Get an overview of the latest Custom Visuals added to the xViz Suite. Understand how xViz Suite is geared towards helping Power BI Users and Enterprises to visualize data in a whole different way and enables effective business insights and decisions.
This document outlines a research project proposal for implementing real-time face recognition on an attendance system. The project aims to use machine learning and computer vision techniques to detect student faces and recognize their names for attendance tracking. The proposal discusses conducting an initial prototype using Python, OpenCV, NumPy and local binary pattern (LBP) classification. It describes collecting a database of facial images, developing the system design using use case, activity and sequence diagrams. The work plan outlines developing the prototype over several months. The goal is to gain experience with computer vision tools and apply face recognition to applications like security, banking and more.
The document discusses the curse of dimensionality, which refers to the problem caused by an exponential increase in volume associated with adding extra dimensions to a mathematical space. This causes several issues, including an increase in running time and overfitting as the number of dimensions increases. It also requires exponentially more samples to maintain the same level of accuracy as more dimensions are added. Several methods are discussed to help address this problem, such as dimensionality reduction techniques like principal component analysis, which projects the data onto a lower dimensional space.
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
How Machine Learning and AI Can Support the Fight Against COVID-19Databricks
In this session, we show how to leverage CORD dataset, containing more than 400000 scientific papers on COVID and related topics, and recent advances in natural language processing and other AI techniques to generate new insights in support of the ongoing fight against this infectious disease.
The idea explored in our talk is to apply modern NLP methods, such and named entity recognition (NER) and relation extraction to article’s abstracts (and, possibly, full text), to extract some meaningful insights from the text, and to enable semantically rich search over the paper corpus. We first investigate how to train NER model using Medical NER dataset from Kaggle, and specialized version of BERT (PubMedBERT) as a feature extractor, to allow automatic extraction of such entities as medical condition names, medicine names and pathogens. Entity extraction alone can provide us with some interesting findings, such as how approaches to COVID treatment evolved with time, in terms of mentioned medicines. We demonstrate how to use Azure Machine Learning for training the model.
To take this investigation one step further, we also investigate the usage of pre-trained medical models, available as Text Analytics for Health service on the Microsoft Azure cloud. In addition to many entity types, it can also extract relations (such as the dosage of medicine provisioned), entity negation, and entity mapping to some well-known medical ontologies. We investigate the best way to use Azure ML at scale to score large paper collection, and to store the results.
Face recognition technology uses unique facial features to identify or verify individuals. It works by measuring distances between nodal points on the face, like the eyes, nose, and chin. The technology has various applications and advantages over other biometrics like fingerprints. It does not require physical contact and can identify people quickly without an expert. While very accurate, face recognition may have issues distinguishing between identical twins. The document discusses the components, implementation, advantages and uses of face recognition systems.
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters.
Below topics are explained in this Machine Learning presentation:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Machine Learning Algorithms
- Linear Regression
- Decision Trees
- Support Vector Machine
5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM
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 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.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
This document outlines a research project proposal for implementing real-time face recognition on an attendance system. The project aims to use machine learning and computer vision techniques to detect student faces and recognize their names for attendance tracking. The proposal discusses conducting an initial prototype using Python, OpenCV, NumPy and local binary pattern (LBP) classification. It describes collecting a database of facial images, developing the system design using use case, activity and sequence diagrams. The work plan outlines developing the prototype over several months. The goal is to gain experience with computer vision tools and apply face recognition to applications like security, banking and more.
The document discusses the curse of dimensionality, which refers to the problem caused by an exponential increase in volume associated with adding extra dimensions to a mathematical space. This causes several issues, including an increase in running time and overfitting as the number of dimensions increases. It also requires exponentially more samples to maintain the same level of accuracy as more dimensions are added. Several methods are discussed to help address this problem, such as dimensionality reduction techniques like principal component analysis, which projects the data onto a lower dimensional space.
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
How Machine Learning and AI Can Support the Fight Against COVID-19Databricks
In this session, we show how to leverage CORD dataset, containing more than 400000 scientific papers on COVID and related topics, and recent advances in natural language processing and other AI techniques to generate new insights in support of the ongoing fight against this infectious disease.
The idea explored in our talk is to apply modern NLP methods, such and named entity recognition (NER) and relation extraction to article’s abstracts (and, possibly, full text), to extract some meaningful insights from the text, and to enable semantically rich search over the paper corpus. We first investigate how to train NER model using Medical NER dataset from Kaggle, and specialized version of BERT (PubMedBERT) as a feature extractor, to allow automatic extraction of such entities as medical condition names, medicine names and pathogens. Entity extraction alone can provide us with some interesting findings, such as how approaches to COVID treatment evolved with time, in terms of mentioned medicines. We demonstrate how to use Azure Machine Learning for training the model.
To take this investigation one step further, we also investigate the usage of pre-trained medical models, available as Text Analytics for Health service on the Microsoft Azure cloud. In addition to many entity types, it can also extract relations (such as the dosage of medicine provisioned), entity negation, and entity mapping to some well-known medical ontologies. We investigate the best way to use Azure ML at scale to score large paper collection, and to store the results.
Face recognition technology uses unique facial features to identify or verify individuals. It works by measuring distances between nodal points on the face, like the eyes, nose, and chin. The technology has various applications and advantages over other biometrics like fingerprints. It does not require physical contact and can identify people quickly without an expert. While very accurate, face recognition may have issues distinguishing between identical twins. The document discusses the components, implementation, advantages and uses of face recognition systems.
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...Simplilearn
This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters.
Below topics are explained in this Machine Learning presentation:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Machine Learning Algorithms
- Linear Regression
- Decision Trees
- Support Vector Machine
5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM
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 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.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
Logistic regression : Use Case | Background | Advantages | DisadvantagesRajat Sharma
This slide will help you to understand the working of logistic regression which is a type of machine learning model along with use cases, pros and cons.
Build an efficient Machine Learning model with LightGBMPoo Kuan Hoong
Poo Kuan Hoong gives a presentation on building effective machine learning models with LightGBM. He begins with an introduction to decision trees and ensemble methods like gradient boosting. He explains that LightGBM is a gradient boosting framework that is faster and more accurate than other algorithms. It grows trees vertically rather than horizontally for increased speed and accuracy. Tips are provided for fine-tuning LightGBM like adjusting the number of leaves, learning rate, and using techniques like bagging and feature sub-sampling. A demo is then shown on a Kaggle dataset to predict safe drivers.
Machine Learning - Breast Cancer DiagnosisPramod Sharma
Machine learning is helping in making smart decisions faster. In this presentation measurements carried out on FNAC was analysed. The results were validated using 20 percent of the data. The data used for POC is from UCI Repository/
This document summarizes support vector machines (SVMs), a machine learning technique for classification and regression. SVMs find the optimal separating hyperplane that maximizes the margin between positive and negative examples in the training data. This is achieved by solving a convex optimization problem that minimizes a quadratic function under linear constraints. SVMs can perform non-linear classification by implicitly mapping inputs into a higher-dimensional feature space using kernel functions. They have applications in areas like text categorization due to their ability to handle high-dimensional sparse data.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
As we know that health care industry is completely based on assumptions, which after get tested and verified via various tests and patient have to be depend on the doctors knowledge on that topic . so we made a system that uses data mining techniques to predict the health of a person based on various medical test results. so we can predict the health of that person based on that analysis performed by the system.The system currently design only for heart issues, for that we had used Statlog (Heart) Data Set from UCI Machine Learning Repository it includes attributes like age, sex, chest pain type, cholesterol, sugar, outcomes,etc.for training the system. we only need to passed few general inputs in order to generate the prediction and the prediction results from all algorithms are they merged together by calculating there mean value that value shows the actual outcome of the prediction process which entirely works in background
This document discusses analyzing the Iris flower data set using R. It provides an overview of the Iris data, which contains measurements of Iris flowers from three species. Various data exploration techniques are demonstrated, including scatter plots, box plots, histograms and outlier detection. Clustering, classification and regression algorithms are explored, such as k-means clustering, Fisher's linear discriminant analysis, and linear regression. The document serves as a tutorial for analyzing a sample data set using common statistical and machine learning methods in R.
This document summarizes different methods for predicting stroke risk using a patient's historical medical information. It discusses scoring metrics like CHADS2 that evaluate risk factors such as heart failure, hypertension, age, and previous strokes. Bayesian Rule Lists are proposed to generate rules to predict stroke risk using decision lists. Recurrent neural networks can also be used with a custom loss function to model medical data over time. The document outlines the types of patient data needed, how to handle missing values and embed records, and how to generate and validate rules from the modeled and fitted data to predict stroke risk.
Anomaly detection (or Outlier analysis) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. It is used is applications such as intrusion detection, fraud detection, fault detection and monitoring processes in various domains including energy, healthcare and finance.
In this workshop, we will discuss the core techniques in anomaly detection and discuss advances in Deep Learning in this field.
Through case studies, we will discuss how anomaly detection techniques could be applied to various business problems. We will also demonstrate examples using R, Python, Keras and Tensorflow applications to help reinforce concepts in anomaly detection and best practices in analyzing and reviewing results.
What you will learn:
Anomaly Detection: An introduction
Graphical and Exploratory analysis techniques
Statistical techniques in Anomaly Detection
Machine learning methods for Outlier analysis
Evaluating performance in Anomaly detection techniques
Detecting anomalies in time series data
Case study 1: Anomalies in Freddie Mac mortgage data
Case study 2: Auto-encoder based Anomaly Detection for Credit risk with Keras and Tensorflow
Exploratory Data Analysis (EDA) was promoted by John Tukey in 1977 to encourage visually examining data without hypotheses. EDA uses graphical and non-graphical techniques like histograms, scatter plots, box plots to summarize variable characteristics. EDA allows understanding data distributions and relationships without models through inspection and information graphics. Common EDA goals are describing typical values, variability, distributions, and relationships between variables.
This document provides an overview of pattern recognition techniques. It begins with an introduction to pattern recognition and its applications. It then outlines the syllabus, which includes topics like design principles, statistical pattern recognition, parameter estimation methods, principal component analysis, linear discriminant analysis, and classification techniques. Under each topic, it provides further details and explanations.
This document discusses rule-based classification. It describes how rule-based classification models use if-then rules to classify data. It covers extracting rules from decision trees and directly from training data. Key points include using sequential covering algorithms to iteratively learn rules that each cover positive examples of a class, and measuring rule quality based on both coverage and accuracy to determine the best rules.
This article is used to give a basic information regarding the change points that occur in excel and in other files. The detection methods are proposed and they are analyzed with a real time example. The features and application of the change point is also discussed in the later. Copy the link given below and paste it in new browser window to get more information on Change Point:- http://www.transtutors.com/homework-help/statistics/change-point.aspx
Data analytics - Cross selling personal loansKuldeep Mahani
Single-handedly worked on a data mining project to build a model using logistic regression to identify profitable segments for cross-selling personal loans. A data set of 30,000 records was mined to gain customer insights and 9 distinct variables were identified to target profitable segments. Overall accuracy of the model was 86.03%.
This document provides an introduction to the concepts of data science. It defines data science as an interdisciplinary field drawing from computer science, statistics, and application domains. The document outlines the typical workflow of a data scientist, including obtaining data, exploring it, cleaning it, performing analysis, drawing conclusions, and reporting results. It describes the focus areas of the course as mathematics, technology, visualization, and communication skills. The document emphasizes the importance of learning new skills independently and communicating results effectively to non-technical audiences.
This document discusses using deep learning and convolutional neural networks to detect diabetic retinopathy through analyzing fundus images. It proposes a CNN model trained on a public Kaggle dataset to classify images based on the severity of retinopathy. The CNN architecture would automatically diagnose retinopathy without user input. The document outlines modules for an app, including uploading images, displaying results, and providing doctor referrals. It aims to address the growing problem of vision loss from diabetic retinopathy worldwide.
The document provides an overview of various machine learning algorithms and methods. It begins with an introduction to predictive modeling and supervised vs. unsupervised learning. It then describes several supervised learning algorithms in detail including linear regression, K-nearest neighbors (KNN), decision trees, random forest, logistic regression, support vector machines (SVM), and naive Bayes. It also briefly discusses unsupervised learning techniques like clustering and dimensionality reduction methods.
This document discusses the K-nearest neighbors (KNN) algorithm, an instance-based learning method used for classification. KNN works by identifying the K training examples nearest to a new data point and assigning the most common class among those K neighbors to the new point. The document covers how KNN calculates distances between data points, chooses the value of K, handles feature normalization, and compares strengths and weaknesses of the approach. It also briefly discusses clustering, an unsupervised learning technique where data is grouped based on similarity.
The document describes a study that used convolutional neural networks (CNNs) to detect brain tumors in MRI images. Three CNN models were developed and their performance was evaluated using metrics like accuracy, precision, recall, F1-score, and confusion matrices. Model 3 achieved the highest test accuracy of 94% for tumor detection. In total, over 2000 MRI images were used in the study after data augmentation. The CNN models incorporated convolution, pooling, and fully connected layers to analyze image features and classify tumors. This research demonstrates that CNNs can accurately detect brain tumors in medical images.
Random Forest Algorithm widespread popularity stems from its user-friendly nature and adaptability, enabling it to tackle both classification and regression problems effectively. The algorithm’s strength lies in its ability to handle complex datasets and mitigate overfitting, making it a valuable tool for various predictive tasks in machine learning.
One of the most important features of the Random Forest Algorithm is that it can handle the data set containing continuous variables, as in the case of regression, and categorical variables, as in the case of classification. It performs better for classification and regression tasks. In this tutorial, we will understand the working of random forest and implement random forest on a classification task.
Landing Self Service Analytics using Microsoft Azure & Power BIVisual_BI
This webinar recording is designed to provide guidance for implementing self-service analytics utilizing Microsoft’s cloud data platform (Azure & Power BI) for broad consumption across the organization.
Expanding the capabilities of SAC with App DesignVisual_BI
SAC Application Design provides the ability to build more powerful applications on the cloud through advanced scripting capabilities. In this webinar replay, we will be focusing on the major features of Application Design and how it would add value to SAP Analytics Cloud.
Logistic regression : Use Case | Background | Advantages | DisadvantagesRajat Sharma
This slide will help you to understand the working of logistic regression which is a type of machine learning model along with use cases, pros and cons.
Build an efficient Machine Learning model with LightGBMPoo Kuan Hoong
Poo Kuan Hoong gives a presentation on building effective machine learning models with LightGBM. He begins with an introduction to decision trees and ensemble methods like gradient boosting. He explains that LightGBM is a gradient boosting framework that is faster and more accurate than other algorithms. It grows trees vertically rather than horizontally for increased speed and accuracy. Tips are provided for fine-tuning LightGBM like adjusting the number of leaves, learning rate, and using techniques like bagging and feature sub-sampling. A demo is then shown on a Kaggle dataset to predict safe drivers.
Machine Learning - Breast Cancer DiagnosisPramod Sharma
Machine learning is helping in making smart decisions faster. In this presentation measurements carried out on FNAC was analysed. The results were validated using 20 percent of the data. The data used for POC is from UCI Repository/
This document summarizes support vector machines (SVMs), a machine learning technique for classification and regression. SVMs find the optimal separating hyperplane that maximizes the margin between positive and negative examples in the training data. This is achieved by solving a convex optimization problem that minimizes a quadratic function under linear constraints. SVMs can perform non-linear classification by implicitly mapping inputs into a higher-dimensional feature space using kernel functions. They have applications in areas like text categorization due to their ability to handle high-dimensional sparse data.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
As we know that health care industry is completely based on assumptions, which after get tested and verified via various tests and patient have to be depend on the doctors knowledge on that topic . so we made a system that uses data mining techniques to predict the health of a person based on various medical test results. so we can predict the health of that person based on that analysis performed by the system.The system currently design only for heart issues, for that we had used Statlog (Heart) Data Set from UCI Machine Learning Repository it includes attributes like age, sex, chest pain type, cholesterol, sugar, outcomes,etc.for training the system. we only need to passed few general inputs in order to generate the prediction and the prediction results from all algorithms are they merged together by calculating there mean value that value shows the actual outcome of the prediction process which entirely works in background
This document discusses analyzing the Iris flower data set using R. It provides an overview of the Iris data, which contains measurements of Iris flowers from three species. Various data exploration techniques are demonstrated, including scatter plots, box plots, histograms and outlier detection. Clustering, classification and regression algorithms are explored, such as k-means clustering, Fisher's linear discriminant analysis, and linear regression. The document serves as a tutorial for analyzing a sample data set using common statistical and machine learning methods in R.
This document summarizes different methods for predicting stroke risk using a patient's historical medical information. It discusses scoring metrics like CHADS2 that evaluate risk factors such as heart failure, hypertension, age, and previous strokes. Bayesian Rule Lists are proposed to generate rules to predict stroke risk using decision lists. Recurrent neural networks can also be used with a custom loss function to model medical data over time. The document outlines the types of patient data needed, how to handle missing values and embed records, and how to generate and validate rules from the modeled and fitted data to predict stroke risk.
Anomaly detection (or Outlier analysis) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. It is used is applications such as intrusion detection, fraud detection, fault detection and monitoring processes in various domains including energy, healthcare and finance.
In this workshop, we will discuss the core techniques in anomaly detection and discuss advances in Deep Learning in this field.
Through case studies, we will discuss how anomaly detection techniques could be applied to various business problems. We will also demonstrate examples using R, Python, Keras and Tensorflow applications to help reinforce concepts in anomaly detection and best practices in analyzing and reviewing results.
What you will learn:
Anomaly Detection: An introduction
Graphical and Exploratory analysis techniques
Statistical techniques in Anomaly Detection
Machine learning methods for Outlier analysis
Evaluating performance in Anomaly detection techniques
Detecting anomalies in time series data
Case study 1: Anomalies in Freddie Mac mortgage data
Case study 2: Auto-encoder based Anomaly Detection for Credit risk with Keras and Tensorflow
Exploratory Data Analysis (EDA) was promoted by John Tukey in 1977 to encourage visually examining data without hypotheses. EDA uses graphical and non-graphical techniques like histograms, scatter plots, box plots to summarize variable characteristics. EDA allows understanding data distributions and relationships without models through inspection and information graphics. Common EDA goals are describing typical values, variability, distributions, and relationships between variables.
This document provides an overview of pattern recognition techniques. It begins with an introduction to pattern recognition and its applications. It then outlines the syllabus, which includes topics like design principles, statistical pattern recognition, parameter estimation methods, principal component analysis, linear discriminant analysis, and classification techniques. Under each topic, it provides further details and explanations.
This document discusses rule-based classification. It describes how rule-based classification models use if-then rules to classify data. It covers extracting rules from decision trees and directly from training data. Key points include using sequential covering algorithms to iteratively learn rules that each cover positive examples of a class, and measuring rule quality based on both coverage and accuracy to determine the best rules.
This article is used to give a basic information regarding the change points that occur in excel and in other files. The detection methods are proposed and they are analyzed with a real time example. The features and application of the change point is also discussed in the later. Copy the link given below and paste it in new browser window to get more information on Change Point:- http://www.transtutors.com/homework-help/statistics/change-point.aspx
Data analytics - Cross selling personal loansKuldeep Mahani
Single-handedly worked on a data mining project to build a model using logistic regression to identify profitable segments for cross-selling personal loans. A data set of 30,000 records was mined to gain customer insights and 9 distinct variables were identified to target profitable segments. Overall accuracy of the model was 86.03%.
This document provides an introduction to the concepts of data science. It defines data science as an interdisciplinary field drawing from computer science, statistics, and application domains. The document outlines the typical workflow of a data scientist, including obtaining data, exploring it, cleaning it, performing analysis, drawing conclusions, and reporting results. It describes the focus areas of the course as mathematics, technology, visualization, and communication skills. The document emphasizes the importance of learning new skills independently and communicating results effectively to non-technical audiences.
This document discusses using deep learning and convolutional neural networks to detect diabetic retinopathy through analyzing fundus images. It proposes a CNN model trained on a public Kaggle dataset to classify images based on the severity of retinopathy. The CNN architecture would automatically diagnose retinopathy without user input. The document outlines modules for an app, including uploading images, displaying results, and providing doctor referrals. It aims to address the growing problem of vision loss from diabetic retinopathy worldwide.
The document provides an overview of various machine learning algorithms and methods. It begins with an introduction to predictive modeling and supervised vs. unsupervised learning. It then describes several supervised learning algorithms in detail including linear regression, K-nearest neighbors (KNN), decision trees, random forest, logistic regression, support vector machines (SVM), and naive Bayes. It also briefly discusses unsupervised learning techniques like clustering and dimensionality reduction methods.
This document discusses the K-nearest neighbors (KNN) algorithm, an instance-based learning method used for classification. KNN works by identifying the K training examples nearest to a new data point and assigning the most common class among those K neighbors to the new point. The document covers how KNN calculates distances between data points, chooses the value of K, handles feature normalization, and compares strengths and weaknesses of the approach. It also briefly discusses clustering, an unsupervised learning technique where data is grouped based on similarity.
The document describes a study that used convolutional neural networks (CNNs) to detect brain tumors in MRI images. Three CNN models were developed and their performance was evaluated using metrics like accuracy, precision, recall, F1-score, and confusion matrices. Model 3 achieved the highest test accuracy of 94% for tumor detection. In total, over 2000 MRI images were used in the study after data augmentation. The CNN models incorporated convolution, pooling, and fully connected layers to analyze image features and classify tumors. This research demonstrates that CNNs can accurately detect brain tumors in medical images.
Random Forest Algorithm widespread popularity stems from its user-friendly nature and adaptability, enabling it to tackle both classification and regression problems effectively. The algorithm’s strength lies in its ability to handle complex datasets and mitigate overfitting, making it a valuable tool for various predictive tasks in machine learning.
One of the most important features of the Random Forest Algorithm is that it can handle the data set containing continuous variables, as in the case of regression, and categorical variables, as in the case of classification. It performs better for classification and regression tasks. In this tutorial, we will understand the working of random forest and implement random forest on a classification task.
Landing Self Service Analytics using Microsoft Azure & Power BIVisual_BI
This webinar recording is designed to provide guidance for implementing self-service analytics utilizing Microsoft’s cloud data platform (Azure & Power BI) for broad consumption across the organization.
Expanding the capabilities of SAC with App DesignVisual_BI
SAC Application Design provides the ability to build more powerful applications on the cloud through advanced scripting capabilities. In this webinar replay, we will be focusing on the major features of Application Design and how it would add value to SAP Analytics Cloud.
ValQ Data Acquisition Transformation TechniquesVisual_BI
This document discusses best practices for data acquisition and transformation when using ValQ for Power BI. It provides an overview of ValQ and its capabilities for planning, budgeting, forecasting and other business modeling needs. The document also presents two case studies that demonstrate how ValQ can be used for portfolio management and modeling Azure consumption. Key recommendations include separating source data from staging tables, proper handling of data types and hierarchies, and using Power Query to transform data into a single ValQ dataset table.
In this webinar replay, we explore the various options available in SAP Lumira Designer and Visual BI Extensions for SAP Lumira Designer – (VBX) to help migrate existing SAP Dashboards/Xcelsius and BusinessObjects Explorer applications to SAP Lumira Designer/ SAP Analytics Cloud (SAC).
In this webinar recording, we explore why the latest version of VBI View is the only enterprise BI portal you need to manage multiple BI platforms like Tableau, Microsoft Power BI, SSRS, SAP BusinessObjects, Qlik, TIBCO Spotfire, MicroStrategy and more.
Learn why Microsoft Power BI is an Undisputed Market Leader?Visual_BI
Power BI Report Server is the on-premise version of Power BI that allows organizations to consume Power BI reports within their internal network behind the firewall. It provides a dedicated user interface and organizational resources to view and interact with Power BI reports on-premises. Power BI Embedded allows embedding Power BI reports and visualizations into third-party applications using REST APIs. It is used to distribute reports to a large audience without requiring each user to have a Power BI license. Premium capacity in Power BI provides dedicated cloud resources for large datasets, frequent refreshes and advanced capabilities like paginated reports and predictive analytics.
VBI View Your one stop solution to manage multiple BI PlatformsVisual_BI
In this webinar recording, we explore why Visual BI’s VBI View is the only enterprise BI portal you need to manage multiple BI platforms like Tableau, Microsoft Power BI, SSRS, SAP BusinessObjects, Qlik, TIBCO Spotfire, MicroStrategy and more.
Value driver planning for mining using microsoft power bi webinarVisual_BI
This document provides an overview of value driver planning and modeling for the mining industry using Microsoft Power BI. It discusses how typical spreadsheet models have limitations and how value driver models can help visualize the links between key performance indicators and operational drivers. The presentation demonstrates how ValQ, a product from Visual BI Solutions, can be used to build interactive value driver models in Power BI to support scenario analysis, planning and decision making for mining companies.
Snowflake: The most cost-effective agile and scalable data warehouse ever!Visual_BI
In this webinar, the presenter will take you through the most revolutionary data warehouse, Snowflake with a live demo and technical and functional discussions with a customer. Ryan Goltz from Chesapeake Energy and Tristan Handy, creator of DBT Cloud and owner of Fishtown Analytics will also be joining the webinar.
Decoding SAP's BI Analytics SAP Statement of Direction Visual_BI
In Sep 2019, SAP announced its new BI & Analytics strategy and Statement of Direction. This webinar, from Visual BI, will dwell deep into this Statement of Direction, what this announcement means to you, it’s the potential impact to your landscape and existing investments, and how to plan your BI and Analytics Initiatives for 2020.
Why Customers need to upgrade to SAP Lumira 2.2?Visual_BI
SAP Lumira 2.2 includes new features that enhance self-service capabilities for both the Discovery and Designer editions. In Discovery, there are improvements to filtering, number formatting, and SAP BW data presentation. Designer adds offline data refresh, variant support for prompts, and performance enhancements. It also allows for more end-user control through features like runtime application authoring and saving dashboard changes. These updates strengthen SAP Lumira's position in SAP's convergence strategy for agile data discovery and dashboarding.
Impactful Financial Reporting using Microsoft Power BI - WebinarVisual_BI
Financial reporting has traditionally had two options: the boring but very effective tabular reporting (preferred by many), and highly appealing but functionally limiting basic charts & graphs (preferred by a few).In this webinar, we will be showcasing how Finance users & analysts can have the best of both worlds!
This webinar we will focus on products of Tableau, it’s data preparation and analytics capabilities and evaluate its features with that of other leading BI tools.
This document outlines a webinar presentation about the ValQ product for modern digital planning. The webinar agenda includes overviews of Visual BI and ValQ, a ValQ design experience demo, building a model from scratch in under 5 minutes, a ValQ demo for executives, pricing plans, product vision and roadmap, and a Q&A. ValQ allows users to instantly visualize and optimize key performance indicators and drivers through interactive modeling and simulation in Power BI. It offers flexible modeling, integration with various data sources, high performance, and low total cost of ownership.
In this webinar replay, we dwell deep into the latest updates in BI and Analytics, technology, and process trends that will shape data and analytics strategy in 2019 and beyond.
In this webinar recording, we evaluate Traditional BI tools like SAP Business Objects (Web Intelligence and SAP Lumira Designer) and compare them against Self-Service BI and Data Discovery capabilities of the top players in the market, namely SAP Analytics Cloud, Microsoft Power BI, Tableau, Qlik Sense & TIBCO Spotfire.
Data Analytics Strategies & Solutions for SAP customersVisual_BI
SAP customers are challenged in multiple fronts today, where we have rapidly evolved tools and technologies with smaller internal IT teams to evaluate them. In this webinar replay, Visual BI will offer strategies and solutions for some of the most common challenges faced by SAP BI & Analytics Leaders, Managers and Architects.
In this webinar recording, we will be introducing the core concepts of Data Science and the resources in Azure to deliver a complete Data Science solution. We will also walk through a demonstration on how best to use Azure Databricks as a data scientist to process enterprise data and build a machine learning model to deploy.
How To Convert Your SAP BusinessObjects Unused Licenses To SAP Analytics CloudWiiisdom
Learn how you can easily find all your SAP BusinessObjects unused licenses to apply those resources to SAP Analytics Cloud and deliver greater agility to your organization thanks to hybrid analytics.
Understand the options SAP Cloud conversion program has to offer and hear the experience of one of your peers.
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"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."
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
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!
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.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
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