Logistic Regression in Python | Logistic Regression Example | Machine Learnin...Edureka!
** Python Data Science Training : https://www.edureka.co/python **
This Edureka Video on Logistic Regression in Python will give you basic understanding of Logistic Regression Machine Learning Algorithm with examples. In this video, you will also get to see demo on Logistic Regression using Python. Below are the topics covered in this tutorial:
1. What is Regression?
2. What is Logistic Regression?
3. Why use Logistic Regression?
4. Linear vs Logistic Regression
5. Logistic Regression Use Cases
6. Logistic Regression Example Demo in Python
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Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
Material for Azure Machine Learning tutorial lecture, held within Data Mining course of MoS in Engineering in Computer Science at Università degli Studi di Roma "La Sapienza" (A.Y. 2016/2017).
Lecturers:
Fabio Rosato - rosato.1565173@studenti.uniroma1.it
Giacomo Lanciano - lanciano.1487019@studenti.uniroma1.it
Francisco Ferreres Garcia - matakukos@gmail.com
Leonardo Martini - martini.1722989@studenti.uniroma1.it
Simone Caldaro - caldaro.1324152@studenti.uniroma1.it
Na Zhu - nana.zhu@hotmail.com
Github repo: https://github.com/giacomolanciano/Azure-Machine-Learning-tutorial
Video tutorial: https://youtu.be/_zvPX6Kk7z8
House Price Estimates Based on Machine Learning Algorithmijtsrd
Housing prices are increasing every year, necessitating the creation of a long term housing price strategy. Predicting a homes price will assist a developer in determining a homes purchase price, as well as a consumer in determining the best time to buy a home. The sale price of real estate in major cities depends on the specific circumstances. Housing prices are constantly changing from day to day and are sometimes fired rather than based on estimates. Predicting real estate prices by real factors is a key element as part of our analysis. We want to make our test dependent on all of the simple metrics that are taken into account when deciding the significance. In this research we use linear regression techniques pathway and our results are not self inflicted process rather is a weighted method of various techniques to give the most accurate results. There are fifteen features in the data collection. In this research. There has been an effort to build a forecasting model for determining the price based on the variables that influence the price.The results have proven to be effective lower error and higher accuracy than individual algorithms are used. Jakir Khan | Dr. Ganesh D "House Price Estimates Based on Machine Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42367.pdf Paper URL: https://www.ijtsrd.comcomputer-science/other/42367/house-price-estimates-based-on-machine-learning-algorithm/jakir-khan
Data visualization in data science: exploratory EDA, explanatory. Anscobe's quartet, design principles, visual encoding, design engineering and journalism, choosing the right graph, narrative structures, technology and tools.
Logistic Regression in Python | Logistic Regression Example | Machine Learnin...Edureka!
** Python Data Science Training : https://www.edureka.co/python **
This Edureka Video on Logistic Regression in Python will give you basic understanding of Logistic Regression Machine Learning Algorithm with examples. In this video, you will also get to see demo on Logistic Regression using Python. Below are the topics covered in this tutorial:
1. What is Regression?
2. What is Logistic Regression?
3. Why use Logistic Regression?
4. Linear vs Logistic Regression
5. Logistic Regression Use Cases
6. Logistic Regression Example Demo in Python
Subscribe to our channel to get video updates. Hit the subscribe button above.
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
Material for Azure Machine Learning tutorial lecture, held within Data Mining course of MoS in Engineering in Computer Science at Università degli Studi di Roma "La Sapienza" (A.Y. 2016/2017).
Lecturers:
Fabio Rosato - rosato.1565173@studenti.uniroma1.it
Giacomo Lanciano - lanciano.1487019@studenti.uniroma1.it
Francisco Ferreres Garcia - matakukos@gmail.com
Leonardo Martini - martini.1722989@studenti.uniroma1.it
Simone Caldaro - caldaro.1324152@studenti.uniroma1.it
Na Zhu - nana.zhu@hotmail.com
Github repo: https://github.com/giacomolanciano/Azure-Machine-Learning-tutorial
Video tutorial: https://youtu.be/_zvPX6Kk7z8
House Price Estimates Based on Machine Learning Algorithmijtsrd
Housing prices are increasing every year, necessitating the creation of a long term housing price strategy. Predicting a homes price will assist a developer in determining a homes purchase price, as well as a consumer in determining the best time to buy a home. The sale price of real estate in major cities depends on the specific circumstances. Housing prices are constantly changing from day to day and are sometimes fired rather than based on estimates. Predicting real estate prices by real factors is a key element as part of our analysis. We want to make our test dependent on all of the simple metrics that are taken into account when deciding the significance. In this research we use linear regression techniques pathway and our results are not self inflicted process rather is a weighted method of various techniques to give the most accurate results. There are fifteen features in the data collection. In this research. There has been an effort to build a forecasting model for determining the price based on the variables that influence the price.The results have proven to be effective lower error and higher accuracy than individual algorithms are used. Jakir Khan | Dr. Ganesh D "House Price Estimates Based on Machine Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42367.pdf Paper URL: https://www.ijtsrd.comcomputer-science/other/42367/house-price-estimates-based-on-machine-learning-algorithm/jakir-khan
Data visualization in data science: exploratory EDA, explanatory. Anscobe's quartet, design principles, visual encoding, design engineering and journalism, choosing the right graph, narrative structures, technology and tools.
This session is continuation of “Automated Production Ready ML at Scale” in last Spark AI Summit at Europe. In this session you will learn about how H&M evolves reference architecture covering entire MLOps stack addressing a few common challenges in AI and Machine learning product, like development efficiency, end to end traceability, speed to production, etc.
모두를 위한 기계번역 (박찬준)
○ 개요
2014년 본격적으로 NMT에 대한 연구가 진행되었으며 현재는 Transformer 기반의 다양한 NMT 시스템들이 연구되고 있습니다.
더 나아가 최근 NLP에서 가장 뜨거운 연구분야인 Language Representation 분야에서도 Transformer를 기반으로 한 BERT, GPT-2, XLNET 등의 모델이 개발되고 있습니다.
본 테크톡에서는 먼저 RBMT와 SMT에 대해서 간략하게 살펴보고 RNN기반 NMT 부터 Transformer를 기반으로 하는 NMT까지 자세히 살펴볼 예정입니다.
더 나아가 최근 WMT에서 매년 Shared Task로 열리고 있는 Automatic Post Editing System과 Parallel Corpus Filtering, Quality Estimation 분야에 대해서 설명하며 NMT를 이용한 다양한 응용 연구분야를 소개해드리겠습니다. (ex. 실시간 강연통역 시스템, 문법교정 시스템) , 기계번역에 대해서 아무것도 모르시는 분, 궁금하시분들도 이해할 수 있는 수준으로 쉽게 설명을 진행할 예정입니다.
○ 목차
1)기계번역이란
2)RBMT에 대한 간략한 소개
3)SMT에 대한 간략한 소개
4)RNN기반 딥러닝부터 Transformer까지
5)NMT를 이용한 다양한 응용 연구 소개
a. Automatic Post Editing
b. Quality Estimation
c. Parallel Corpus Filtering
d. Grammar Error Correction
e. 실시간 강연통역 시스템
6)OpenNMT 소개
Predictive Analytics enables organisations to forecast future events, analyse risks and opportunities, and automate decision making processes by analysing historic data.
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Simplilearn
This presentation on "Supervised and Unsupervised Learning" will help you understand what is machine learning, what are the types of Machine learning, what is supervised machine learning, types of supervised machine learning, what is unsupervised learning, types of unsupervised learning and what are the differences between supervised and unsupervised machine learning. In supervised learning, the model learns from a labeled data whereas in unsupervised learning, model trains itself on unlabeled data. Now, let us get started and understand supervised and unsupervised learning and how they are different from each other.
Below are the topics explained in this supervised and unsupervised learning in Machine Learning presentation-
1. What is Machine Learning
- Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
2. Supervised Learning
- Types of Supervised Learning
3. Unsupervised Learning
- Types of Unsupervised Learning
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with the knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire a thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
Learn more at: https://www.simplilearn.com/
The Power of Auto ML and How Does it WorkIvo Andreev
Automated ML is an approach to minimize the need of data science effort by enabling domain experts to build ML models without having deep knowledge of algorithms, mathematics or programming skills. The mechanism works by allowing end-users to simply provide data and the system automatically does the rest by determining approach to perform particular ML task. At first this may sound discouraging to those aiming to the “sexiest job of the 21st century” - the data scientists. However, Auto ML should be considered as democratization of ML, rather that automatic data science.
In this session we will talk about how Auto ML works, how is it implemented by Microsoft and how it could improve the productivity of even professional data scientists.
This presentation briefly discusses about the following topics:
Data Analytics Lifecycle
Importance of Data Analytics Lifecycle
Phase 1: Discovery
Phase 2: Data Preparation
Phase 3: Model Planning
Phase 4: Model Building
Phase 5: Communication Results
Phase 6: Operationalize
Data Analytics Lifecycle Example
Machine Learning in 10 Minutes | What is Machine Learning? | EdurekaEdureka!
YouTube Link: https://youtu.be/qWHi09C3Dq0
** Machine Learning Training with Python: https://www.edureka.co/machine-learning-certification-training**
This Edureka video on 'Machine Learning in 10 Minutes' will help you understand what exactly is Machine Learning and what are the different types of Machine Learning along with some career opportunities that you can achieve through Machine Learning.
Example
What is AI?
What is Machine Learning
Steps for Machine Learning
Types of Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Applications of Machine Learning
What can you be with Machine Learning?
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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
Castbox: https://castbox.fm/networks/505?country=in
Machine Learning Model Deployment: Strategy to ImplementationDataWorks Summit
This talk will introduce participants to the theory and practice of machine learning in production. The talk will begin with an intro on machine learning models and data science systems and then discuss data pipelines, containerization, real-time vs. batch processing, change management and versioning.
As part of this talk, an audience will learn more about:
• How data scientists can have the complete self-service capability to rapidly build, train, and deploy machine learning models.
• How organizations can accelerate machine learning from research to production while preserving the flexibility and agility of data scientists and modern business use cases demand.
A small demo will showcase how to rapidly build, train, and deploy machine learning models in R, python, and Spark, and continue with a discussion of API services, RESTful wrappers/Docker, PMML/PFA, Onyx, SQLServer embedded models, and
lambda functions.
Speakers
Sagar Kewalramani, Solutions Architect
Cloudera
Justin Norman, Director, Research and Data Science Services
Cloudera Fast Forward Labs
Slide for Arithmer Seminar given by Dr. Daisuke Sato (Arithmer) at Arithmer inc.
The topic is on "explainable AI".
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Great Learning is an online and blended learning platform designed to empower working professionals to develop relevant competencies and accelerate their career progression. Great Learning programs are offered in partnership with Great Lakes Institute of Management, one of India’s leading business schools. Great Learning offers a wide variety of programs designed to help professionals develop the competencies they need.
https://www.greatlearning.in/
Multiclass classification of imbalanced dataSaurabhWani6
Pydata Talk on Classification of imbalanced data.
It is an overview of concepts for better classification in imbalanced datasets.
Resampling techniques are introduced along with bagging and boosting methods.
SVM Algorithm Explained | Support Vector Machine Tutorial Using R | EdurekaEdureka!
YouTube: https://youtu.be/RKZoJVMr6CU
** Data Science Certification using R: https://www.edureka.co/data-science **
This session is dedicated to how SVM works, the various features of SVM and how it used in the real world. The following topics will be covered today:
Introduction to machine learning
What is Support Vector Machine (SVM)?
How does SVM work?
Non-linear SVM
SVM Use case
Hands-On
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
Top contenders in the 2015 KDD cup include the team from DataRobot comprising Owen Zhang, #1 Ranked Kaggler and top Kagglers Xavier Contort and Sergey Yurgenson. Get an in-depth look as Xavier describes their approach. DataRobot allowed the team to focus on feature engineering by automating model training, hyperparameter tuning, and model blending - thus giving the team a firm advantage.
Discover BigQuery ML, build your own CREATE MODEL statementMárton Kodok
With BigQuery ML, you can build machine learning models without leaving the database environment and training it on massive datasets. In this demo session we are going to demonstrate common marketing Machine Learning use cases of how to build, train, eval, and predict, your own scalable machine learning models using SQL language in Google BigQuery and to address the following use cases: - Customer Segmentation + Product cross sale recommendation - Conversion/Purchase prediction - Inference with other in-built >20 models The audience will get first-hand experience with how to write CREATE MODEL sql syntax to build machine learning models such as: - Multiclass logistic regression for classification - K-means clustering - Matrix factorization - ARIMA time series predictions ... and more Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision-making through predictive analytics across the organization without leaving the query editor. In the end, the audience will learn how everyday developers can build/train/run their own machine-learning models straight from the database query editor, by issuing CREATE MODEL statements
Amazon SageMaker is a fully-managed platform that lets developers and data scientists build and scale machine learning solutions. First, we'll show you how SageMaker Ground Truth helps you label large training datasets. Then, using Jupyter notebooks, we'll show you how to build, train and deploy models using built-in algorithms and frameworks (TensorFlow, Apache MXNet, etc). Finally, we'll show you how to use 3rd-party models from the AWS marketplace.
Fraud Detection with Graphs at the Danish Business AuthorityNeo4j
Traditional fraud prevention measures focus on discrete data points such as specific accounts, individuals, devices or IP addresses. However, today’s sophisticated fraudsters escape detection by forming fraud rings with individuals paid, lured into or unknowingly fronting these activities. To uncover such fraud rings and the people behind them, it is essential to look beyond individual data points to the connections that link them.
Neo4j uncovers difficult-to-detect patterns that far outstrip the power of a relational database. Enterprise organisations use Neo4j to augment their existing fraud detection capabilities to combat a variety of financial crimes including first-party bank fraud, credit card fraud, ecommerce fraud, insurance fraud and money laundering – and all in real time.
Learn more how to battle fraud with the power of graph databases during this webinar. We are pleased to invite you to hear Marius Hartmann from Danish Business Authority talking about how they are combining graph analysis with machine learning to prevent fraud. In context of the COVID-19 compensation scheme controls, he will present use cases currently in production and explain why graph is a good fit for government authorities.
Elevate the Sales Process: B2B sales intelligence with LinkedIn Social Sellin...John Chao
The use of big data intelligence for sales development is still in its infancy stages. At LinkedIn, we use big data to push the envelope in the sales development process. Traditional methods of sales development are less data driven and little has been done to quantitatively understand an accounts propensity to purchase. We develop a set of full funnel b2b sales models that incorporates both company and individuals information and synthesize such information to dynamically inform our sales team on how to best manage their sales development. We are able to produce an information machine that decides which firms to target, who to target within each firm, understand each individual's propensity change in real time during the sales process so that the sales team is better equipped to win business deals. - See more at: https://ieondemand.com/divisions/analytics/presentations/elevate-the-sales-process-b2b-sales-intelligence-with-linkedin-social-selling-data#sthash.yILVhWId.dpuf
Course5 & Lenovo: Analytics driven Digital Trading DeskCourse5i
We build for organizations the capabilities and intelligence to make the most effective strategic and tactical moves related to customers, markets, and competition
This session is continuation of “Automated Production Ready ML at Scale” in last Spark AI Summit at Europe. In this session you will learn about how H&M evolves reference architecture covering entire MLOps stack addressing a few common challenges in AI and Machine learning product, like development efficiency, end to end traceability, speed to production, etc.
모두를 위한 기계번역 (박찬준)
○ 개요
2014년 본격적으로 NMT에 대한 연구가 진행되었으며 현재는 Transformer 기반의 다양한 NMT 시스템들이 연구되고 있습니다.
더 나아가 최근 NLP에서 가장 뜨거운 연구분야인 Language Representation 분야에서도 Transformer를 기반으로 한 BERT, GPT-2, XLNET 등의 모델이 개발되고 있습니다.
본 테크톡에서는 먼저 RBMT와 SMT에 대해서 간략하게 살펴보고 RNN기반 NMT 부터 Transformer를 기반으로 하는 NMT까지 자세히 살펴볼 예정입니다.
더 나아가 최근 WMT에서 매년 Shared Task로 열리고 있는 Automatic Post Editing System과 Parallel Corpus Filtering, Quality Estimation 분야에 대해서 설명하며 NMT를 이용한 다양한 응용 연구분야를 소개해드리겠습니다. (ex. 실시간 강연통역 시스템, 문법교정 시스템) , 기계번역에 대해서 아무것도 모르시는 분, 궁금하시분들도 이해할 수 있는 수준으로 쉽게 설명을 진행할 예정입니다.
○ 목차
1)기계번역이란
2)RBMT에 대한 간략한 소개
3)SMT에 대한 간략한 소개
4)RNN기반 딥러닝부터 Transformer까지
5)NMT를 이용한 다양한 응용 연구 소개
a. Automatic Post Editing
b. Quality Estimation
c. Parallel Corpus Filtering
d. Grammar Error Correction
e. 실시간 강연통역 시스템
6)OpenNMT 소개
Predictive Analytics enables organisations to forecast future events, analyse risks and opportunities, and automate decision making processes by analysing historic data.
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Simplilearn
This presentation on "Supervised and Unsupervised Learning" will help you understand what is machine learning, what are the types of Machine learning, what is supervised machine learning, types of supervised machine learning, what is unsupervised learning, types of unsupervised learning and what are the differences between supervised and unsupervised machine learning. In supervised learning, the model learns from a labeled data whereas in unsupervised learning, model trains itself on unlabeled data. Now, let us get started and understand supervised and unsupervised learning and how they are different from each other.
Below are the topics explained in this supervised and unsupervised learning in Machine Learning presentation-
1. What is Machine Learning
- Types of Machine Learning
- Supervised Learning
- Unsupervised Learning
2. Supervised Learning
- Types of Supervised Learning
3. Unsupervised Learning
- Types of Unsupervised Learning
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with the knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire a thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
Learn more at: https://www.simplilearn.com/
The Power of Auto ML and How Does it WorkIvo Andreev
Automated ML is an approach to minimize the need of data science effort by enabling domain experts to build ML models without having deep knowledge of algorithms, mathematics or programming skills. The mechanism works by allowing end-users to simply provide data and the system automatically does the rest by determining approach to perform particular ML task. At first this may sound discouraging to those aiming to the “sexiest job of the 21st century” - the data scientists. However, Auto ML should be considered as democratization of ML, rather that automatic data science.
In this session we will talk about how Auto ML works, how is it implemented by Microsoft and how it could improve the productivity of even professional data scientists.
This presentation briefly discusses about the following topics:
Data Analytics Lifecycle
Importance of Data Analytics Lifecycle
Phase 1: Discovery
Phase 2: Data Preparation
Phase 3: Model Planning
Phase 4: Model Building
Phase 5: Communication Results
Phase 6: Operationalize
Data Analytics Lifecycle Example
Machine Learning in 10 Minutes | What is Machine Learning? | EdurekaEdureka!
YouTube Link: https://youtu.be/qWHi09C3Dq0
** Machine Learning Training with Python: https://www.edureka.co/machine-learning-certification-training**
This Edureka video on 'Machine Learning in 10 Minutes' will help you understand what exactly is Machine Learning and what are the different types of Machine Learning along with some career opportunities that you can achieve through Machine Learning.
Example
What is AI?
What is Machine Learning
Steps for Machine Learning
Types of Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Applications of Machine Learning
What can you be with Machine Learning?
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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
Castbox: https://castbox.fm/networks/505?country=in
Machine Learning Model Deployment: Strategy to ImplementationDataWorks Summit
This talk will introduce participants to the theory and practice of machine learning in production. The talk will begin with an intro on machine learning models and data science systems and then discuss data pipelines, containerization, real-time vs. batch processing, change management and versioning.
As part of this talk, an audience will learn more about:
• How data scientists can have the complete self-service capability to rapidly build, train, and deploy machine learning models.
• How organizations can accelerate machine learning from research to production while preserving the flexibility and agility of data scientists and modern business use cases demand.
A small demo will showcase how to rapidly build, train, and deploy machine learning models in R, python, and Spark, and continue with a discussion of API services, RESTful wrappers/Docker, PMML/PFA, Onyx, SQLServer embedded models, and
lambda functions.
Speakers
Sagar Kewalramani, Solutions Architect
Cloudera
Justin Norman, Director, Research and Data Science Services
Cloudera Fast Forward Labs
Slide for Arithmer Seminar given by Dr. Daisuke Sato (Arithmer) at Arithmer inc.
The topic is on "explainable AI".
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Great Learning is an online and blended learning platform designed to empower working professionals to develop relevant competencies and accelerate their career progression. Great Learning programs are offered in partnership with Great Lakes Institute of Management, one of India’s leading business schools. Great Learning offers a wide variety of programs designed to help professionals develop the competencies they need.
https://www.greatlearning.in/
Multiclass classification of imbalanced dataSaurabhWani6
Pydata Talk on Classification of imbalanced data.
It is an overview of concepts for better classification in imbalanced datasets.
Resampling techniques are introduced along with bagging and boosting methods.
SVM Algorithm Explained | Support Vector Machine Tutorial Using R | EdurekaEdureka!
YouTube: https://youtu.be/RKZoJVMr6CU
** Data Science Certification using R: https://www.edureka.co/data-science **
This session is dedicated to how SVM works, the various features of SVM and how it used in the real world. The following topics will be covered today:
Introduction to machine learning
What is Support Vector Machine (SVM)?
How does SVM work?
Non-linear SVM
SVM Use case
Hands-On
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
Top contenders in the 2015 KDD cup include the team from DataRobot comprising Owen Zhang, #1 Ranked Kaggler and top Kagglers Xavier Contort and Sergey Yurgenson. Get an in-depth look as Xavier describes their approach. DataRobot allowed the team to focus on feature engineering by automating model training, hyperparameter tuning, and model blending - thus giving the team a firm advantage.
Discover BigQuery ML, build your own CREATE MODEL statementMárton Kodok
With BigQuery ML, you can build machine learning models without leaving the database environment and training it on massive datasets. In this demo session we are going to demonstrate common marketing Machine Learning use cases of how to build, train, eval, and predict, your own scalable machine learning models using SQL language in Google BigQuery and to address the following use cases: - Customer Segmentation + Product cross sale recommendation - Conversion/Purchase prediction - Inference with other in-built >20 models The audience will get first-hand experience with how to write CREATE MODEL sql syntax to build machine learning models such as: - Multiclass logistic regression for classification - K-means clustering - Matrix factorization - ARIMA time series predictions ... and more Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision-making through predictive analytics across the organization without leaving the query editor. In the end, the audience will learn how everyday developers can build/train/run their own machine-learning models straight from the database query editor, by issuing CREATE MODEL statements
Amazon SageMaker is a fully-managed platform that lets developers and data scientists build and scale machine learning solutions. First, we'll show you how SageMaker Ground Truth helps you label large training datasets. Then, using Jupyter notebooks, we'll show you how to build, train and deploy models using built-in algorithms and frameworks (TensorFlow, Apache MXNet, etc). Finally, we'll show you how to use 3rd-party models from the AWS marketplace.
Fraud Detection with Graphs at the Danish Business AuthorityNeo4j
Traditional fraud prevention measures focus on discrete data points such as specific accounts, individuals, devices or IP addresses. However, today’s sophisticated fraudsters escape detection by forming fraud rings with individuals paid, lured into or unknowingly fronting these activities. To uncover such fraud rings and the people behind them, it is essential to look beyond individual data points to the connections that link them.
Neo4j uncovers difficult-to-detect patterns that far outstrip the power of a relational database. Enterprise organisations use Neo4j to augment their existing fraud detection capabilities to combat a variety of financial crimes including first-party bank fraud, credit card fraud, ecommerce fraud, insurance fraud and money laundering – and all in real time.
Learn more how to battle fraud with the power of graph databases during this webinar. We are pleased to invite you to hear Marius Hartmann from Danish Business Authority talking about how they are combining graph analysis with machine learning to prevent fraud. In context of the COVID-19 compensation scheme controls, he will present use cases currently in production and explain why graph is a good fit for government authorities.
Elevate the Sales Process: B2B sales intelligence with LinkedIn Social Sellin...John Chao
The use of big data intelligence for sales development is still in its infancy stages. At LinkedIn, we use big data to push the envelope in the sales development process. Traditional methods of sales development are less data driven and little has been done to quantitatively understand an accounts propensity to purchase. We develop a set of full funnel b2b sales models that incorporates both company and individuals information and synthesize such information to dynamically inform our sales team on how to best manage their sales development. We are able to produce an information machine that decides which firms to target, who to target within each firm, understand each individual's propensity change in real time during the sales process so that the sales team is better equipped to win business deals. - See more at: https://ieondemand.com/divisions/analytics/presentations/elevate-the-sales-process-b2b-sales-intelligence-with-linkedin-social-selling-data#sthash.yILVhWId.dpuf
Course5 & Lenovo: Analytics driven Digital Trading DeskCourse5i
We build for organizations the capabilities and intelligence to make the most effective strategic and tactical moves related to customers, markets, and competition
How To Unify Data with Bespoke Dashboards for True InsightsTinuiti
Today’s landscape requires Marketing leaders to stay informed on a daily basis and act with purposeful agility. With an adaptive, frequently updated look across all channels and budget performance, marketers can expose inefficiencies and seize opportunities faster than their competitors to drive real business results. Join our insights experts to see how they empower clients with a fully customized and automated dashboard so they can spend more time acting on data opposed to pulling it.
B2BMF2019 - How Data Driven is Your Marketing Organization? - TableauB2B Marketing Forum
Of je marketingteam nu aan het begin van de data-reis is of verder, tijdens de presentatie van Christy en Eulalie ontdek jij hoe marketeers betere beslissingen kunnen nemen én hun impact kunnen aantonen. Gebruikmakend van marketingkanalen vol data én continu ontwikkelende technologie kun jij een analysestrategie ontwikkelen die nu én op de lange termijn succesvol is.
Around 75,000 million machines will be integrated into the Internet of Things (IoT) and the Industrial Internet of Things by 2025, according to New Electronics (IIoT). Leading market research organizations predict 31% annual growth. Huge volumes of data are being generated globally as a result of this evolution, and this data needs to be assessed and delivered promptly, securely, and reliably.
In the recent past, we have learnt that data is the lifeline of any business and it is really important to collect data, more and more of it. But no one is telling us what to do with large volumes of data.
Shailendra has successfully delivered over One Billion Dollars in incremental value and will spend 30 minutes in showcasing how many large organisations are using data to their advantage by creating value through generating incremental revenue and optimising costs using analytics techniques.
Key Takeaways:
(i) Demystify the myths of analytics
(ii) Walkthrough a step-by-step approach to delivering successful projects that created an incremental value of hundreds and millions of dollars.
(iii) Three use cases where large organisations are using analytics to their advantage by creating value by generating incremental revenue and optimising costs.
Accelerating Customer Insights & enhancing Business impactAjay Kelkar
How do you convert data into insight and then use that to drive business impact. Here are some lessons from my experience in HDFC bank and also as an entrepreneur at Cequity
Satish Kumar is an Enterprise Digital Marketing Consultant with over 10 years of experience in SEO, Content Marketing and Social Media. He is currently working with many Indian corporates by providing personalized Digital Strategy through his company Pyrite Technologies.
Operationalizing Customer Analytics with Azure and Power BICCG
Many organizations fail to realize the value of data science teams because they are not effectively translating the analytic findings produced by these teams into quantifiable business results. This webinar demonstrates how to visualize analytic models like churn and turn their output into action. Senior Business Solution Architect, Mike Druta, presents methods for operationalizing analytic models produced by data science teams into a repeatable process that can be automated and applied continuously using Azure.
Do you know the real story your data is telling you?4Ps Marketing
Do you know the real story your data is telling you, or are you still stuck reading a fairytale? Insights Consultant Emma Haslam spoke about the subject at Brighton SEO 2014.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
How Business Analytics drives business value - Teradata partners conference Nashvile 2014
1. How Business Analytics Drives
Insights and Makes Business
Impact Leveraging Big Data
Michael Li, Jonathan Wu
Business Analytics, LinkedIn
2. Business Analytics @ Linkedin
Jonathan Wu
Michael Li
www.linkedin.com/in/limichael www.linkedin.com/in/jiyewu
• Director, Business Analytics Data Solutions
• Builder of massive scalable analytic platform
• Director, Business Analytics
• Big data evangelist and practitioner
7. Linkedin for members
Identity Network Knowledge
The professional profile
of record
Connect all of the
world’s professionals
The definitive
professional
publishing platform
8. Hire Market Sell
Enable passive
recruiting at
massive scale
Identify and engage
professionals with
relevant content
Transform cold
calls into warm
prospects
Linkedin for customers
9. LinkedIn’s business model & why analytics is important
Member growth and engagement
Critical mass of data
Relevant and valuable
products and services
Technology
platform
10. Business Analytics Team Mission
“Provide the best-in-class end-to-end analytic
solutions to power internal LinkedIn teams to
be more productive and successful!”
11. 60+ team members support 4000+ internal employees
Global
Sales
Organization
Biz Analytics
Team $
Engineering
Operations
Sales Ops,
Ad Ops,
Biz Ops
Team $
Product
Global
Customer
Organization
Marketing
Talent
Solutions
Marketing
Solutions
Sales
Solutions
Premium
Subscriptions
Consumer
Marketing
12. Business analytics evolution: from data to insights
Insights
What is the best
that could happen?
Intelligence
What will happen?
Information/Knowledge
Why did it happen?
Data
Business ROI
What happened?
16. Business analytics drives business value thru the EOI Framework leveraging big data
Core
Strategic
Venture
Empower
Empower business partners to have
access to the data and insights they
need when they need them
Optimize
Optimize business performance
leveraging the powerful & unique
LinkedIn data we have
Innovate
Innovate the way on how analytics can
help our business grow leveraging both
internal & external data
Details: https://www.linkedin.com/pulse/article/20140930145256-16223736-beyond-big-data-introducing-the-eoi-framework-for-analytics-teams-to-drive-business-impact
17. Business analytics deliver results to business in three progressive ways
1. Empower
Empower people to have accesss to
data thru interactive tools
Talent Flows
Dynamic tools to enable discovery
on business insights
3. Innovate
Level up marketing strategies by
innovation on analytics
Account Interest Score (B2B)
Account-based targeting instead of
member-based,
capturing/summarizing signals
intelligently
2. Optimize
Optimize marketing campaigns thru
analysis and propensity models
Sub Propensity model (B2C)
Identify the right segment and offer
the right product at right timing
18. Talent Flows Help Discover Dynamic Trends on Where
Companies are Winning Talents from and Losing Talents to
19. Business analytics deliver results to business in three progressive ways
2. Optimize
Optimize marketing campaigns thru
analysis and propensity models
Sub Propensity model (B2C)
Identify the right segment and offer
the right product at right timing
1. Empower
Empower people to have accesss to
data thru interactive tools
Talent Flows
Dynamic tools to enable discovery
on business insights
3. Innovate
Level up marketing strategies by
innovation on analytics
Account Interest Score (B2B)
Account-based targeting instead of
member-based,
capturing/summarizing signals
intelligently
20. Subscription: one of the most important B2C business LinkedIn offers to consumers
B2C
Business to Consumer
22. How to leverage analytics to identify future subscribers?
Social Data
Identity Data
Behavioral Data
Overall Audience Target Audience
23. Propensity Now Accounts for 56% of Total Marketing Acquisitions
Monthly subscription marketing acquisition
Online sourcing business
Total Prediction
Power
56%
Propensity
Model
34%
Signal Based
Marketing
1100%%
Profile Based
Marketing
34%
56%
100%
24. Business analytics deliver results to business in three progressive ways
3. Innovate
Level up marketing strategies by
innovation on analytics
Account Interest Score (B2B)
Account-based targeting instead of
member-based,
capturing/summarizing signals
intelligently
2. Optimize
Optimize marketing campaigns thru
analysis and propensity models
Sub Propensity model (B2C)
Identify the right segment and offer
the right product at right timing
1. Empower
Empower people to have accesss to
data thru interactive tools
Talent Flows
Dynamic tools to enable discovery
on business insights
25. LinkedIn has a Unique Mix of B2C and B2B Business
B2C
Business to Consumer
Analytics is the key to bridge the gap
B2B
Business to Business
27. We built the Contact Interest Score Based on Decision Maker Scoring and Engagement
Decision Maker
Score
Engagement
Score
Contact Interest Score
Model Input
Marketing
activities
Linkedin
Usage
Profile
LI
Connect-ions
28. Account Score
Then Aggregate Contact Interest Score to Account Interest Score*
29. Higher account interest score leads to higher deal win rate and more revenue
Average +48%
No Account
Interest Score
High Account
Interest Score
Low Account
Interest Score
+35%
21%
31%
42%
Deal Win Rate by Account Interest Score bucket
30. Recap - the EOI framework
Core
Strategic
Venture
Empower
Empower business partners to have
access to the data and insights they
need when they need them
Optimize
Optimize business performance
leveraging the powerful & unique
LinkedIn data we have
Innovate
Innovate the way on how analytics can
help our business grow leveraging both
internal & external data
Details: https://www.linkedin.com/pulse/article/20140930145256-16223736-beyond-big-data-introducing-the-eoi-framework-for-analytics-teams-to-drive-business-impact
32. High-level Linkedin data flow
Voldemort
Nearline data
Oracle
Espresso
Online data
Web Logs
Web logs
33. High-level Linkedin data flow
Hadoop
Teradata
Offline data
Voldemort
Nearline data
Oracle
Espresso
Online data
Web Logs
Web logs
Databus
KAFKA
34. High-level Linkedin data flow
Hadoop
Teradata
Offline data
Voldemort
Nearline data
Oracle
Espresso
Online data
Web Logs
Web logs
Recommendation data
Databus
KAFKA
35. Web API
The technology powers E2E analytics
Petabytes
Gigabytes
Megabytes
Terabytes
1
2
3
4
Data
Visualization
Data ELT and
Aggregation
Analytics Portal
5 Kilobytes
39. Comments from Linkedin internal teams
"Want to shoot you a quick note, w/o our analytics team’s
work, I would not have this vacation with my family now!”
- A sales rep
40. “Now, I believe.”
- A product marketing leader
“We always believe!”
- Our CFO
41. Hire the right people with the right…
5%
15%
100%
80%
?
Skills + IQ & EQ + Passion = Good Analyst Great Analyst?
0%
+X %
42. Want to Know the Answer? Please Join LinkedIn!
We are hiring!
43. Jonathan Wu
www.linkedin.com/in/limichael www.linkedin.com/in/jiyewu
Email: Contact us thru Linkedin
Twitter: @
PARTNERS Mobile App
InfoHub Kiosks
teradata-partners.com
Follow Teradata
Twitter.com/teradatanews
Linkedin.com/company/teradata
Michael Li
jiyewu, @li_mike_yue