Search results “Neural network data mining techniques for banking”
Fraud Prevention | AI in Finance
Can AI be used for fraud prevention? Yes! In this video, we'll go over the history of fraud prevention techniques, then talk about some recent AI startups that are helping business reduce credit card fraud. We'll break down what the different AI models that help with fraud prevention look like (decision trees, logistic regression, neural networks) and finally, we'll try it out on a transaction dataset. Code for this video: https://github.com/llSourcell/AI_for_Financial_Data Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval More learning resources: https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505541d877 https://www.youtube.com/watch?v=GlV_QO5B2eU https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data https://pythonprogramming.net/python-programming-finance-machine-learning-framework/ https://gist.github.com/yhilpisch/648565d3d5d70663b7dc418db1b81676 https://www.quantopian.com/posts/simple-machine-learning-example Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Sign up for the next course at The School of AI: https://www.theschool.ai And please support me on Patreon: https://www.patreon.com/user?u=3191693 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 30506 Siraj Raval
Data Mining Techniques to Prevent Credit Card Fraud
Includes a brief introduction to credit card fraud, types of credit card fraud, how fraud is detected, applicable data mining techniques, as well as drawbacks.
Views: 12949 Ben Rodick
Data mining on Banking Industry
Data mining in the Banking industry
Views: 40 prazan kayestha
R tutorial: Intro to Credit Risk Modeling
Learn more about credit risk modeling with R: https://www.datacamp.com/courses/introduction-to-credit-risk-modeling-in-r Hi, and welcome to the first video of the credit risk modeling course. My name is Lore, I'm a data scientist at DataCamp and I will help you master some basics of the credit risk modeling field. The area of credit risk modeling is all about the event of loan default. Now what is loan default? When a bank grants a loan to a borrower, which could be an individual or a company, the bank will usually transfer the entire amount of the loan to the borrower. The borrower will then reimburse this amount in smaller chunks, including some interest payments, over time. Usually these payments happen monthly, quarterly or yearly. Of course, there is a certain risk that a borrower will not be able to fully reimburse this loan. This results in a loss for the bank. The expected loss a bank will incur is composed of three elements. The first element is the probability of default, which is the probability that the borrower will fail to make a full repayment of the loan. The second element is the exposure at default, or EAD, which is the expected value of the loan at the time of default. You can also look at this as the amount of the loan that still needs to be repaid at the time of default. The third element is loss given default, which is the amount of the loss if there is a default, expressed as a percentage of the EAD. Multiplying these three elements leads to the formula of expected loss. In this course, we will focus on the probability of default. Banks keep information on the default behavior of past customers, which can be used to predict default for new customers. Broadly, this information can be classified in two types. The first type of information is application information. Examples of application information are income, marital status, et cetera. The second type of information, behavioral information, tracks the past behavior of customers, for example the current account balance and payment arrear history. Let's have a look at the first ten lines of our data set. This data set contains information on past loans. Each line represents one customer and his or her information, along with a loan status indicator, which equals 1 if the customer defaulted, and 0 if the customer did not default. Loan status will be used as a response variable and the explanatory variables are the amount of the loan, the interest rate, grade, employment length, home ownership status, the annual income and the age. The grade is the bureau score of the customer, where A indicates the highest class of creditworthiness and G the lowest. This bureau score reflects the credit history of the individual and is the only behavioral variable in the data set. For an overview of the data structure for categorical variables, you can use the CrossTable() function in the gmodels package. Applying this function to the home ownership variable, you get a table with each of the categories in this variable, with the number of cases and proportions. Using loan status as a second argument, you can look at the relationship between this factor variable and the response. By setting prop.r equal to TRUE and the other proportions listed here equal to FALSE, you get the row-wise proportions. Now what does this result tell you? It seems that the default rate in the home ownership group OTHER is quite a bit higher than the default rate in, for example, the home ownership group MORTGAGE, with 17.5 versus 9.8 percent of defaults in these groups, respectively. Now, let's explore other aspects of the data using R.
Views: 32239 DataCamp
Project (Success of Bank Marketing Campaign)
The classification goal is to predict if the client will subscribe a term deposit using Logistic Regression, C5.0 Decision Tree, Random Forest, SVM, Neural Network models.
Views: 261 Royston Denzil
DEF CON 24 - Clarence Chio - Machine Duping 101: Pwning Deep Learning Systems
Deep learning and neural networks have gained incredible popularity in recent years. The technology has grown to be the most talked-about and least well-understood branch of machine learning. Aside from it’s highly publicized victories in playing Go, numerous successful applications of deep learning in image and speech recognition has kickstarted movements to integrate it into critical fields like medical imaging and self-driving cars. In the security field, deep learning has shown good experimental results in malware/anomaly detection, APT protection, spam/phishing detection, and traffic identification. This DEF CON 101 session will guide the audience through the theory and motivations behind deep learning systems. We look at the simplest form of neural networks, then explore how variations such as convolutional neural networks and recurrent neural networks can be used to solve real problems with an unreasonable effectiveness. Then, we demonstrate that most deep learning systems are not designed with security and resiliency in mind, and can be duped by any patient attacker with a good understanding of the system. The efficacy of applications using machine learning should not only be measured with precision and recall, but also by their malleability in an adversarial setting. After diving into popular deep learning software, we show how it can be tampered with to do what you want it do, while avoiding detection by system administrators. Besides giving a technical demonstration of deep learning and its inherent shortcomings in an adversarial setting, we will focus on tampering real systems to show weaknesses in critical systems built with it. In particular, this demo-driven session will be focused on manipulating an image recognition system built with deep learning at the core, and exploring the difficulties in attacking systems in the wild. We will introduce a tool that helps deep learning hackers generate adversarial content for arbitrary machine learning systems, which can help make models more robust. By discussing defensive measures that should be put in place to prevent the class of attacks demonstrated, we hope to address the hype behind deep learning from the context of security, and look towards a more resilient future of the technology where developers can use it safely in critical deployments. Bio: Clarence Chio graduated with a B.S. and M.S. in Computer Science from Stanford, specializing in data mining and artificial intelligence. He currently works as a Security Research Engineer at Shape Security, building a product that protects high valued web assets from automated attacks. At Shape, he works on the data analysis systems used to tackle this problem. Clarence spoke on Machine Learning and Security at PHDays, BSides Las Vegas and NYC, Code Blue, SecTor, and Hack in Paris. He had been a community speaker with Intel, and is also the founder and organizer of the ‘Data Mining for Cyber Security’ meet up group, the largest gathering of security data scientists in the San Francisco Bay Area.
Views: 8774 DEFCONConference
Predicting Stock Prices - Learn Python for Data Science #4
In this video, we build an Apple Stock Prediction script in 40 lines of Python using the scikit-learn library and plot the graph using the matplotlib library. The challenge for this video is here: https://github.com/llSourcell/predicting_stock_prices Victor's winning recommender code: https://github.com/ciurana2016/recommender_system_py Kevin's runner-up code: https://github.com/Krewn/learner/blob/master/FieldPredictor.py#L62 I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Stock prediction with Tensorflow: https://nicholastsmith.wordpress.com/2016/04/20/stock-market-prediction-using-multi-layer-perceptrons-with-tensorflow/ Another great stock prediction tutorial: http://eugenezhulenev.com/blog/2014/11/14/stock-price-prediction-with-big-data-and-machine-learning/ This guy made 500K doing ML stuff with stocks: http://jspauld.com/post/35126549635/how-i-made-500k-with-machine-learning-and-hft Please share this video, like, comment and subscribe! That's what keeps me going. and please support me on Patreon!: https://www.patreon.com/user?u=3191693 Check out this youtube channel for some more cool Python tutorials: https://www.youtube.com/watch?v=RZF17FfRIIo Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 549600 Siraj Raval
【TOSHIBA】「Data mining」Productivity improvement at the manufacturing site
Using Artificial Intelligence--or AI--to analyze “Big Data”and automatically identify the causes of manufacturing failures. Productivity improves dramatically.
Data Science in 25 Minutes with GP Pulipaka (Ganapathi Pulipaka): Mastering TensorFlow Tutorial
Ganapathi Pulipaka Chief Data Scientist for AI strategy, neural network architectures, application development of Machine learning, Deep Learning algorithms, experience in applying algorithms, integrating IoT platforms, Python, PyTorch, R, JavaScript, Go Lang, and TensorFlow, Big Data, IaaS, IoT, Data Science, Blockchain, Apache Hadoop, Apache Kafka, Apache Spark, Apache Storm, Apache Flink, SQL, NoSQL, Mathematics, Data Mining, Statistical Framework, SIEM with 6+ Years of AI Research and Development Experience in AWS, Azure, and GCP. Education: PostDoc– CS, PhD in Machine Learning, AI, Big Data Analytics, Engineering and CS, Colorado Technical University, Colorado Springs PhD, Business Administration in Data Analytics, Management Information Systems and Enterprise Resource Management, California University, Irvine Design, develop, and deploy machine learning and deep learning applications to solve the real-world problems in natural language processing, speech recognition, text to speech, chatbots, and speech to text analytics. Experience in data exploration, data preparation, applying supervised and unsupervised machine learning algorithms, machine learning model training, machine learning model evaluation, predictive analytics, bio-inspired algorithms, genetic algorithms, and natural language processing. I wrote around 400 research papers, published two books as a bestselling author on Amazon "The Future of Data Science and Parallel Computing," "Big Data Appliances for In-Memory Computing: A Real-World Research Guide for Corporations to Tame and Wrangle Their Data," and with a vast number of big data tool installations, SQL, NoSQL, practical machine learning project implementations, data analytics implementations, applied mathematics and statistics for publishing with the Universities as part of academic research programs. Currently, I’m working a video course “Mastering PyTorch for Advanced Data Scientist,” to build millions of data scientists around the world for AI practice. I implemented Many projects for Fortune 100 corporations Aerospace, manufacturing, IS-AFS (Apparel footwear solutions), IS-MEDIA (Media and Entertainment), ISUCCS (Customer care services), IS-AUTOMOTIVE (Automotive), IS-Utilities, retail, high-tech, life sciences, healthcare, chemical industry, banking, and service management. Public Keynote Speaker on Robotics and artificial intelligence held on May 21-22 at Los Angeles, CA. Published eBook in November 2017 for SAP Leonardo IoT “The Digital Evolution of Supply Chain Management with SAP Leonardo,” sponsored by SAP. Published eBook in December 2017 for Change HealthCare (McKesson’s HealthCare Corporation) on Machine Learning and Artificial Intelligence for Enterprise HealthCare and Health. Building recommendation systems and applying algorithms for anomaly detection in the financial industry. Deep reinforcement learning algorithms for robotics and IoT. Applying convolutional neural networks, recurrent neural networks, and long-term short memory with deep learning techniques to solve various conundrums. Developed number of machine learning and deep learning programs applying various algorithms and published articles with architecture and practical project implementations on GitHub, medium.com, data driven investor Experience with Python, TensorFlow, Caffe, Theano, Keras, Java, and R Programming languages implementing stacked auto encoders, backpropagation, perceptron, Restricted Boltzmann machines, and Deep Belief Networks. Experience in multiple IoT platforms. Twitter: https://twitter.com/gp_pulipaka Facebook: https://www.facebook.com/ganapathipulipaka LinkedIn: https://www.linkedin.com/in/dr-ganapathi-pulipaka-56417a2
Views: 146 GP Pulipaka
Computer Applications: An International Journal (CAIJ) ISSN :2393 - 8455 http://airccse.com/caij/index.html ********************************************* Computer Applications: An International Journal (CAIJ), Vol.4, No.1/2/3/4, November 2017 DOI:10.5121/caij.2017.4401 THE EFFECTIVENESS OF DATA MINING TECHNIQUES IN BANKING Yuvika Priyadarshini Researcher, Jharkhand Rai University, Ranchi. ABSTRACT The aim of this study is to identify the extent of Data mining activities that are practiced by banks, Data mining is the ability to link structured and unstructured information with the changing rules by which people apply it. It is not a technology, but a solution that applies information technologies. Currently several industries including like banking, finance, retail, insurance, publicity, database marketing, sales predict, etc are Data Mining tools for Customer . Leading banks are using Data Mining tools for customer segmentation and benefit, credit scoring and approval, predicting payment lapse, marketing, detecting illegal transactions, etc. The Banking is realizing that it is possible to gain competitive advantage deploy data mining. This article provides the effectiveness of Data mining technique in organized Banking. It also discusses standard tasks involved in data mining; evaluate various data mining applications in different sectors KEYWORDS Definition of Data Mining and its task, Effectiveness of Data Mining Technique, Application of Data Mining in Banking, Global Banking Industry Trends, Effective Data Mining Component and Capabilities, Data Mining Strategy, Benefit of Data Mining Program in Banking
Views: 46 aircc journal
StatQuest: Principal Component Analysis (PCA), Step-by-Step
Principal Component Analysis, is one of the most useful data analysis and machine learning methods out there. It can be used to identify patterns in highly complex datasets and it can tell you what variables in your data are the most important. Lastly, it can tell you how accurate your new understanding of the data actually is. In this video, I go one step at a time through PCA, and the method used to solve it, Singular Value Decomposition. I take it nice and slowly so that the simplicity of the method is revealed and clearly explained. If you are interested in doing PCA in R see: https://youtu.be/0Jp4gsfOLMs For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt... https://teespring.com/stores/statquest ...or buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/
Lecture 10 - Neural Networks
Neural Networks - A biologically inspired model. The efficient backpropagation learning algorithm. Hidden layers. Lecture 10 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/ This lecture was recorded on May 3, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
Views: 356290 caltech
Making Predictions with Data and Python : Predicting Credit Card Default | packtpub.com
This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2eZbdPP]. Demonstrate how to build, evaluate and compare different classification models for predicting credit card default and use the best model to make predictions. • Introduce, load and prepare data for modeling • Show how to build different classification models • Show how to evaluate models and use the best to make predictions For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 25184 Packt Video
Natural Language Processing In 10 Minutes | NLP Tutorial For Beginners | NLP Training | Edureka
** Natural Language Processing Using Python: https://www.edureka.co/python-natural-language-processing-course ** This Edureka video will provide you with a short and crisp description of NLP (Natural Language Processing) and Text Mining. You will also learn about the various applications of NLP in the industry. NLP Tutorial : https://www.youtube.com/watch?v=05ONoGfmKvA Subscribe to our channel to get video updates. Hit the subscribe button above. ------------------------------------------------------------------------------------------------------- #NLPin10minutes #NLPtutorial #NLPtraining #Edureka Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Instagram: https://www.instagram.com/edureka_learning/ ------------------------------------------------------------------------------------------------------- - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training, you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Natural Language Processing using Python Training focuses on step by step guide to NLP and Text Analytics with extensive hands-on using Python Programming Language. It has been packed up with a lot of real-life examples, where you can apply the learned content to use. Features such as Semantic Analysis, Text Processing, Sentiment Analytics and Machine Learning have been discussed. This course is for anyone who works with data and text– with good analytical background and little exposure to Python Programming Language. It is designed to help you understand the important concepts and techniques used in Natural Language Processing using Python Programming Language. You will be able to build your own machine learning model for text classification. Towards the end of the course, we will be discussing various practical use cases of NLP in python programming language to enhance your learning experience. -------------------------- Who Should go for this course ? Edureka’s NLP Training is a good fit for the below professionals: From a college student having exposure to programming to a technical architect/lead in an organisation Developers aspiring to be a ‘Data Scientist' Analytics Managers who are leading a team of analysts Business Analysts who want to understand Text Mining Techniques 'Python' professionals who want to design automatic predictive models on text data "This is apt for everyone” --------------------------------- Why Learn Natural Language Processing or NLP? Natural Language Processing (or Text Analytics/Text Mining) applies analytic tools to learn from collections of text data, like social media, books, newspapers, emails, etc. The goal can be considered to be similar to humans learning by reading such material. However, using automated algorithms we can learn from massive amounts of text, very much more than a human can. It is bringing a new revolution by giving rise to chatbots and virtual assistants to help one system address queries of millions of users. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Human language, developed over thousands and thousands of years, has become a nuanced form of communication that carries a wealth of information that often transcends the words alone. NLP will become an important technology in bridging the gap between human communication and digital data. --------------------------------- For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Views: 38565 edureka!
Build A Complete Project In Machine Learning | Credit Card Fraud Detection | Eduonix
Look what we have for you! Another complete project in Machine Learning! In today's tutorial, we will be building a Credit Card Fraud Detection System from scratch! It is going to be a very interesting project to learn! It is one of the 10 projects from our course 'Projects in Machine Learning' which is currently running on Kickstarter. For this project, we will be using the several methods of Anomaly detection with Probability Densities. We will be implementing the two major algorithms namely, 1. A local out wire factor to calculate anomaly scores. 2. Isolation forced algorithm. To get started we will first build a dataset of over 280,000 credit card transactions to work on! You can access the source code of this tutorial here: https://github.com/eduonix/creditcardML Want to learn Machine learning in detail? Then try our course Machine Learning For Absolute Beginners. Apply coupon code "YOUTUBE10" to get this course for $10 http://bit.ly/2Mi5IuP Kickstarter Campaign on AI and ML E-Degree is Launched. Back this Campaign and Explore all the Courses with over 58 Hours of Learning. Link- http://bit.ly/aimledegree Thank you for watching! We’d love to know your thoughts in the comments section below. Also, don’t forget to hit the ‘like’ button and ‘subscribe’ to ‘Eduonix Learning Solutions’ for regular updates. https://goo.gl/BCmVLG Follow Eduonix on other social networks: ■ Facebook: http://bit.ly/2nL2p59 ■ Linkedin: http://bit.ly/2nKWhKa ■ Instagram: http://bit.ly/2nL8TRu | @eduonix ■ Twitter: http://bit.ly/2eKnxq8
Multi-threaded software for algorithmic trading featuring machine learning / artificial intelligence
Here is an authomatic trading script coded in Perl by me as a 'side project', and featuring machine learning (neural networks + genetic algorithm). I used machine learning to predict the trends of 10 stocks included in a couple of arbitrary portfolios: 5 stocks per portfolio. Buy/sell/hold decisions taken by the system are based on model outputs and specific trading strategies. The genetic algorithm, entirely designed by me, is used to help creating an optimum portfolio of shares based on statistical parameters. Predictive model features a supervised machine learning scheme enompassing different maths such as 8 different neural networks (perceptron), one for each stock, stochastic process and regression on existing data. The systems learns dynamically from previous negotiations stored in a specific database. Negotiating sessions are highly parallelized (multi-threading). Semaphores are use to coordinate threads execution and speed up trading actions. This video has been made for demostration purpose only, therefore all quotes you'll see in it are just simulated with pseudo-random generators. The idea was also to test the system before throwing it, evetually, on real data. There's no correspondence between quotations you see in this video and real market data. Next steps would be including sell/buy codes from a whatsoever bank trading web-service by sobstituting the pseudo-random generator with real quotes from other existing web-services (e.g.yahoo, google...).
Views: 55 Michele Scaratti
Credit Card Fraud Detection
Get the project at http://nevonprojects.com/credit-card-fraud-detection-project/ The credit card fraud detection features uses user behavior and location scanning to check for unusual patterns.
Views: 22611 Nevon Projects
Customer Segmentation in Python - PyConSG 2016
Speaker: Mao Ting Description By segmenting customers into groups with distinct patterns, businesses can target them more effectively with customized marketing and product features. I'll dive into a few machine learning and statistical techniques to extract insights from customer data, and demonstrate how to execute them on real data using Python and open-source libraries. Abstract I will go through clustering and decision tree analysis using sciki-learn and two-sample t test using scipy. We will learn the intuition for each technique, the math behind them, and how to implement them and evaluate the results using Python. I will be using open-source data for the demonstration, and show what insights you can extract from actual data using these techniques. Event Page: https://pycon.sg Produced by Engineers.SG Help us caption & translate this video! http://amara.org/v/P6SD/
Views: 16853 Engineers.SG
Email Spam Classifier using Data Mining Techniques
Email is an effective, faster and cheaper way of communication. It is expected that the total number of worldwide email accounts is increased from 3.3 billion email accounts in 2012 to over 4.3 billion by the end of year 2016. Spam is an unwanted, junk, unsolicited bulk mails, used to spreading virus, Trojans, malicious code, advertisement or to gain profit on negligible cost. Ham is a legitimate, wanted, solicited mails. Email spamming is increasing day by day because of effective, fast and cheap way of exchanging information with each other. According to the investigation, User receives spam mails - ham mails About 120 billion of spam mails are sent per day and the cost of sending is approximately zero. Spam is a major problem that attacks the existence of electronic mails. So, it is very important to distinguish ham emails from spam emails, many methods have been proposed for classification of email as spam or ham emails. Classification Classification is a predictive modelling. Classification consists of assigning a class label to a set of unclassified cases as in Steps of Classification: 1. Model construction: Describing a set of predetermined classes -Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute. -The set of tuples used for model construction is training set. -The model is represented as classification rules, decision trees, or mathematical formulae. 2. Model usage: For classifying future or unknown objects -Estimate accuracy of the model -If the accuracy is acceptable, use the model to classify new data For more information and query visit our website: Website : http://www.e2matrix.com Blog : http://www.e2matrix.com/blog/ WordPress : https://teche2matrix.wordpress.com/ Blogger : https://teche2matrix.blogspot.in/ Contact Us : +91 9041262727 Follow Us on Social Media Facebook : https://www.facebook.com/etwomatrix.researchlab Twitter : https://twitter.com/E2MATRIX1 LinkedIn : https://www.linkedin.com/in/e2matrix-training-research Google Plus : https://plus.google.com/u/0/+E2MatrixJalandhar Pinterest : https://in.pinterest.com/e2matrixresearchlab/ Tumblr : https://www.tumblr.com/blog/e2matrix24
Getting Started with Orange 06: Making Predictions
Making predictions with classification tree and logistic regression. Train data set: http://tinyurl.com/fruits-and-vegetables-train Test data set: http://tinyurl.com/test-fruits-and-vegetables License: GNU GPL + CC Music by: http://www.bensound.com/ Website: http://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 65119 Orange Data Mining
Breast Cancer Diagnosis with Artificial Neural Network
Building, training, exporting and embedding an artificial neural network for use in a custom application for diagnosing cancer in breast tissue samples. Using patient data samples from UCI Machine Learning Repository for research. The resulting application and AI builder are available for download. Send email to [email protected] to request. Or visit tinmansystems.com/aibuilder
Views: 71707 TinMan Systems
BADM 1.1: Data Mining Applications
This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining. It is part of a series of 37 videos, all of which are available on YouTube. For more information: www.dataminingbook.com twitter.com/gshmueli facebook.com/dataminingbook Here is the complete list of the videos: • Welcome to Business Analytics Using Data Mining (BADM) • BADM 1.1: Data Mining Applications • BADM 1.2: Data Mining in a Nutshell • BADM 1.3: The Holdout Set • BADM 2.1: Data Visualization • BADM 2.2: Data Preparation • BADM 3.1: PCA Part 1 • BADM 3.2: PCA Part 2 • BADM 3.3: Dimension Reduction Approaches • BADM 4.1: Linear Regression for Descriptive Modeling Part 1 • BADM 4.2 Linear Regression for Descriptive Modeling Part 2 • BADM 4.3 Linear Regression for Prediction Part 1 • BADM 4.4 Linear Regression for Prediction Part 2 • BADM 5.1 Clustering Examples • BADM 5.2 Hierarchical Clustering Part 1 • BADM 5.3 Hierarchical Clustering Part 2 • BADM 5.4 K-Means Clustering • BADM 6.1 Classification Goals • BADM 6.2 Classification Performance Part 1: The Naive Rule • BADM 6.3 Classification Performance Part 2 • BADM 6.4 Classification Performance Part 3 • BADM 7.1 K-Nearest Neighbors • BADM 7.2 Naive Bayes • BADM 8.1 Classification and Regression Trees Part 1 • BADM 8.2 Classification and Regression Trees Part 2 • BADM 8.3 Classification and Regression Trees Part 3 • BADM 9.1 Logistic Regression for Profiling • BADM 9.2 Logistic Regression for Classification • BADM 10 Multi-Class Classification • BADM 11 Ensembles • BADM 12.1 Association Rules Part 1 • BADM 12.2 Association Rules Part 2 • Neural Networks: Part I • Neural Nets: Part II • Discriminant Analysis (Part 1) • Discriminant Analysis: Statistical Distance (Part 2) • Discriminant Analysis: Misclassification costs and over-sampling (Part 3)
Views: 3127 Galit Shmueli
AI in Financial Services–Final Mile of a Debit Card Fraud Machine Learning Model
This meetup was recorded on August 21, 2018 in Mountain View, CA. Description: Today, credit and debit card fraud detection machine learning models are a critical component of a financial institution’s fraud mitigation operations. Predictive performance of these models is extremely important to help catch fraudsters and shut down a customer’s compromised card as soon as possible. Because of this, data scientists often focus all efforts on the training phase of the model life cycle, trying to squeeze out as much predictive power as possible. In highly regulated U.S. banks, and really anywhere one is deploying machine learning models for critical business results, carefully delivering the models that final mile into production can be just as important. In this talk, we explore two ways data scientists can help deliver in the final mile: Gradient Boosting Machine (GBM) fraud model interpretability and model monitoring. Speaker's Bio: Daniel Dixon is a senior data engineer on the Enterprise Analytics & Data Science team at Wells Fargo, where he is responsible for designing and building scalable, big data pipelines to feed intelligent systems across the bank. In this role he specializes in big data and advanced analytics challenges, utilizing machine learning, statistics, process optimization, and visualization techniques to analyze and assemble large, complex datasets. Prior to joining Wells Fargo in 2014, Daniel spent five years as a professional services consultant for Teradata with a focus on visualization and ETL technologies. He holds a Bachelor of Science in Electrical Engineering with a minor in Computer Science from the Georgia Institute of Technology. LinkedIn: www.linkedin.com/in/danielbdixon
Views: 757 H2O.ai
Deep Learning on Graphs (Neo4j Online Meetup #41)
Knowledge graphs generation is outpacing the ability to intelligently use the information that they contain. Octavian's work is pioneering Graph Artificial Intelligence to provide the brains to make knowledge graphs useful. Our neural networks can take questions and knowledge graphs and return answers. Imagine: a google assistant that reads your own knowledge graph (and actually works) a BI tool reads your business' knowledge graph a legal assistant that reads the graph of your case Taking a neural network approach is important because neural networks deal better with the noise in data and variety in schema. Using neural networks allows people to ask questions of the knowledge graph in their own words, not via code or query languages. Octavian's approach is to develop neural networks that can learn to manipulate graph knowledge into answers. This approach is radically different to using networks to generate graph embeddings. We believe this approach could transform how we interact with databases. Prior knowledge of Neural Networks is not required and the talk will include a simple demonstration of how a Neural Network can use graph data. ----------------------------- ABOUT THE SPEAKER ----------------------------- Andy believes that graphs have the potential to provide both a representation of the world and a technical interface that allows us to develop better AI and to turn it rapidly into useful products. Andy combines expertise in machine learning with experience building and operating distributed software systems and an understanding of the scientific process. Before he worked as a software engineer, Andy was a chemist, and he enjoys using the tensor algebra that he learned in quantum chemistry when working on neural networks. ----------------------------- ONLINE DISCUSSIONS ----------------------------- We'll be taking questions live during the session, but if you have any before or after be sure to post them in the project's thread in the Neo4j Community Site (https://community.neo4j.com/t/online-meetup-deep-learning-with-knowledge-graphs/2963). ---------------------------------------------------------------------------------------- WANT TO BE FEATURED IN OUR NEXT NEO4J ONLINE MEETUP? ---------------------------------------------------------------------------------------- We select talks from our Neo4j Community site! https://community.neo4j.com/ To submit your talk, post in in the #projects (if including a link to github or website) or #content (if linking to a blog post, slideshow, video, or article) categories. ------------------------------------------------------------------------- VOTE FOR THE PRESENTATIONS YOU'D LIKE TO SEE! ------------------------------------------------------------------------- 'VOTE' for the projects and content you'd like to see! Browse the the projects and content categories in our community site and 'heart' the ones you're interested in seeing! community.neo4j.com
Views: 3192 Neo4j
Danske Bank: Innovating in Artificial Intelligence and Deep Learning to Detect Sophisticated Fraud
Using a cutting edge “champion/challenger” method, Danske Bank employs an eclectic team to innovate in fraud detection with Artificial Intelligence and Deep Learning with incredible results in fighting sophisticated fraud.
Views: 363 Teradata Customers
Understanding Wavelets, Part 1: What Are Wavelets
This introductory video covers what wavelets are and how you can use them to explore your data in MATLAB®. •Try Wavelet Toolbox: https://goo.gl/m0ms9d •Ready to Buy: https://goo.gl/sMfoDr The video focuses on two important wavelet transform concepts: scaling and shifting. The concepts can be applied to 2D data such as images. Video Transcript: Hello, everyone. In this introductory session, I will cover some basic wavelet concepts. I will be primarily using a 1-D example, but the same concepts can be applied to images, as well. First, let's review what a wavelet is. Real world data or signals frequently exhibit slowly changing trends or oscillations punctuated with transients. On the other hand, images have smooth regions interrupted by edges or abrupt changes in contrast. These abrupt changes are often the most interesting parts of the data, both perceptually and in terms of the information they provide. The Fourier transform is a powerful tool for data analysis. However, it does not represent abrupt changes efficiently. The reason for this is that the Fourier transform represents data as sum of sine waves, which are not localized in time or space. These sine waves oscillate forever. Therefore, to accurately analyze signals and images that have abrupt changes, we need to use a new class of functions that are well localized in time and frequency: This brings us to the topic of Wavelets. A wavelet is a rapidly decaying, wave-like oscillation that has zero mean. Unlike sinusoids, which extend to infinity, a wavelet exists for a finite duration. Wavelets come in different sizes and shapes. Here are some of the well-known ones. The availability of a wide range of wavelets is a key strength of wavelet analysis. To choose the right wavelet, you'll need to consider the application you'll use it for. We will discuss this in more detail in a subsequent session. For now, let's focus on two important wavelet transform concepts: scaling and shifting. Let' start with scaling. Say you have a signal PSI(t). Scaling refers to the process of stretching or shrinking the signal in time, which can be expressed using this equation [on screen]. S is the scaling factor, which is a positive value and corresponds to how much a signal is scaled in time. The scale factor is inversely proportional to frequency. For example, scaling a sine wave by 2 results in reducing its original frequency by half or by an octave. For a wavelet, there is a reciprocal relationship between scale and frequency with a constant of proportionality. This constant of proportionality is called the "center frequency" of the wavelet. This is because, unlike the sinewave, the wavelet has a band pass characteristic in the frequency domain. Mathematically, the equivalent frequency is defined using this equation [on screen], where Cf is center frequency of the wavelet, s is the wavelet scale, and delta t is the sampling interval. Therefore when you scale a wavelet by a factor of 2, it results in reducing the equivalent frequency by an octave. For instance, here is how a sym4 wavelet with center frequency 0.71 Hz corresponds to a sine wave of same frequency. A larger scale factor results in a stretched wavelet, which corresponds to a lower frequency. A smaller scale factor results in a shrunken wavelet, which corresponds to a high frequency. A stretched wavelet helps in capturing the slowly varying changes in a signal while a compressed wavelet helps in capturing abrupt changes. You can construct different scales that inversely correspond the equivalent frequencies, as mentioned earlier. Next, we'll discuss shifting. Shifting a wavelet simply means delaying or advancing the onset of the wavelet along the length of the signal. A shifted wavelet represented using this notation [on screen] means that the wavelet is shifted and centered at k. We need to shift the wavelet to align with the feature we are looking for in a signal.The two major transforms in wavelet analysis are Continuous and Discrete Wavelet Transforms. These transforms differ based on how the wavelets are scaled and shifted. More on this in the next session. But for now, you've got the basic concepts behind wavelets.
Views: 176464 MATLAB
Using Machine Learning to Detect Money Laundering
In this talk, Maria (Mahtab) Kamali, Data Scientist at Thomson Reuters, will present new machine learning methods employed to discover money laundering patterns. Money laundering is the process of transferring profit from crime and illegal activities into legitimate assets. Based on the United Nations Office on Drugs and Crime, 2 to 5% of global GDP, or $800 billion - $2 trillion, is laundered globally on an annual basis. The laundered money often finances drug trafficking, human trafficking and terrorist activities. Advanced analytic techniques are increasingly being employed to identify and reduce illegal activities such as money laundering. Machine Learning (ML) is playing an increasingly important role by way of two main mechanisms: transaction behavioural pattern analysis and network structure. Many financial institutions combine these two mechanisms to construct a rule-based system to flag suspicious transactions. A challenge is that these systems can generate significant false positives which require tedious resource-intensive investigations. Such rule-based systems are also challenged when seeking to detect new patterns and/or activities. Modern data mining and machine learning methods can help financial institutions reduce system-generated false positives.
LDSS 2017 - Building a Real-time Banking Fraud Detection System - Dr Karthik Tadinada, Featurespace
If you enjoyed this talk join us at our next event, https://cambridgespark.com/datascience-summit or sign up for regular updates, https://bit.ly/2rA5VRc
Views: 1923 Cambridge Spark
Fraud Detection in Real Time with Graphs
Gorka Sadowski, a CISSP from the akalak cybersecurity consulting firm and Philip Rathle, VP of Product for Neo4j, talk about handling real-time fraud detection with graphs. They discuss retail banking + first-party fraud, automobile insurance fraud and online payment ecommerce fraud.
Views: 17278 Neo4j
Introduction to Deep Learning: What Is Deep Learning?
Explore deep learning fundamentals in this MATLAB® Tech Talk. - Learn more about Deep Learning: https://goo.gl/F8tBZi - Download a trial: https://goo.gl/PSa78r You’ll learn why deep learning has become so popular, and walk through 3 concepts: what deep learning is, how it is used in the real world, and how you can get started. Deep learning is a machine learning technique that learns features and tasks directly from data. This data can include images, text, or sound. The video uses an example image recognition problem to illustrate how deep learning algorithms learn to classify input images into appropriate categories. Lastly, the video explores the three reasons why deep learning has surged in popularity over the last five years.
Views: 85452 MATLAB
AI for Marketing & Growth #1 - Predictive Analytics in Marketing
AI for Marketing & Growth #1 - Predictive Analytics in Marketing Download our list of the world's best AI Newsletters 👉https://hubs.ly/H0dL7N60 Welcome to our brand new AI for Marketing & Growth series in which we’ll get you up to speed on Predictive Analytics in Marketing! This series you-must-watch-this-every-two-weeks sort of series or you’re gonna get left behind.. Predictive analytics in marketing is a form of data mining that uses machine learning and statistical modeling to predict the future. Based on historical data. Applications in action are all around us already. For example, If your bank notifies you of suspicious activity on your bank card, it is likely that a statistical model was used to predict your future behavior based on your past transactions. Serious deviations from this pattern are flagged as suspicious. And that’s when you get the notification. So why should marketers care? Marketers can use it to help optimise conversions for their funnels by forecasting the best way to move leads down the different stages, turning them into qualified prospects and eventually converting them into paying customers. Now, if you can predict your customers’ behavior along the funnel, you can also think of messages to best influence that behavior and reach your customer’s highest potential value. This is super-intelligence for marketers! Imagine if you could not only determine whether a lead is a good fit for your product but also which are most promising. This’ll allow you to focus your team’s efforts on leads with the highest ROI. Which will also imply a shift in mindset. Going from quantity metrics, or how many leads you can attract, to quality metrics, or how many good leads you can engage. You can now easily predict your OMTM or KPIs in real-time and finally push vanity metrics aside. For example, based on my location, age, past purchases, and gender, how likely are you to buy eggs I if you just added milk to your basket? A supermarket can use this information to automatically recommend products to you A financial services provider can use thousands of data points created by your online behaviour to decide which credit card to offer you, and when. A fashion retailer can use your data to decide which shoes to recommend as your next purchase, based on the jacket you just bought. Sure, businesses can improve their conversion rates, but the implications are much bigger than that. Predictive analytics allows companies to set pricing strategies based on consumer expectations and competitor benchmarks. Retailers can predict demand, and therefore make sure they have the right level of stock for each of their products. The evidence of this revolution is already around us. Every time we type a search query into Google, Facebook or Amazon we’re feeding data into the machine. The machine thrives on data, growing ever more intelligent. To leverage the potential of artificial intelligence and predictive analytics, there are four elements that organizations need to put into place. 1. The right questions 2. The right data 3. The right technology 4. The right people Ok.. let’s look at some use cases of businesses that are already leveraging predictive analytics. Other topics discussed: Ai analytics case study artificial intelligence big data deep learning demand forecasting forecasting sales machine learning predictive analytics in marketing data mining statistical modelling predict the future historical data AI Marketing machine learning marketing machine learning in marketing artificial intelligence in marketing artificial intelligence AI Machine learning ------------------------------------------------------- Amsterdam bound? Want to make AI your secret weapon? Join our A.I. for Marketing and growth Course! A 2-day course in Amsterdam. No previous skills or coding required! https://hubs.ly/H0dkN4W0 OR Check out our 2-day intensive, no-bullshit, skills and knowledge Growth Hacking Crash Course: https://hubs.ly/H0dkN4W0 OR our 6-Week Growth Hacking Evening Course: https://hubs.ly/H0dkN4W0 OR Our In-House Training Programs: https://hubs.ly/H0dkN4W0 OR The world’s only Growth & A.I. Traineeship https://hubs.ly/H0dkN4W0 Make sure to check out our website to learn more about us and for more goodies: https://hubs.ly/H0dkN4W0 London Bound? Join our 2-day intensive, no-bullshit, skills and knowledge Growth Marketing Course: https://hubs.ly/H0dkN4W0 ALSO! Connect with Growth Tribe on social media and stay tuned for nuggets of wisdom, updates and more: Facebook: https://www.facebook.com/GrowthTribeIO/ LinkedIn: https://www.linkedin.com/company/growth-tribe Twitter: https://twitter.com/GrowthTribe/ Instagram: https://www.instagram.com/growthtribe/ Snapchat: growthtribe Video URL: https://youtu.be/uk82DHcU7z8
Views: 18330 Growth Tribe
F14 Team P: Bank Telemarketing Project
Our team deals with a bank telemarketing dataset. We built various models to predict client decision and find out the characteristics of potential subscribers. Based on the results from our data mining process, we further made recommendations on how to make the bank's telemarketing more effective.
Views: 416 E Zhang
Sentiment Analysis in 4 Minutes
Link to the full Kaggle tutorial w/ code: https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words Sentiment Analysis in 5 lines of code: http://blog.dato.com/sentiment-analysis-in-five-lines-of-python I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ The Stanford Natural Language Processing course: https://class.coursera.org/nlp/lecture Cool API for sentiment analysis: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 100597 Siraj Raval
Bank Telemarketing Campaign
Analyze customer response to bank telemarketing campaign by using machine learning techniques
Views: 31 Shashank Rampalli
Model Selection and Management with KNIME
This video shows what you can do with KNIME in terms of model selection and management. The workflow used in this video can be found on the KNIME EXAMPLES Server under 50_Applications/21_Model_Selection_and_Management It explores: - data and tool blending, by importing R code and R models into a KNIME workflow - cross-validation to assess data set quality - data visualization using the new Javascript based nodes - three techniques for dimensionality reduction (% missing values, low variance, high correlation) - machine learning (decision tree, naive Bayes, neural networks, k-Means, logistic regression) - random forest - how to build your own customized ensemble model - Modular PMML export - Automated model selection and management - Model evaluation (ROC curve) - one-click model deployment - the new look and feel of the KNIME Analytics Platform 3.0 Th example workflow used for this video can be found in the KNIME EXAMPLES server (in the KNIME Explorer from within the KNIME workbench) under 050_Applications/05021_ModelSelectionandManagement
Views: 16080 KNIMETV
Applied Machine Learning - Credit Card Fraud Detection Problem
BIA 780 Applications of Artificial Intelligence Final Project Presentation
Views: 9041 Joshua Krause
Hanna Meyer: "Machine-learning based modelling of spatial and spatio-temporal data" (practical)
This practical session will base on the introductory lecture on machine-learning based modelling of spatial and spatio-temporal data held on Monday. Two examples will be provided to dive into machine learning for spatial and spatio-temporal data in R: The first example is a classic remote sensing example dealing with land cover classification at the example of the Banks Peninsula in New Zealand that suffers from spread of the invasive gorse. In this example we will use the random forest classifier via the caret package to learn the relationships between spectral satellite information and provided reference data on the land cover classes. Spatial predictions will then be made to create a map of land use/cover based on the trained model. As second example, the vignette "Introduction to CAST" is taken from the CAST package. In this example the aim is to model soil moisture in a spatio-temporal way for the cookfarm (http://gsif.r-forge.r-project.org/cookfarm.html). In this example we focus on the differences between different cross-validation strategies for error assessment of spatio-temporal prediction models as well as on the need of a careful selection of predictor variables to avoid overfitting. Slides: https://github.com/HannaMeyer/Geostat2018/tree/master/slides Exercise A: https://github.com/HannaMeyer/Geostat2018/tree/master/practice/LUCmodelling.html Exercise B: https://github.com/HannaMeyer/Geostat2018/tree/master/practice/CAST-intro.html Data for Exercise A: https://github.com/HannaMeyer/Geostat2018/tree/master/practice/data/
Weka Tutorial 02: Data Preprocessing 101 (Data Preprocessing)
This tutorial demonstrates various preprocessing options in Weka. However, details about data preprocessing will be covered in the upcoming tutorials.
Views: 168184 Rushdi Shams
Data Mining Software in Healthcare
This is a brief discussion of data mining software with an emphasis on the healthcare field.
Views: 4229 Joshua White
Enhanced AML fraud detection solutions with Azure Machine Learning - Ravi Kanth
AML (Anti-Money Laundering) solutions typically tend to be rule engine driven and involve significant manual follow-up activities. Using a Machine Learning approach, AML solutions can be enhanced to reduce false positives, as well as to better prioritize the items flagged for manual follow-up. This one-hour session will be structured as below: First, we will briefly discuss the AML domain and the typical AML detection workflow. Secondly, we will have an in-depth look into how Machine Learning algorithms can help with enhancing the AML solutions through better detection and better prioritization of detected fraud activity items. Thirdly, we will look at how this can be implemented with Azure Machine Learning to achieve qualitative as well as quantitative enhancement objectives. Finally, we will briefly look at the applicability of the Machine Learning approach to other areas within Financial Services domain like Insurance Claims Fraud Detection, etc. NDC Conferences https://ndc-london.com https://ndcconferences.com
Views: 5946 NDC Conferences
Cluster analysis in Excel:Segmentation of Households by Banking Status
http://www.NeuroXL.com This demo shows an example of using NeuroXL Clusterizer in financial services marketing clustering of households by their banking status)
Views: 34864 NeuroXL
Machine Learning, News Analytics, and Stock Selection
Slides available ► https://goo.gl/Sb5RJu Full Event ► https://goo.gl/LvnmwY Yin Luo, Managing Director, Global Head of Quantitative Strategy, Deutsche Bank. Big data and machine learning have generated tremendous interest in empirical finance research. In this paper, we study a unique news analytics database provided by Ravenpack. We apply a suite of innovative machine learning algorithms, including adaBoost, spline regression, and other boosting/bagging techniques on both traditional and unstructured news data in predicting stock returns. We find news sentiment data adds significant incremental predictive power to our machine learning based global stock selection models. Session recorded June 16, 2016 at the RavenPack 4th Annual Research Conference, titled "Reshaping Finance with Alternative Data". Watch all sessions: ► https://goo.gl/3ij1Ev Visit us at ►https://www.ravenpack.com/ Follow RavenPack on Twitter ► https://twitter.com/RavenPack #RavenPack #finance #sentiment #newsanalytics #bigdata
Views: 7328 RavenPack
Data Science & Machine Learning - KNN Classification - DIY- 21 -of-50
Data Science & Machine Learning - KNN Classification - DIY- 21 -of-50 Do it yourself Tutorial by Bharati DW Consultancy cell: +1-562-646-6746 (Cell & Whatsapp) email: [email protected] website: http://bharaticonsultancy.in/ Google Drive- https://drive.google.com/open?id=0ByQlW_DfZdxHeVBtTXllR0ZNcEU K – Nearest Neighbors (K-NN) Get the data from Balance Scale Data Set. Attribute Information: Class Name: 3 (L, B, R) Left-Weight: 5 (1, 2, 3, 4, 5) Left-Distance: 5 (1, 2, 3, 4, 5) Right-Weight: 5 (1, 2, 3, 4, 5) Right-Distance: 5 (1, 2, 3, 4, 5) http://archive.ics.uci.edu/ml/datasets/Balance+Scale Citation Policy: If you publish material based on databases obtained from this repository, then, in your acknowledgements, please note the assistance you received by using this repository. This will help others to obtain the same data sets and replicate your experiments. We suggest the following pseudo-APA reference format for referring to this repository: Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. Here is a BiBTeX citation as well: @misc{Lichman:2013 , author = "M. Lichman", year = "2013", title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" } Data Science & Machine Learning - Getting Started - DIY- 1 -of-50 Data Science & Machine Learning - R Data Structures - DIY- 2 -of-50 Data Science & Machine Learning - R Data Structures - Factors - DIY- 3 -of-50 Data Science & Machine Learning - R Data Structures - List & Matrices - DIY- 4 -of-50 Data Science & Machine Learning - R Data Structures - Data Frames - DIY- 5 -of-50 Data Science & Machine Learning - Frequently used R commands - DIY- 6 -of-50 Data Science & Machine Learning - Frequently used R commands contd - DIY- 7 -of-50 Data Science & Machine Learning - Installing RStudio- DIY- 8 -of-50 Data Science & Machine Learning - R Data Visualization Basics - DIY- 9 -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(a) -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(b) -of-50 Data Science & Machine Learning - Multiple Linear Regression Model - DIY- 11 -of-50 Data Science & Machine Learning - Evaluate Model Performance - DIY- 12 -of-50 Data Science & Machine Learning - RMSE & R-Squared - DIY- 13 -of-50 Data Science & Machine Learning - Numeric Predictions using Regression Trees - DIY- 14 -of-50 Data Science & Machine Learning - Regression Decision Trees contd - DIY- 15 -of-50 Data Science & Machine Learning - Method Types in Regression Trees - DIY- 16 -of-50 Data Science & Machine Learning - Real Time Project 1 - DIY- 17 -of-50 Data Science & Machine Learning - KNN Classification - DIY- 21 -of-50 Machine learning, data science, R programming, Deep Learning, Regression, Neural Network, R Data Structures, Data Frame, RMSE & R-Squared, Regression Trees, Decision Trees, Real-time scenario, KNN
Competing on Analytics at Kaggle using R | Improving Machine Learning Skills with Real World Data
Provides example of using Kaggle to improve machine learning skills with real-world data. R file: https://goo.gl/Muek9H Related machine learning videos: https://goo.gl/WHHqWP R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 2721 Bharatendra Rai
6k:175 Business Intelligence -- Naive Bayes within XLMiner
In this video I explain how to create a Naive Bayes video in XLMiner. For a demonstration on how to create binned attributes, please see my other video: http://youtu.be/RtUXbIxzdQg
Views: 2858 Michael Rechenthin
Building a Real Time Fraud Prevention Engine Using Open Source Big Data: by Keesjan de Vries
Fraudsters attempt to pay for goods, flights, hotels – you name it – using stolen credit cards. This hurts both the trust of card holders and the business of vendors around the world. We built a Real-Time Fraud Prevention Engine using Open Source (Big Data) Software: Spark, Spark ML, H2O, Hive, Esper. In my talk I will highlight both the business and the technical challenges that we’ve faced and dealt with.
Views: 3625 Spark Summit