Introduces some key ideas for deploying machine learning based predictive analytics models effectively. Based on the book "Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, worked Examples & Case Studies" (www.machinelearningbook.com)
AI should be Fair, Accountable and Transparent (FAT* AI), hence it's crucial to raise awareness among these topics not only among machine learning practitioners but among the entire population, as ML systems can take life-changing decisions and influence our lives now more than ever.
This document provides guidance on how to write a journal article. It begins with an introduction to the presenter, Prof. Dr. Khalid Mahmood, who has extensive experience in research publication. The presentation then covers various aspects of writing a journal article, including preparing to write, identifying topics, structuring the article, writing different sections like introduction, methods, results and discussion. It provides details on what to include in each section and common mistakes to avoid. The presentation emphasizes writing clearly and ethically while following guidelines for research writing. It concludes with a checklist for reviewing one's own article.
This presentation begins with a specific issue in text mining that connect it with word embeddings. Later, the importance of the Wikipedia is highlighted and finally, lessons to be learned from the Wikipedia are discussed.
Serverless Machine Learning - Hanoi Google Next 2019Vũ Đào
This document summarizes serverless machine learning capabilities on Google Cloud Platform (GCP). It discusses machine learning workflows including understanding problems, data collection, preprocessing, modeling, evaluation and deployment. Specific GCP services are mentioned like Cloud Vision, Translate API, AutoML for automated model building, and AI Platform (ML Engine) for custom model development. Data processing techniques are reviewed such as data cleansing, exploration, profiling and issue resolution using tools like Datalab, BigQuery, and Dataflow. The document concludes with contact information for the presenter's company Eway, which provides big data services including recruitment applications using machine learning.
This document discusses how Hadoop can help solve challenges with key corporate data known as "small data". Small data refers to structured data that is critical to main business activities. It discusses issues with small data like multiple sources and definitions causing inconsistencies. It proposes using Hadoop to iteratively detect errors and inconsistencies in small data to allow normalization. Normalization combines subject knowledge and rules to make small data more consistent and meaningful for analysis. The document argues Hadoop provides a flexible, fast environment for data analytics that can help address requirements around understanding, preparing, and maintaining small but critical corporate data.
Top 7 Reasons why Maintenance Work Orders are Closed Out AccuratelyRicky Smith CMRP, CMRT
Closing out work orders accurately is critical for leadership to make the “right decisions at the right time with accurate data” and it can only occur if work orders are “Closed with the Right Information/Data”.
If metrics and Key Performance Indicators are so important where are people pulling the data from without their work orders having the right data on them when they are closed into that dark hole called the CMMS or EAM.
Without good data you are lost and probably are making decisions based on passion and not facts.
[DSC Europe 22] Avoid mistakes building AI products - Karol PrzystalskiDataScienceConferenc1
Based on Gartner's research, 85% of AI projects fail. In this talk, we show the most common mistakes made by the managers, developers, and data scientists while building AI products. We go through ten case studies of products that failed and analyze the reasons for each failure. We also present how to avoid such mistakes and deliver a successful AI product by introducing a few lifecycle changes.
This document discusses forward-looking and predictive metrics that can be used for recruiting. It begins by defining key terms like historical, real-time, and predictive metrics. It then discusses reasons for using traditional metrics, such as increased business results when data-driven decision making is used. Examples of predictive recruiting metrics are also provided, such as predicting changing source effectiveness and upcoming talent availability. The document concludes by outlining elements that make a predictive metric actionable, such as listing revenue impact and recommended actions.
AI should be Fair, Accountable and Transparent (FAT* AI), hence it's crucial to raise awareness among these topics not only among machine learning practitioners but among the entire population, as ML systems can take life-changing decisions and influence our lives now more than ever.
This document provides guidance on how to write a journal article. It begins with an introduction to the presenter, Prof. Dr. Khalid Mahmood, who has extensive experience in research publication. The presentation then covers various aspects of writing a journal article, including preparing to write, identifying topics, structuring the article, writing different sections like introduction, methods, results and discussion. It provides details on what to include in each section and common mistakes to avoid. The presentation emphasizes writing clearly and ethically while following guidelines for research writing. It concludes with a checklist for reviewing one's own article.
This presentation begins with a specific issue in text mining that connect it with word embeddings. Later, the importance of the Wikipedia is highlighted and finally, lessons to be learned from the Wikipedia are discussed.
Serverless Machine Learning - Hanoi Google Next 2019Vũ Đào
This document summarizes serverless machine learning capabilities on Google Cloud Platform (GCP). It discusses machine learning workflows including understanding problems, data collection, preprocessing, modeling, evaluation and deployment. Specific GCP services are mentioned like Cloud Vision, Translate API, AutoML for automated model building, and AI Platform (ML Engine) for custom model development. Data processing techniques are reviewed such as data cleansing, exploration, profiling and issue resolution using tools like Datalab, BigQuery, and Dataflow. The document concludes with contact information for the presenter's company Eway, which provides big data services including recruitment applications using machine learning.
This document discusses how Hadoop can help solve challenges with key corporate data known as "small data". Small data refers to structured data that is critical to main business activities. It discusses issues with small data like multiple sources and definitions causing inconsistencies. It proposes using Hadoop to iteratively detect errors and inconsistencies in small data to allow normalization. Normalization combines subject knowledge and rules to make small data more consistent and meaningful for analysis. The document argues Hadoop provides a flexible, fast environment for data analytics that can help address requirements around understanding, preparing, and maintaining small but critical corporate data.
Top 7 Reasons why Maintenance Work Orders are Closed Out AccuratelyRicky Smith CMRP, CMRT
Closing out work orders accurately is critical for leadership to make the “right decisions at the right time with accurate data” and it can only occur if work orders are “Closed with the Right Information/Data”.
If metrics and Key Performance Indicators are so important where are people pulling the data from without their work orders having the right data on them when they are closed into that dark hole called the CMMS or EAM.
Without good data you are lost and probably are making decisions based on passion and not facts.
[DSC Europe 22] Avoid mistakes building AI products - Karol PrzystalskiDataScienceConferenc1
Based on Gartner's research, 85% of AI projects fail. In this talk, we show the most common mistakes made by the managers, developers, and data scientists while building AI products. We go through ten case studies of products that failed and analyze the reasons for each failure. We also present how to avoid such mistakes and deliver a successful AI product by introducing a few lifecycle changes.
This document discusses forward-looking and predictive metrics that can be used for recruiting. It begins by defining key terms like historical, real-time, and predictive metrics. It then discusses reasons for using traditional metrics, such as increased business results when data-driven decision making is used. Examples of predictive recruiting metrics are also provided, such as predicting changing source effectiveness and upcoming talent availability. The document concludes by outlining elements that make a predictive metric actionable, such as listing revenue impact and recommended actions.
How To Get Into Data Science & Analytics - 2nd Talk - feliperego.com.auFelipe Rego
These are the slides from my second talk at Academy Xi on How to Get Started in Data Science and Analytics given on 3 Oct 2018. On the day, I had the pleasure of having Joel Stein from Precision Sourcing and his team presenting with me once again. And this time I had the absolute pleasure of having Con Georgelos also present and talking with me on the day. Also, big thank you to Hazel Cooper and Byron Allen for providing valuable input during the talk and also in the content. Finally, thank you yo Academy Xi for hosting us.
How To Get Into Data Science & Analytics - feliperego.com.auFelipe Rego
These are the slides from my talk at Academy Xi on How to Get Started in Data Science and Analytics. On the day, I had the pleasure of having Joel Stein from Precision Sourcing and his team presenting with me. Also, big thank you to Byron Allen for providing valuable content. Finally, thank you yo Academy Xi for hosting us.
DataCamp is an interactive online learning platform for data science with over 120,000 registered users. It addresses the shortage of data science professionals by providing learn-by-doing training in tools like R, SQL, Python and Excel. DataCamp has experienced strong growth, completing over 4.6 million exercises, and is pursuing a freemium business model with individual and enterprise subscriptions.
Set the Hiring Managers’ Expectations: Using Big Data to answer Big Questions...Textkernel
Presentation by Abdel Tefridj at Textkernel's Conference Intelligent Machines and the Future of Recruitment on 2 June 2016 in Amsterdam.
Abdel shares some client scenarios when data was the key element in the decision making process for recruitment challenges. You can become a better partner with hiring managers when they are informed about the latest trends in the marketplace using supply, demand and compensation data. Learn how to use big data to make you a stronger leader and contributor.
This document provides an overview of artificial intelligence (AI) and machine learning. It begins by defining AI as computer systems able to perform cognitive tasks like reasoning, decision making, perception, and language understanding. It then discusses what AI is good at, including classification, pattern recognition, prediction, and information retrieval. The document also covers different types of machine learning algorithms like supervised and unsupervised learning. It aims to demystify key AI concepts and discuss opportunities for applying AI in the chemical industry.
This document provides an overview and introduction to big data. It discusses the technical challenges of big data including issues of volume, variety, velocity and veracity. It also discusses solutions like Hadoop, MapReduce, and big data databases. Additionally, it covers big data analytics including different levels of analytics maturity and techniques like data mining, machine learning, and predictive analytics. Finally, it provides resources for learning more about big data including online courses, sandbox environments, open source tools, and public datasets.
Machine Learning Interviews_ Lessons from Both Sides - FSDL.pptxAbhinavSagar21
This document summarizes key points from a presentation on machine learning interviews. It discusses different machine learning jobs, common career paths in ML, tips for understanding interviewers' mindsets, the typical interview process, and strategies for improving one's chances of getting a job in ML. The presenter provides advice on preparing for interviews, examples of good and bad interview questions, alternative interview formats, and how companies evaluate candidates throughout the recruiting pipeline.
VLDB Slides on Making Sense of Applying ML to APIs Anant Jhingran
Sridhar and I argue that ML to make Databases work better (data prep, explainability, labeling etc.) is an important area of research, and we give examples supporting this through our work on APIs.
This document discusses gamification of learning and instruction. It begins with definitions of gamification, game-based learning, and simulation learning. Gamification uses elements of games to encourage learning, while game-based learning uses entire games to teach. Simulation learning creates realistic practice environments. The document then discusses various elements that can aid learning when incorporated into instruction gamification, such as story, challenge, feedback, and rewards. It provides examples of how these elements can be implemented and cites related research. Throughout, it offers suggestions for how readers can apply gamification concepts to their own instructional design.
This document provides a three-step guide for companies to get more value from their data and analytics. The three steps are:
1. Map your needs - Create a customized map of your business goals, assets, data, and skills gaps. This helps identify what is most important.
2. Assess your team - Measure your teams' analytics knowledge and skills to benchmark against your needs. Relevant testing is critical for planning training.
3. Train your people - Train your teams based on the gaps identified between your needs and their current skills. Off-the-shelf solutions often don't address specific company needs.
The document recommends using Corsair's Analytic Survey tool to map needs in
The document discusses the process of data science. It begins by defining the typical steps in a data science project as identifying a problem/business question, collecting and cleaning data, performing exploratory data analysis, using algorithms and machine learning, reporting answers/minimum viable products, and getting feedback to review results. It then lists "inconvenient truths" about data science, such as data never being clean and most time being spent on preparation. Finally, it provides an example of using import.io and MonkeyLearn tools for text analysis.
Top 10 Data Science Interview Questions in 2022.pptxinfosec train
Data science is rapidly dominating the world with its diverse usage in various industries. It currently plays a critical role in profit generation.
https://www.infosectrain.com/courses/data-science-with-python-and-r-certification-training/
Cool vs Creepy - Ethics and Data Science - Cooper 2FebCathy Cooper
The document discusses ethics in data science and provides examples of potential issues around biased data, anonymized data, lack of context, and data influencing behavior. It argues that data science tools and algorithms must be designed with fairness, legality, and understandability in mind. Human intervention is still needed to check that models are performing as intended and are not introducing unintended bias. Transparency into how data is collected and models are developed is important.
The document is a resume for Dillipan M. It summarizes his work experience of 14 months in data mining and web research. It also lists his educational qualifications including a B.E in Computer Science, technical skills like Oracle and Photoshop, an academic project on automatic database performance tuning, and internship experience in Linux training. The resume is submitted for seeking a challenging and responsible opportunity to further his career.
Data Science-Why?What?How? By Hari PrasadHari Prasad
This document provides an overview of data science from several perspectives:
- It introduces the presenter and their background/experience in fields related to data science such as social network analysis, big data analytics, and machine learning applications.
- The agenda outlines exploring why data science is important, what it involves technically, and how the data science process works using a standardized approach.
- Key aspects of what data science involves are discussed like machine learning algorithms, the data science skillset, and how machine learning techniques can be demystified and applied to problems.
- The process of data science is reviewed using a popular CRISP-DM framework and an IBM methodology, with examples of how questions can initiate a
Is Data Scientist the Sexiest Job of the 21st century?Edureka!
This document discusses why data science is the most sought after job in 2015. It outlines the career opportunities and advantages of data science jobs. Major companies are increasingly using data science to analyze large amounts of data and make predictions. Data science jobs have high salaries and growth potential, which is why they are considered very attractive careers. The document concludes that data science is indeed the sexiest job of the 21st century due to the high pay, difficultly of hiring qualified professionals, and importance of the field.
P 01 ins_analytics_ai_in_life_case_studies_2017_10_16_v12Vishwa Kolla
1. AI is helping life insurance companies improve processes like underwriting and claims handling by making them more efficient.
2. One case study showed how using consented health data and predictive models allowed underwriting decisions to be made in hours instead of weeks.
3. Another case study demonstrated how combining human and machine pattern detection helped identify suspicious claims patterns that could indicate fraud.
How To Get Into Data Science & Analytics - 2nd Talk - feliperego.com.auFelipe Rego
These are the slides from my second talk at Academy Xi on How to Get Started in Data Science and Analytics given on 3 Oct 2018. On the day, I had the pleasure of having Joel Stein from Precision Sourcing and his team presenting with me once again. And this time I had the absolute pleasure of having Con Georgelos also present and talking with me on the day. Also, big thank you to Hazel Cooper and Byron Allen for providing valuable input during the talk and also in the content. Finally, thank you yo Academy Xi for hosting us.
How To Get Into Data Science & Analytics - feliperego.com.auFelipe Rego
These are the slides from my talk at Academy Xi on How to Get Started in Data Science and Analytics. On the day, I had the pleasure of having Joel Stein from Precision Sourcing and his team presenting with me. Also, big thank you to Byron Allen for providing valuable content. Finally, thank you yo Academy Xi for hosting us.
DataCamp is an interactive online learning platform for data science with over 120,000 registered users. It addresses the shortage of data science professionals by providing learn-by-doing training in tools like R, SQL, Python and Excel. DataCamp has experienced strong growth, completing over 4.6 million exercises, and is pursuing a freemium business model with individual and enterprise subscriptions.
Set the Hiring Managers’ Expectations: Using Big Data to answer Big Questions...Textkernel
Presentation by Abdel Tefridj at Textkernel's Conference Intelligent Machines and the Future of Recruitment on 2 June 2016 in Amsterdam.
Abdel shares some client scenarios when data was the key element in the decision making process for recruitment challenges. You can become a better partner with hiring managers when they are informed about the latest trends in the marketplace using supply, demand and compensation data. Learn how to use big data to make you a stronger leader and contributor.
This document provides an overview of artificial intelligence (AI) and machine learning. It begins by defining AI as computer systems able to perform cognitive tasks like reasoning, decision making, perception, and language understanding. It then discusses what AI is good at, including classification, pattern recognition, prediction, and information retrieval. The document also covers different types of machine learning algorithms like supervised and unsupervised learning. It aims to demystify key AI concepts and discuss opportunities for applying AI in the chemical industry.
This document provides an overview and introduction to big data. It discusses the technical challenges of big data including issues of volume, variety, velocity and veracity. It also discusses solutions like Hadoop, MapReduce, and big data databases. Additionally, it covers big data analytics including different levels of analytics maturity and techniques like data mining, machine learning, and predictive analytics. Finally, it provides resources for learning more about big data including online courses, sandbox environments, open source tools, and public datasets.
Machine Learning Interviews_ Lessons from Both Sides - FSDL.pptxAbhinavSagar21
This document summarizes key points from a presentation on machine learning interviews. It discusses different machine learning jobs, common career paths in ML, tips for understanding interviewers' mindsets, the typical interview process, and strategies for improving one's chances of getting a job in ML. The presenter provides advice on preparing for interviews, examples of good and bad interview questions, alternative interview formats, and how companies evaluate candidates throughout the recruiting pipeline.
VLDB Slides on Making Sense of Applying ML to APIs Anant Jhingran
Sridhar and I argue that ML to make Databases work better (data prep, explainability, labeling etc.) is an important area of research, and we give examples supporting this through our work on APIs.
This document discusses gamification of learning and instruction. It begins with definitions of gamification, game-based learning, and simulation learning. Gamification uses elements of games to encourage learning, while game-based learning uses entire games to teach. Simulation learning creates realistic practice environments. The document then discusses various elements that can aid learning when incorporated into instruction gamification, such as story, challenge, feedback, and rewards. It provides examples of how these elements can be implemented and cites related research. Throughout, it offers suggestions for how readers can apply gamification concepts to their own instructional design.
This document provides a three-step guide for companies to get more value from their data and analytics. The three steps are:
1. Map your needs - Create a customized map of your business goals, assets, data, and skills gaps. This helps identify what is most important.
2. Assess your team - Measure your teams' analytics knowledge and skills to benchmark against your needs. Relevant testing is critical for planning training.
3. Train your people - Train your teams based on the gaps identified between your needs and their current skills. Off-the-shelf solutions often don't address specific company needs.
The document recommends using Corsair's Analytic Survey tool to map needs in
The document discusses the process of data science. It begins by defining the typical steps in a data science project as identifying a problem/business question, collecting and cleaning data, performing exploratory data analysis, using algorithms and machine learning, reporting answers/minimum viable products, and getting feedback to review results. It then lists "inconvenient truths" about data science, such as data never being clean and most time being spent on preparation. Finally, it provides an example of using import.io and MonkeyLearn tools for text analysis.
Top 10 Data Science Interview Questions in 2022.pptxinfosec train
Data science is rapidly dominating the world with its diverse usage in various industries. It currently plays a critical role in profit generation.
https://www.infosectrain.com/courses/data-science-with-python-and-r-certification-training/
Cool vs Creepy - Ethics and Data Science - Cooper 2FebCathy Cooper
The document discusses ethics in data science and provides examples of potential issues around biased data, anonymized data, lack of context, and data influencing behavior. It argues that data science tools and algorithms must be designed with fairness, legality, and understandability in mind. Human intervention is still needed to check that models are performing as intended and are not introducing unintended bias. Transparency into how data is collected and models are developed is important.
The document is a resume for Dillipan M. It summarizes his work experience of 14 months in data mining and web research. It also lists his educational qualifications including a B.E in Computer Science, technical skills like Oracle and Photoshop, an academic project on automatic database performance tuning, and internship experience in Linux training. The resume is submitted for seeking a challenging and responsible opportunity to further his career.
Data Science-Why?What?How? By Hari PrasadHari Prasad
This document provides an overview of data science from several perspectives:
- It introduces the presenter and their background/experience in fields related to data science such as social network analysis, big data analytics, and machine learning applications.
- The agenda outlines exploring why data science is important, what it involves technically, and how the data science process works using a standardized approach.
- Key aspects of what data science involves are discussed like machine learning algorithms, the data science skillset, and how machine learning techniques can be demystified and applied to problems.
- The process of data science is reviewed using a popular CRISP-DM framework and an IBM methodology, with examples of how questions can initiate a
Is Data Scientist the Sexiest Job of the 21st century?Edureka!
This document discusses why data science is the most sought after job in 2015. It outlines the career opportunities and advantages of data science jobs. Major companies are increasingly using data science to analyze large amounts of data and make predictions. Data science jobs have high salaries and growth potential, which is why they are considered very attractive careers. The document concludes that data science is indeed the sexiest job of the 21st century due to the high pay, difficultly of hiring qualified professionals, and importance of the field.
P 01 ins_analytics_ai_in_life_case_studies_2017_10_16_v12Vishwa Kolla
1. AI is helping life insurance companies improve processes like underwriting and claims handling by making them more efficient.
2. One case study showed how using consented health data and predictive models allowed underwriting decisions to be made in hours instead of weeks.
3. Another case study demonstrated how combining human and machine pattern detection helped identify suspicious claims patterns that could indicate fraud.
Similar to All Models Are Wrong, But Some Are Useful: 6 Lessons for Making Predictive Analytics Work (20)
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
Generative Classifiers: Classifying with Bayesian decision theory, Bayes’ rule, Naïve Bayes classifier.
Discriminative Classifiers: Logistic Regression, Decision Trees: Training and Visualizing a Decision Tree, Making Predictions, Estimating Class Probabilities, The CART Training Algorithm, Attribute selection measures- Gini impurity; Entropy, Regularization Hyperparameters, Regression Trees, Linear Support vector machines.
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of May 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
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
6. if LOAN-SALARY RATIO < 1.5 then
OUTCOME=’repay’
else if LOAN-SALARY RATIO > 4 then
OUTCOME=’default’
else if AGE < 40 and OCCUPATION =’industrial’then
OUTCOME=’default’
else
OUTCOME=’repay’
end if
Fundamentals of Machine Learning for Predictive Data Analytics
John Kelleher, Brian Mac Namee, and Aoife D'Arcy
www.machinelearningbook.com
41. ●
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Fundamentals of Machine Learning for Predictive Data Analytics
John Kelleher, Brian Mac Namee, and Aoife D'Arcy
www.machinelearningbook.com
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Fundamentals of Machine Learning for Predictive Data Analytics
John Kelleher, Brian Mac Namee, and Aoife D'Arcy
www.machinelearningbook.com
43. ●
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Fundamentals of Machine Learning for Predictive Data Analytics
John Kelleher, Brian Mac Namee, and Aoife D'Arcy
www.machinelearningbook.com
44. ●
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20000400006000080000
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Fundamentals of Machine Learning for Predictive Data Analytics
John Kelleher, Brian Mac Namee, and Aoife D'Arcy
www.machinelearningbook.com
45. 0 50 100 150 200
0.10.20.30.40.5
Training Iteration
MisclassificationRate
Performance on Training Set
Performance on Validation Set
46. 0 50 100 150 200
0.10.20.30.40.5
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MisclassificationRate
Performance on Training Set
Performance on Validation Set
Fundamentals of Machine Learning for Predictive Data Analytics
John Kelleher, Brian Mac Namee, and Aoife D'Arcy
www.machinelearningbook.com
47. 0 50 100 150 200
0.10.20.30.40.5
Training Iteration
MisclassificationRate
Performance on Training Set
Performance on Validation Set
Fundamentals of Machine Learning for Predictive Data Analytics
John Kelleher, Brian Mac Namee, and Aoife D'Arcy
www.machinelearningbook.com
48. 0 50 100 150 200
0.10.20.30.40.5
Training Iteration
MisclassificationRate
Performance on Training Set
Performance on Validation Set
Fundamentals of Machine Learning for Predictive Data Analytics
John Kelleher, Brian Mac Namee, and Aoife D'Arcy
www.machinelearningbook.com
49. 0 50 100 150 200
0.10.20.30.40.5
Training Iteration
MisclassificationRate
Performance on Training Set
Performance on Validation Set
Fundamentals of Machine Learning for Predictive Data Analytics
John Kelleher, Brian Mac Namee, and Aoife D'Arcy
www.machinelearningbook.com
50. 0 50 100 150 200
0.10.20.30.40.5
Training Iteration
MisclassificationRate
Performance on Training Set
Performance on Validation Set
Fundamentals of Machine Learning for Predictive Data Analytics
John Kelleher, Brian Mac Namee, and Aoife D'Arcy
www.machinelearningbook.com
51. 0 50 100 150 200
0.10.20.30.40.5
Training Iteration
MisclassificationRate
Performance on Training Set
Performance on Validation Set
Fundamentals of Machine Learning for Predictive Data Analytics
John Kelleher, Brian Mac Namee, and Aoife D'Arcy
www.machinelearningbook.com
52. 0 50 100 150 200
0.10.20.30.40.5
Training Iteration
MisclassificationRate
Performance on Training Set
Performance on Validation Set
Fundamentals of Machine Learning for Predictive Data Analytics
John Kelleher, Brian Mac Namee, and Aoife D'Arcy
www.machinelearningbook.com
53. 0 50 100 150 200
0.10.20.30.40.5
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MisclassificationRate
Performance on Training Set
Performance on Validation Set
54. 0 50 100 150 200
0.10.20.30.40.5
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Performance on Validation Set
77. On Welsh Corgis, Computer Vision, and the Power of Deep Learning, Microsoft Research, 2014
http://research.microsoft.com/en-us/news/features/dnnvision-071414.aspx
Rise of the machines, The Economist, 2015
http://www.economist.com/news/briefing/21650526-artificial-intelligence-scares-peopleexcessively-so-rise-machines
81. A marketing company working for a charity has
developed two different models that predict the
likelihood that donors will respond to a mail-
shot asking them to make a special extra
donation.
Two models have been built and an evaluation
experiment had been performed. Now we must
decide which model to use.