Agile Data Science just two buzzwords put together? I argue that agile is a very practical and applicable methodology, that does work well in the real world for all sorts of Analytics and Data Science workflows.
http://theinnovationenterprise.com/summits/digital-web-analytics-summit-london-2015/schedule
Estamos presenciando inovações tecnológicas que possibilitam utilizar ciência dos dados sem a necessidade de antecipar grandes investimentos. Este contexto facilita a adoção de práticas e valores ágeis que encorajam a antecipação de insights e aprendizado contínuo. Nesta palestra, iremos abordar temas como times multi-funcionais, práticas ágeis de engenharia de software e desenvolvimento iterativo, incremental e colaborativo no contexto de produtos e soluções de ciência dos dados.
Ofer Ron, senior data scientist at LivePerson.
Recently, I've had the pleasure of presenting an introduction to Data Science and data driven products at DevconTLV
I focused this talk around the basic ideas of data science, not the technology used, since I thought that far too many times companies and developers rush to play around with "big data" related technologies, instead of figuring out what questions they want to answer, and whether these answers form a successful product.
This talk discusses techniques for dealing with messy and imperfect data. Three examples are provided: (1) commercial real estate data with validity issues that were addressed through consensus, imputation, outlier removal and OCR; (2) click data with cookie issues that required transformations like sampling and clustering after preprocessing; (3) digital media data with access issues that necessitated working with insufficient data using decomposition and seeking more data. The overall message is to focus on the problem, identify data limitations, and apply creative solutions like reworking the data.
These are slides for a guest talk I gave for course 15.S14: Global Business of Artificial Intelligence and Robotics (GBAIR) taught in Spring 2017. Here is the YouTube video (filmed in 360/VR): https://youtu.be/s3MuSOl1Rog
Slides for the course Big Data and Automated Content Analysis, in which students of the social sciences (communication science) learn how to conduct analyses using Python. First Meeting.
Introduction to Data Science and Large-scale Machine LearningNik Spirin
This document is a presentation about data science and artificial intelligence given by James G. Shanahan. It provides an outline that covers topics such as machine learning, data science applications, architecture, and future directions. Shanahan has over 25 years of experience in data science and currently works as an independent consultant and teaches at UC Berkeley. The presentation provides background on artificial intelligence and machine learning techniques as well as examples of their successful applications.
Agile Data Science is a lean methodology that is adopted from Agile Software Development. At the core it centers around people, interactions, and building minimally viable products to ship fast and often to solicit customer feedback. In this presentation, I describe how this work was done in the past with examples. Get started today with our help by visiting http://www.alpinenow.com
Is Agile Data Science just two buzzwords put together? I argue that agile is a very practical and applicable methodology, that does work well in the real world for all sorts of Analytics and Data Science workflows.
http://theinnovationenterprise.com/summits/digital-web-analytics-summit-london-2015/schedule
Estamos presenciando inovações tecnológicas que possibilitam utilizar ciência dos dados sem a necessidade de antecipar grandes investimentos. Este contexto facilita a adoção de práticas e valores ágeis que encorajam a antecipação de insights e aprendizado contínuo. Nesta palestra, iremos abordar temas como times multi-funcionais, práticas ágeis de engenharia de software e desenvolvimento iterativo, incremental e colaborativo no contexto de produtos e soluções de ciência dos dados.
Martina Pugliese gives a presentation about her background in physics and transition to a career in data science. She completed degrees in physics, including a PhD exploring how natural language evolves over time. She did a data science bootcamp to gain industry skills. Her current role involves using machine learning and data visualization to understand user behavior on a fashion app and improve personalization, retention, and other business metrics. Data science draws on her physics training in modeling reality mathematically and dealing with large datasets, combining academic rigor with an application to real-world problems.
This document discusses how security teams are overwhelmed by large volumes of data from security alerts and indicators. It proposes that graph algorithms can help identify related alerts and events that should be investigated together, such as those targeting the same users or part of the same attack. The document provides examples of how community detection, centrality analysis, and other graph algorithms run on preprocessed security data can help prioritize work and generate new threat indicators.
This document discusses big data and data science. It addresses three main points:
1) Big data methods and algorithms can be useful for smaller datasets as well as large ones.
2) To successfully extract insights from data requires a team with a variety of skills, including business and domain knowledge.
3) For HR in particular, big data can help determine the optimal time to approach potential candidates by analyzing patterns in their job seeking activities online.
The document proposes two ideas to improve search engines: 1) Allow webmasters to block specific websites or keywords from being crawled or indexed on their pages to provide more relevant search results. It also suggests building a search engine that provides results for related links in addition to keywords by analyzing outgoing links and traffic to find similar websites. The document uses an example of a search on Google India for "career objectives" returning an irrelevant US site to illustrate issues with current search engine indexes.
The document discusses machine learning techniques for analyzing big data. It outlines three tenants of success: prediction, optimization, and automation. Various machine learning models are examined, including linear models, decision trees, neural networks, and clustering. Implementing machine learning algorithms in Hadoop distributed environments is also discussed. Optimization techniques like evolutionary algorithms are presented. Regularly adapting models with updated data is recommended to keep analyses current.
H2O World - Intro to Data Science with Erin LedellSri Ambati
This document provides an introduction to data science. It defines data science as using data to solve problems through the scientific method. The roles of data scientists, data analysts, and data engineers on a data science team are discussed. Popular tools for data science include Python, R, and APIs that connect data processing engines. Machine learning algorithms are used to perform tasks like classification, regression, and clustering by learning from data rather than being explicitly programmed. Deep learning and ensemble methods are also introduced. Resources for learning more about data science and machine learning are provided.
Identifying sick cannabis with ai defcon 2018Harry Moreno
This talk covers how we built a predictive model for plant disease in Cannabis. We cover methodology, model training, model evaluation, deployment and ideas for improving the model. Check out the deployed model at https://chronicsickness.com
4. Document Discovery with Graph Data ScienceNeo4j
This document discusses using graphs for document discovery and data science. Graphs can combine structured and unstructured data, show relationships between information, and enable visual exploration of data. Graph algorithms can enhance graphs by identifying important entities, predicting unknown relationships, and supporting analytical use cases like discovery. The document advocates building a graph from documents, applying graph analytics to aid discovery, enabling search and exploration of the graph, and developing applications to integrate these capabilities.
David Lary and Rick McGeer presented on envisioning the future of the internet and big data visualization. They discussed how the Global Environment for Network Innovations (GENI) and US Ignite are helping to build an internet that can visualize big data fast from any device anywhere in a collaborative manner. The goal is to achieve response times of 0.15 seconds or less to enable an interactive user experience.
A presentation delivered by Mohammed Barakat on the 2nd Jordanian Continuous Improvement Open Day in Amman. The presentation is about Data Science and was delivered on 3rd October 2015.
This is my experience of going to my first data hackathon, Govhack 2015 and what it taught me.
A Hackathon is an event where you gather a heap of resources and people, form small teams and try to deliver as fully realised solution to a set theme or problem in a short intense amount of time.
Normally a hackathon is focused on delivering working software, but in the case of a data hackathon you work from a heap of datasets and try to deliver something of value, that can be working software, but often is something else. For this reason non coders can participate in a data hack easily.
Another difference is a hackathon normally revolves around creating some sort of business (be that profit or non-profit) idea and validating it.
Data hackathons are about understanding and realising value from data, and that value can often just be delivering better access to the information the data represents.
The role of data engineering in data science and analytics practiceJoseph Benjamin Ilagan
The role of data engineering in data science and analytics practice. Presented in the Philippine Software Industry Association (PSIA) 40th Enablement Seminar.
Slides template by Slides Carnival (https://www.slidescarnival.com/)
This document summarizes an introductory presentation on data science. It introduces the presenter and their background in data and analytics. The goals of the presentation are to define what a data scientist is, how the field has emerged, and how to become one. It discusses the growing demand and salaries for data scientists. Examples are given of how data science has been applied at companies like LinkedIn and Netflix. The presentation covers big data, Hadoop, data processing techniques, machine learning algorithms, and tools used in data science. Finally, attendees are encouraged to consider Thinkful's data science bootcamp program.
Worst Practices in Artificial IntelligenceWilliam Tsoi
In this talk I discuss six "worst practices" in Artificial Intelligence, so that you don't make the same mistakes as you embark on your AI and Machine Learning journey!
The full talk (in cantonese) is here: https://youtu.be/NIIztmpA6Hc?t=1172
Here's a starting template for anyone presenting data science topic to elementary school students. Exhibits how fun the field is and how the job market for these skills is excellent. Includes hyperlinks to various examples of interesting interactive visualizations.
This document provides an introduction to machine learning. It begins with an agenda that lists topics such as introduction, theory, top 10 algorithms, recommendations, classification with naive Bayes, linear regression, clustering, principal component analysis, MapReduce, and conclusion. It then discusses what big data is and how data is accumulating at tremendous rates from various sources. It explains the volume, variety, and velocity aspects of big data. The document also provides examples of machine learning applications and discusses extracting insights from data using various algorithms. It discusses issues in machine learning like overfitting and underfitting data and the importance of testing algorithms. The document concludes that machine learning has vast potential but is very difficult to realize that potential as it requires strong mathematics skills.
Who is a Data Scientist? | How to become a Data Scientist? | Data Science Cou...Edureka!
** Data Scientist Masters' Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka PPT on "Who is a Data Scientist" will help you understand what a data scientist does, their roles and responsibilities, and what the data science profile is all about. You will also get a glimpse of what kind of salary packages and career opportunities the data science domain offers.
Below topics are covered in this PPT:
Who is a Data Scientist?
What is Data Science?
Who can take up Data Science?
How to become a Data Scientist?
Data Scientist Skills
Data Scientist Roles & Responsibilities
Data Scientist Salary
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
Students will be able to work as
research assistants in academia and industry.
Entrepreneurship: Students can start their own
data science consulting firms or startups.
Higher Education: Students will be well
prepared for advanced degrees in Data Science,
Computer Science, Statistics or related fields.
Data Science Tutorial | Introduction To Data Science | Data Science Training ...Edureka!
This Edureka Data Science tutorial will help you understand in and out of Data Science with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. Why Data Science?
2. What is Data Science?
3. Who is a Data Scientist?
4. How a Problem is Solved in Data Science?
5. Data Science Components
There are two main approaches to building a data warehouse - top-down and bottom-up. The top-down approach builds a centralized data repository first and then creates subject-specific data marts from it. The bottom-up approach incrementally builds individual data marts and then integrates them. Successful data warehouse design considers data sources, usage requirements, and takes a holistic, iterative approach addressing data content, metadata, distribution, tools, and technical factors like hardware, DBMS, and communication infrastructure.
Introduction of Artificial Intelligence and Machine Learning bigdata trunk
A Workshop to introduce Artificial Intelligence and Machine Learning for beginners. It starts with basics , terminologies and concepts for machine learning, compares with deep learning and artificial Intelligence. Highlights the ML and AI offerings like Jupyter Notebook, Azure ML , Amazon Sagemaker, Tensorflow etc.
Introduction to Machine Learning with Python ( PDFDrive.com ).pdfbisan3
This document is the preface to the book "Introduction to Machine Learning with Python" by Andreas C. Müller and Sarah Guido. The preface provides an overview of the book, including who it is intended for, why the authors wrote it, how it is organized, and conventions used. It is an introductory book on machine learning using Python and scikit-learn that requires no previous experience, focusing on practical applications rather than advanced mathematics.
Martina Pugliese gives a presentation about her background in physics and transition to a career in data science. She completed degrees in physics, including a PhD exploring how natural language evolves over time. She did a data science bootcamp to gain industry skills. Her current role involves using machine learning and data visualization to understand user behavior on a fashion app and improve personalization, retention, and other business metrics. Data science draws on her physics training in modeling reality mathematically and dealing with large datasets, combining academic rigor with an application to real-world problems.
This document discusses how security teams are overwhelmed by large volumes of data from security alerts and indicators. It proposes that graph algorithms can help identify related alerts and events that should be investigated together, such as those targeting the same users or part of the same attack. The document provides examples of how community detection, centrality analysis, and other graph algorithms run on preprocessed security data can help prioritize work and generate new threat indicators.
This document discusses big data and data science. It addresses three main points:
1) Big data methods and algorithms can be useful for smaller datasets as well as large ones.
2) To successfully extract insights from data requires a team with a variety of skills, including business and domain knowledge.
3) For HR in particular, big data can help determine the optimal time to approach potential candidates by analyzing patterns in their job seeking activities online.
The document proposes two ideas to improve search engines: 1) Allow webmasters to block specific websites or keywords from being crawled or indexed on their pages to provide more relevant search results. It also suggests building a search engine that provides results for related links in addition to keywords by analyzing outgoing links and traffic to find similar websites. The document uses an example of a search on Google India for "career objectives" returning an irrelevant US site to illustrate issues with current search engine indexes.
The document discusses machine learning techniques for analyzing big data. It outlines three tenants of success: prediction, optimization, and automation. Various machine learning models are examined, including linear models, decision trees, neural networks, and clustering. Implementing machine learning algorithms in Hadoop distributed environments is also discussed. Optimization techniques like evolutionary algorithms are presented. Regularly adapting models with updated data is recommended to keep analyses current.
H2O World - Intro to Data Science with Erin LedellSri Ambati
This document provides an introduction to data science. It defines data science as using data to solve problems through the scientific method. The roles of data scientists, data analysts, and data engineers on a data science team are discussed. Popular tools for data science include Python, R, and APIs that connect data processing engines. Machine learning algorithms are used to perform tasks like classification, regression, and clustering by learning from data rather than being explicitly programmed. Deep learning and ensemble methods are also introduced. Resources for learning more about data science and machine learning are provided.
Identifying sick cannabis with ai defcon 2018Harry Moreno
This talk covers how we built a predictive model for plant disease in Cannabis. We cover methodology, model training, model evaluation, deployment and ideas for improving the model. Check out the deployed model at https://chronicsickness.com
4. Document Discovery with Graph Data ScienceNeo4j
This document discusses using graphs for document discovery and data science. Graphs can combine structured and unstructured data, show relationships between information, and enable visual exploration of data. Graph algorithms can enhance graphs by identifying important entities, predicting unknown relationships, and supporting analytical use cases like discovery. The document advocates building a graph from documents, applying graph analytics to aid discovery, enabling search and exploration of the graph, and developing applications to integrate these capabilities.
David Lary and Rick McGeer presented on envisioning the future of the internet and big data visualization. They discussed how the Global Environment for Network Innovations (GENI) and US Ignite are helping to build an internet that can visualize big data fast from any device anywhere in a collaborative manner. The goal is to achieve response times of 0.15 seconds or less to enable an interactive user experience.
A presentation delivered by Mohammed Barakat on the 2nd Jordanian Continuous Improvement Open Day in Amman. The presentation is about Data Science and was delivered on 3rd October 2015.
This is my experience of going to my first data hackathon, Govhack 2015 and what it taught me.
A Hackathon is an event where you gather a heap of resources and people, form small teams and try to deliver as fully realised solution to a set theme or problem in a short intense amount of time.
Normally a hackathon is focused on delivering working software, but in the case of a data hackathon you work from a heap of datasets and try to deliver something of value, that can be working software, but often is something else. For this reason non coders can participate in a data hack easily.
Another difference is a hackathon normally revolves around creating some sort of business (be that profit or non-profit) idea and validating it.
Data hackathons are about understanding and realising value from data, and that value can often just be delivering better access to the information the data represents.
The role of data engineering in data science and analytics practiceJoseph Benjamin Ilagan
The role of data engineering in data science and analytics practice. Presented in the Philippine Software Industry Association (PSIA) 40th Enablement Seminar.
Slides template by Slides Carnival (https://www.slidescarnival.com/)
This document summarizes an introductory presentation on data science. It introduces the presenter and their background in data and analytics. The goals of the presentation are to define what a data scientist is, how the field has emerged, and how to become one. It discusses the growing demand and salaries for data scientists. Examples are given of how data science has been applied at companies like LinkedIn and Netflix. The presentation covers big data, Hadoop, data processing techniques, machine learning algorithms, and tools used in data science. Finally, attendees are encouraged to consider Thinkful's data science bootcamp program.
Worst Practices in Artificial IntelligenceWilliam Tsoi
In this talk I discuss six "worst practices" in Artificial Intelligence, so that you don't make the same mistakes as you embark on your AI and Machine Learning journey!
The full talk (in cantonese) is here: https://youtu.be/NIIztmpA6Hc?t=1172
Here's a starting template for anyone presenting data science topic to elementary school students. Exhibits how fun the field is and how the job market for these skills is excellent. Includes hyperlinks to various examples of interesting interactive visualizations.
This document provides an introduction to machine learning. It begins with an agenda that lists topics such as introduction, theory, top 10 algorithms, recommendations, classification with naive Bayes, linear regression, clustering, principal component analysis, MapReduce, and conclusion. It then discusses what big data is and how data is accumulating at tremendous rates from various sources. It explains the volume, variety, and velocity aspects of big data. The document also provides examples of machine learning applications and discusses extracting insights from data using various algorithms. It discusses issues in machine learning like overfitting and underfitting data and the importance of testing algorithms. The document concludes that machine learning has vast potential but is very difficult to realize that potential as it requires strong mathematics skills.
Who is a Data Scientist? | How to become a Data Scientist? | Data Science Cou...Edureka!
** Data Scientist Masters' Program: https://www.edureka.co/masters-program/data-scientist-certification **
This Edureka PPT on "Who is a Data Scientist" will help you understand what a data scientist does, their roles and responsibilities, and what the data science profile is all about. You will also get a glimpse of what kind of salary packages and career opportunities the data science domain offers.
Below topics are covered in this PPT:
Who is a Data Scientist?
What is Data Science?
Who can take up Data Science?
How to become a Data Scientist?
Data Scientist Skills
Data Scientist Roles & Responsibilities
Data Scientist Salary
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
Students will be able to work as
research assistants in academia and industry.
Entrepreneurship: Students can start their own
data science consulting firms or startups.
Higher Education: Students will be well
prepared for advanced degrees in Data Science,
Computer Science, Statistics or related fields.
Data Science Tutorial | Introduction To Data Science | Data Science Training ...Edureka!
This Edureka Data Science tutorial will help you understand in and out of Data Science with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. Why Data Science?
2. What is Data Science?
3. Who is a Data Scientist?
4. How a Problem is Solved in Data Science?
5. Data Science Components
There are two main approaches to building a data warehouse - top-down and bottom-up. The top-down approach builds a centralized data repository first and then creates subject-specific data marts from it. The bottom-up approach incrementally builds individual data marts and then integrates them. Successful data warehouse design considers data sources, usage requirements, and takes a holistic, iterative approach addressing data content, metadata, distribution, tools, and technical factors like hardware, DBMS, and communication infrastructure.
Introduction of Artificial Intelligence and Machine Learning bigdata trunk
A Workshop to introduce Artificial Intelligence and Machine Learning for beginners. It starts with basics , terminologies and concepts for machine learning, compares with deep learning and artificial Intelligence. Highlights the ML and AI offerings like Jupyter Notebook, Azure ML , Amazon Sagemaker, Tensorflow etc.
Introduction to Machine Learning with Python ( PDFDrive.com ).pdfbisan3
This document is the preface to the book "Introduction to Machine Learning with Python" by Andreas C. Müller and Sarah Guido. The preface provides an overview of the book, including who it is intended for, why the authors wrote it, how it is organized, and conventions used. It is an introductory book on machine learning using Python and scikit-learn that requires no previous experience, focusing on practical applications rather than advanced mathematics.
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Topic: Generating Synthetic Data with Generative Adversarial Networks: Opportunities and Challenges
Limited data access continues to be a barrier to data-driven product development. In this talk, we explore if and how generative adversarial networks (GANs) can be used to incentivize data sharing by enabling a generic framework for sharing synthetic datasets with minimal expert knowledge.
We identify key challenges of existing GAN approaches with respect to fidelity (e.g., capturing complex multidimensional correlations, mode collapse) and privacy (i.e., existing guarantees are poorly understood and can sacrifice fidelity).
To address fidelity challenges, we discuss our experiences designing a custom workflow called DoppelGANger and demonstrate that across diverse real-world datasets (e.g., bandwidth measurements, cluster requests, web sessions) and use cases (e.g., structural characterization, predictive modeling, algorithm comparison), DoppelGANger achieves up to 43% better fidelity than baseline models.
With respect to privacy, we identify fundamental challenges with both classical notions of privacy as well as recent advances to improve the privacy properties of GANs, and suggest a potential roadmap for addressing these challenges.
Talk on Data Discovery and Metadata by Mark Grover from July 2019.
Goes into detail of the problem, build/buy/adopt analysis and Lyft's solution - Amundsen, along with thoughts on the future.
This document provides an overview of getting started with data science using Python. It discusses what data science is, why it is in high demand, and the typical skills and backgrounds of data scientists. It then covers popular Python libraries for data science like NumPy, Pandas, Scikit-Learn, TensorFlow, and Keras. Common data science steps are outlined including data gathering, preparation, exploration, model building, validation, and deployment. Example applications and case studies are discussed along with resources for learning including podcasts, websites, communities, books, and TV shows.
Concepts, use cases and principles to build big data systems (1)Trieu Nguyen
1) Introduction to the key Big Data concepts
1.1 The Origins of Big Data
1.2 What is Big Data ?
1.3 Why is Big Data So Important ?
1.4 How Is Big Data Used In Practice ?
2) Introduction to the key principles of Big Data Systems
2.1 How to design Data Pipeline in 6 steps
2.2 Using Lambda Architecture for big data processing
3) Practical case study : Chat bot with Video Recommendation Engine
4) FAQ for student
How will AI and analytics change life in the next 25 years? In this episode, we look forward to the next 25 years and will share predictions about the technological innovations prevalent then based on a projection of AI and analytics forward.
Relationships Matter: Using Connected Data for Better Machine LearningNeo4j
Relationships are highly predictive of behavior, yet most data science models overlook this information because it's difficult to extract network structure for use in machine learning (ML).
With graphs, relationships are embedded in the data itself, making it practical to add these predictive capabilities to your existing practices.
That’s why we’re presenting and demoing the use of graph-native ML to make breakthrough predictions. This will cover:
- Different approaches to graph feature engineering, from queries and algorithms to embeddings
- How ML techniques leverage everything from classical network science to deep learning and graph convolutional neural networks
- How to generate representations of your graph using graph embeddings, create ML models for link prediction or node classification, and apply these models to add missing information to an existing graph/incoming data
- Why no-code visualization and prototyping is important
Part of the ongoing effort with Skater for enabling better Model Interpretation for Deep Neural Network models presented at the AI Conference.
https://conferences.oreilly.com/artificial-intelligence/ai-ny/public/schedule/detail/65118
This document provides a summary of key topics covered during a multi-day AI training session. Day 1 covered introductions to AI and what it can and cannot do. Day 2 focused on selecting AI projects and the steps for a successful machine learning project. Day 3 discussed AI strategy, governance, management, ethics and leadership. The remainder of the document recaps machine learning models and neural networks, discusses building vs buying solutions, reviews cloud architectures and services, and covers ethics, privacy and risk considerations for human interfaces.
Setting up a Big Data Team requires best practices including building a team with diverse skills in areas like math, computer science, statistics and domain expertise. Data scientists fulfill key roles like generating prototypes to demonstrate ideas, decomposing problems, and communicating with stakeholders. Effective teams require data preparation, using tools like statistical systems and data management systems. Certification can increase a team's maturity, while frameworks like CRISP-DM and Six Sigma's DMAIC provide processes to optimize the data workflow. Talent management is also important to support the team over time.
Data Modeling for Security, Privacy and Data ProtectionKaren Lopez
Karen Lopez (@datchick/InfoAdvisors) 90-minute presentation on Data Security, Data Privacy, Compliance and how data modelers should discover, assess, and monitor these important data management responsibilities.
The Myths + Realities of Machine-Learning CybersecurityInterset
The document discusses artificial intelligence (AI) and machine learning concepts. It begins by defining AI as involving data, machine learning, and human interaction. It notes what AI is not capable of and provides a timeline of past AI failures. Specific examples discussed include IBM's Watson system and how it works by analyzing large datasets to answer questions. The document also discusses machine learning applications, common machine learning techniques, and where companies want to use AI. It concludes by discussing the Turing Test for machine intelligence.
How to crack Big Data and Data Science rolesUpXAcademy
How to crack Big Data and Data Science roles is the flagship event of UpX Academy. This slide was used for the event on 10th Sept that was attended by hundreds of participants globally.
DutchMLSchool. Logistic Regression, Deepnets, Time SeriesBigML, Inc
DutchMLSchool. Logistic Regression, Deepnets, and Time Series (Supervised Learning II) - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
This document provides an introduction to machine learning, including:
- It discusses how the human brain learns to classify images and how machine learning systems are programmed to perform similar tasks.
- It provides an example of image classification using machine learning and discusses how machines are trained on sample data and then used to classify new queries.
- It outlines some common applications of machine learning in areas like banking, biomedicine, and computer/internet applications. It also discusses popular machine learning algorithms like Bayes networks, artificial neural networks, PCA, SVM classification, and K-means clustering.
This document discusses best practices for big data analytics using machine learning. It outlines considerations for different types of users like business professionals, IT professionals, and data scientists. It then covers descriptive, predictive, and prescriptive analytics and questions each can answer. The document demonstrates predictive churn modeling using PMML and discusses deploying models from development to production. It concludes with an agenda for a presentation that includes a Q&A section.
Introduction to machine learning with GPUsCarol McDonald
The document provides an introduction to machine learning concepts including supervised and unsupervised learning. It discusses classification and regression as examples of supervised learning techniques and clustering as an example of unsupervised learning. It also provides an overview of deep learning using neural networks and examples of convolutional neural networks and recurrent neural networks. The document emphasizes how GPUs have accelerated machine learning by enabling parallel processing.
Getting started with GCP ( Google Cloud Platform)bigdata trunk
This document provides an overview and introduction to Google Cloud Platform (GCP). It begins with introductions and an agenda. It then discusses cloud computing concepts like deployment models and service models. It provides details on specific GCP computing, storage, machine learning, and other services. It describes how to set up Qwiklabs to do hands-on labs with GCP. Finally, it discusses next steps like training and certification for expanding GCP knowledge.
A guide to understanding the coding interview process at top tech companies like Google, Facebook or a unicorn startup like Uber.
Checkout our Bootcamps to help in coding, Data structures and algorithms, behavior and situational interview
http://programminginterviewprep.com/
Big Data Ecosystem after Spark as part of session hosted by Big data Trunk (www.BigDataTrunk.com) for below Meetup group
https://www.meetup.com/Big-Data-IOT-101/
You can subscribe to our channel and see other videos at
https://www.youtube.com/channel/UCp7pR7BJNnRueEuLSau0TzA
Introduction to machine learning algorithmsbigdata trunk
Introduction to main Machine Learning Algorithms as part of session hosted by Big data Trunk (www.BigDataTrunk.com) for below Meetup group
https://www.meetup.com/Big-Data-IOT-101/
Presented by Antony Ross
You can subscribe to our channel and see other videos at
https://www.youtube.com/channel/UCp7pR7BJNnRueEuLSau0TzA
Data Science Process Walkthrough as part of session hosted by Big data Trunk (www.BigDataTrunk.com) for below Meetup group
https://www.meetup.com/Big-Data-IOT-101/
Presented by Antony Ross
Machine Learning Intro for Anyone and Everyonebigdata trunk
A fun and math free introduction to Machine Learning. It provides a step to step approach for everyone to get started with Machine Learning using Microsoft Azure ML
This was presented at
https://www.siliconvalley-codecamp.com/Session/2017/machine-learning-intro-for-anyone-and-everyone
You can subscribe to our channel and see other videos at
https://www.youtube.com/channel/UCp7pR7BJNnRueEuLSau0TzA
Slides from Apache Spark Workshop from Big Data Trunk. It provides a fun way to introduce Apache Spark in the big data world.
www.BigDataTrunk.com
Youtube channel
https://www.youtube.com/channel/UCp7pR7BJNnRueEuLSau0TzA
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"sameer shah
Embark on a captivating financial journey with 'Financial Odyssey,' our hackathon project. Delve deep into the past performance of two companies as we employ an array of financial statement analysis techniques. From ratio analysis to trend analysis, uncover insights crucial for informed decision-making in the dynamic world of finance."
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
10. www.bigdatatrunk.com
History of AI
• 1950 – Turing Test
• 1951 – First Neural Network
• 1967 – “Nearest Neighbor”
Algorithm is written
• 1974 – First AI winter
• 1979 – Stanford Cart
• 1996 – IBM Deep Blue
beats Garry Kasparov
Source https://www.bbc.com/timelines/zypd97h
• 2006 - Geoffrey Hinton coins the
term “deep learning”
• 2014 – Facebook develops
DeepFace & Google Buys
DeepMind
• 2015 – Amazon, Google & Microsoft
ML offerings
• 2016 – AlphaGo beats Lee Sedol
• 2030 – Singularity?
18. Supervised Learning
18 www.bigdatatrunk.com
Diameter, Thickness à
Features
Currency à Label
Diameter Thickness Currency
1.0430 inches 0.0790 inches US dollar coin
1.0433 inches 0.07680 inches Canadian dollar coin
(Loonie)
0.9154 inches 0.09173 inches One Euro coin
• Supervised learning
uses labeled data to
train the model
• Forecast an outcome
37. www.bigdatatrunk.com
The Data Science Process
(DIAPERS)
Define
Problem
Ingest
Data
Analyze
Data
Prepare
Data
Evaluate
Models
Refine
Model Ship It
What data
should I use?
Is it
labeled?
Is data complete, clean,
does it have coverage?
Which
algorithms
should you
use?
What level of
performance
is acceptable?
Deploy the
Model and
make
predictions
What are we
trying to
achieve?
Is it
labeled?
44. www.bigdatatrunk.com
AI vs ML vs DL
AI ML DL
1950 1980 2006
Driverless car, Alexa Recommendation,
Fraud detection, Image
recognition
Color B/W picture, add
sound to video
Uses ML,DL and
repositories of data
Works with all sizes of
data but needs feature
engineering
Needs large datasets &
compute capability and
takes long time to learn
Linear Regression,
Decision Regression, K
– means Clustering
CNN, ANN
(TensorFlow and Keras)
46. Artificial Intelligence - Types
46 www.bigdatatrunk.com
Artificial
Narrow
Intelligence
Artificial
General
Intelligence
• AI that is good at one specified task which they
are trained on
• Examples – predicting home prices based on
historical data, categorize email as SPAM
• Lot of buzz about the progress in AI, but this is
only in ANI (Artificial Narrow Intelligence)
• Ultimate goal – make the computer smart or
smarter than the humans
• AI that can perform intelligent tasks as humans
• Raises fears about job loses, “terminator” like
scenarios
• Still far from reaching the goal of Artificial
General Intelligence (AGI)