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: 32627 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: 13808 Ben Rodick
Data mining on Banking Industry
Data mining in the Banking industry
Views: 63 prazan kayestha
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! Learn It Up! Summer’s Hottest Learning Sale Is Here! Pick Any Sun-sational Course & Get Other Absolutely FREE! Link: http://bit.ly/summer-bogo-2019 You can access the source code of this tutorial here: https://github.com/eduonix/creditcardML You can even check FREE course on Predict Board Game Reviews with Machine Learning on http://bit.ly/2Wm2uKW 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
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: 32040 Packt Video
Stock market prediction | Data Mining Project
Stock Market prediction using Machine Learning Algorithm. By: Devansh Chauhan Kartik Jain
Views: 900 Devansh Chauhan
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: 58057 edureka!
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: 417 Royston Denzil
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: 17903 Neo4j
Credit Card Fraud Detection System using Genetic Algorithm
Credit Card Fraud Detection System using Genetic Algorithm To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com Due to the rise and rapid growth of E-Commerce, use of credit cards for online purchases has dramatically increased and it caused an explosion in the credit card fraud. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In real life, fraudulent transactions are scattered with genuine transactions and simple pattern matching techniques are not often sufficient to detect those frauds accurately. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. Many modern techniques based on Artificial Intelligence, Data mining, Fuzzy logic, Machine learning, Sequence Alignment, Genetic Programming etc., has evolved in detecting various credit card fraudulent transactions. A clear understanding on all these approaches will certainly lead to an efficient credit card fraud detection system. This paper presents a survey of various techniques used in credit card fraud detection mechanisms and evaluates each methodology based on certain design criteria.
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: 23521 Nevon Projects
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: 34765 DataCamp
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: 588793 Siraj Raval
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. There is a minor error at 1:47: Points 5 and 6 are not in the right location 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/ ...or just donating to StatQuest! https://www.paypal.me/statquest
K - Nearest Neighbors - KNN Fun and Easy Machine Learning
K - Nearest Neighbors - KNN Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS Course - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML In pattern recognition, the KNN algorithm is a method for classifying objects based on closest training examples in the feature space. KNN is a type of instance-based learning, or lazy learning where the function is only approximated locally and all computation is delayed until classification. The KNN is the fundamental and simplest classification technique when there is little or no prior knowledge about the distribution of the data. The K in KNN refers to number of nearest neighbors that the classifier will use to make its predication. In this video we use Game of Thrones example to explain kNN. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 49760 Augmented Startups
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: 362063 caltech
K-Means Clustering Algorithm - Cluster Analysis | Machine Learning Algorithm | Data Science |Edureka
( Data Science Training - https://www.edureka.co/data-science ) This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4 Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #kmeans #clusteranalysis #clustering #datascience #machinelearning How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 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. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 72805 edureka!
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: 71406 Orange Data Mining
Beating Banking Fraud with NetGuardians’ Machine Learning Risk Platform
If you want to learn more about banking fraud prevention please visit our website: https://netguardians.ch/digital-banking-fraud/
Views: 1011 NetGuardians
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: 2352 Cambridge Spark
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: 105685 Siraj Raval
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: 35049 NeuroXL
10 Data Science Projects in the Retail Industry
In this video you will learn about 10 data science projects that you can do in the retail industry. Retail industry is leveraging Big Data analytics to serve customer better. Here are the list of projects 1- Repeat Purchase - Predicting chances of repurchase 2- Cross Sell - Selling an additional products to an existing customer 3- Personalized Recommendation - 4- Pricing products 5- Loyalty analysis 6-Campaign Analysis 7- Market Basket Analysis 8- A/B testing 9-CTR rate prediction 10-Segmentation Analysis You can get data sets for such projects on Kaggle, Data.Gov and UCI ML data repository ANalytics Study Pack : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 4115 Analytics University
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: 3407 Galit Shmueli
Using Machine Learning for Predicting NFL Games | Data Dialogs 2016
You are a HUGE football fan. Every week you pick winners in an NFL pick-em' league. Somehow, all that fan experience doesn't translate into consistently winning your league. Perhaps you need a more systematic approach that takes some of the emotion out of it. Where to start? Betting spreads provide a consistent and robust mechanism for encapsulating the variables and predicting outcomes of NFL games. In a weekly confidence pool, spreads also perform very well as opposed to intuition-based guessing and "knowledge" from years of being a fan. Can we do better? In this talk, we will discuss an approach to use machine learning algorithms to make improvements on the spread method of ranking winners on a weekly basis as an exercise in winning your friendly neighborhood confidence pool. https://datadialogs.ischool.berkeley.edu/2016/schedule/using-machine-learning-predicting-nfl-games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amit Bhattacharyya Senior Data Scientist Teachers Pay Teachers Amit is the Senior Data Scientist at Teachers Pay Teachers, an online marketplace for teachers to buy, sell and share original educational resources. At TpT, Amit works on developing both technical and modeling infrastructure to analyze customer behavior and ways to more effectively connect buyers and sellers. Amit also teaches in the MIDS program at the UC Berkeley School of Information. He received a Ph.D. in physics from Indiana Universtiy. Previously, he did a two-year stint in advertising, and worked as a quantitative analyst at various banks and hedge funds for twelve years. In his spare time, he likes to plan skiing and backpacking trips, and dabble with machine learning algorithms for fantasy football.
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: 72276 TinMan Systems
Train, Evaluate, Repeat: Building a Credit Card Fraud Detection System - Leela Senthil Nathan
PyData LA 2018 This talk covers three major ML problems Stripe faced (and solved!) in building its credit card fraud detection system: choosing labels for fraud that work across all merchants, addressing class imbalance (legitimate charges greatly outnumber fraudulent ones), and performing counterfactual evaluation (to measure performance and obtain training data when the ML system is changing outcomes itself). --- www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 1306 PyData
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: 166 GP Pulipaka
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: 823 H2O.ai
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
Weka Data Mining Tutorial for First Time & Beginner Users
23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 471477 Brandon Weinberg
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: 8987 DEFCONConference
Data Mining Software in Healthcare
This is a brief discussion of data mining software with an emphasis on the healthcare field.
Views: 4302 Joshua White
Data Mining Assignment USF ISM 6136
Data Mining Assignment USF ISM 6136 Data Mining Opportunity In this video we are going to show how to help the guys and girls at the front to get out of Cold Call Hell. This is a dataset from one bank in the United States. Besides usual services, this bank also provides car insurance services. The bank organizes regular campaigns to attract new clients. The bank has potential customers’ data, and bank’s employees contact customers to advertise available car insurance options. The task is to generate a prioritized list of customers sorted by which customers are more likely to buy car insurance.
Views: 27 Sonny Alberdeston
Using Data Mining Technique To Dignosis Heart Disease
Contact - 08975313145
Views: 213 Codeengine
Soil Classification Using Data Mining Techniques: A Comparative Study | Final Year Projects 2016
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 200 Clickmyproject
Applied Machine Learning - Credit Card Fraud Detection Problem
BIA 780 Applications of Artificial Intelligence Final Project Presentation
Views: 10014 Joshua Krause
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: 16994 KNIMETV
Predictive Data Analytics in UNDER 5 Minutes
For NeuroSolutions Infinity software, visit: http://www.neurosolutions.com/infinity/ Download the FREE Trial: http://www.neurosolutions.com/downloads/ What is Predictive Data Analytics? Learn in under 5 minutes. This video is an introduction to Predictive Data Analytics development methodology. By the end of this video, you'll understand the core concepts of predictive data analytics. You'll be able to get started implementing it into your own custom software solutions.
Views: 52705 NeuroDimension
Machine Learning Interview Questions And Answers | Data Science Interview Questions | Simplilearn
This Machine Learning Interview Questions And Answers video will help you prepare for Data Science and Machine learning interviews. This video is ideal for both beginners as well as professionals who are appearing for Machine Learning or Data Science interviews. Learn what are the most important Machine Learning interview questions and answers and know what will set you apart in the interview process. Some of the important Machine Learning Interview Questions are listed below: 1. What are the different types of Machine Learning? 2. What is overfitting? And how can you avoid it? 3. What is false positive and false negative and how are they significant? 4. What are the three stages to build a model in Machine Learning? 5. What is Deep Learning? 6. What are the differences between Machine Learning and Deep Learning? 7. What are the applications of supervised Machine Learning in modern businesses? 8. What is semi-supervised Machine Learning? 9. What are the unsupervised Machine Learning techniques? 10. What is the difference between supervised and unsupervised Machine Learning? 11. What is the difference between inductive Machine Learning and deductive Machine Learning? 12. What is 'naive' in the Naive Bayes classifier? 13. What are Support Vector Machines? 14. How is Amazon able to recommend other things to buy? How does it work? 15. When will you use classification over regression? 16. How will you design an email spam filter? 17. What is Random Forest? 18. What is bias and variance in a Machine Learning model? 19. What’s the trade-off between bias and variance? 20. What is pruning in decision trees and how is it done? Subscribe to our channel for more Machine Learning Tutorials: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Machine Learning Articles: https://www.simplilearn.com/what-is-artificial-intelligence-and-why-ai-certification-article?utm_campaign=Machine-Learning-interview-Questions-and-answers-hB1CTizqGFk&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Machine-Learning-interview-Questions-and-answers-hB1CTizqGFk&utm_medium=Tutorials&utm_source=youtube You can also go through the Slides here: https://goo.gl/rmzjaQ #MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse - - - - - - - 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. - - - - - - What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems - - - - - - - Who should take this Machine Learning Training Course? 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 - - - - - - For more updates on courses and tips follow us on: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 51751 Simplilearn
DrivingStyles: A Mobile Platform for Driving Styles and Fuel Consumption Characterization
DrivingStyles: A Mobile Platform for Driving Styles and Fuel Consumption Characterization To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org Intelligent transportation systems (ITS) rely on connected vehicle applications to address real-world problems. Research is currently being conducted to support safety, mobility and environmental applications. This paper presents the DrivingStyles architecture, which adopts data mining techniques and neural networks to analyze and generate a classification of driving styles and fuel consumption based on driver characterization. In particular, we have implemented an algorithm that is able to characterize the degree of aggressiveness of each driver. We have also developed a methodology to calculate, in real-time, the consumption and environmental impact of spark ignition and diesel vehicles from a set of variables obtained from the vehicle’s electronic control unit (ECU). In this paper, we demonstrate the impact of the driving style on fuel consumption, as well as its correlation with the greenhouse gas emissions generated by each vehicle. Overall, our platform is able to assist drivers in correcting their bad driving habits, while offering helpful tips to improve fuel economy and driving safety.
Final Year Projects 2015 | Soil Classification Using  Data Mining  Techniques
Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-778-1155 Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 643 myproject bazaar
Data Science - Hype Vs Truth | Machine Learning vs AI | Big Data Analytics | Data Science Jobs
This video is a webinar recording of Rajib Layek [ Business Analyst Expert ] talking about Data Science Hype vs Reality, Industrial Revolution 4.0, Data Science Application, Machine Learning vs AI, What is Big Data, Big Data Analysis Pipeline, Fraud Detection Case Study, Data Science Jobs , Data Science Roles,Data Science Companies If you dream to have a career in data science, you can trust us to help you realize your dream. Check out our PG Diploma in Data Science program - Now with placement guarantee http://bit.ly/2W7G5kk About the program Manipal ProLearn’s PG Diploma in Data Science program is designed to provide you with a broad understanding of the basic and advanced concepts of Data Science. The Data Science training will enable you to implement Big Data techniques using tools using R, Excel, Tableau, SQL, NoSQL, Hadoop, Pig, Hive, Apache Spark and Storm. After completing the Data Science diploma, you’ll be considered as a strong and competent data scientist. The course will help you to: Perform data analysis, modelling, predictive analysis, and storytelling through data visualization, which is crucial to business decision-making. Analyze data sets to summarize their main characteristics, often with visual methods with Exploratory Data Analysis Understand and use Big Data technologies as enablers to deploy enterprise information management and solve business problems Learn artificial intelligence and neural network that emphasizes the creation of intelligent machines. Apply the methods, tools and techniques to real-world problems by leveraging technologies such as R, Python, Excel, SQL, NoSQL, Tableau, Hadoop, Pig, Hive, Apache. Spark and Storm, and other open source and proprietary products as well. Communicate analytics problems, methods, and findings effectively verbally, visually, and in writing. Become more accurate in predicting outcomes without Machine Learning. Cleaning and unify messy and complex data set with Data Scrapping and Data Wrangling Help Companies make critical decisions through analysis, modeling, visualization, etc. Learn the emerging data science of unstructured data analysis and robotic process automation by choosing an elective based on your area of interest. Term 1: Programming for Data Science Data Scrapping and Data Wrangling Statistical techniques for Data Science Machine Learning Data Analysis and Visualisation Big Data Technologies Term 2: Artificial Intelligence Elective 1 (Banking Analytics or Marketing Analytics) Elective 2 (Unstructured Data Analysis/Robotic Process Automation) Project/Internship Manipal Placement Guarantee Manipal ProLearn promises that every student successfully completing the academic requirements of the PG Diploma in Data Science from Manipal Academy of Higher Education (MAHE) and conforming to the program’s disciplinary norms will be placed by the end of the program. A student is considered to be placed when he/she receives an offer letter for a paid position from a company. ---------------------------------------------------------------------------------------------------------------------------------- Subscribe to our channel to get video updates. Hit the subscribe button above https://www.youtube.com/channel/UCllnb6S5fPzpVYcV8KYzhnA?view_as=subscriber?sub_confirmation=1 Also follow us on other channels: Facebook: https://www.facebook.com/manipalprolearn/ Twitter: https://twitter.com/manipalprolearn LinkedIn: https://www.linkedin.com/company/manipal-prolearn/
Views: 891 Manipal ProLearn
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: 6513 NDC Conferences
Use of Hybrid Fuzzy Neural Network for Advanced Steganography
In today's world steganography is very important due to the confidential communication between computer users over the internet. Steganography is known as the application of non-visible transmission which is accomplished by cloaking the presence of any communication. Generally, data embedding is attained in various multimedia formats like image, text, audio,and is heavily used in army for transmitting sensitive information, copyright, attestation and various other government sectors and general purpose. In the steganography technique, covert communication is done by embedding some information inside the cover media a.k.a. carrier and produce a stego-image i.e. output image that consists of concealed secret information. The analysis of various steganographictechniquesandfurtherprocessingofthestego- imageisdoneusingtheHybridFuzzyNeuralNetwork is used in the system. The pixels where secret message is to be embedded is selected by using the AES encryption algorithm.Thehybridfuzzyneuralnetworkisusedtohandletheresultantstego-imagequalitywhichcanbedegraded due to the embedding of secret message. This method can achieve better embedding capacity with excellent stego- image quality and high security of secret message confidentiality. Also, analysis of Steganography, its main types, their classification and applications are reviewed. Sai Infocorp Solution Pvt. Ltd. Ph. 0253-6644344 Mobile : +91 - 9096813348 http://www.saiinfosolution.co.in/ Nashik Office: 2nd Floor, Yashomandir Avenue, Opp.Jankalyan Bank, Patil Lane No. 1, College Road, Nashik 422005. Click the link to get location on google map https://www.google.co.in/maps/dir//Sai+Info+Solution+Pvt.+Ltd. Pune Office: Om Chambers, Office No. 307, Above Hotel Panchali, Opp. Jangali Maharaj Mandir, J.M Road, Pune 411005. ph:02030107071 Click the link to get location on google map https://www.google.co.in/maps/dir//Sai+Infocorp+Solution+Pvt.+Ltd.
Application of Genetic algorithms for Neural Network Learning in CSD - Srdjan Mladjenovic
Neural network are systems modeled on the human brain which consist of number of neurons and connections between them. The neural networks weights are that what makes memory possible, i.e. acquiring certain knowledge, and they are modified through iterative learning process.In the process of learning, weight modifications are done by a learning algorithm and back-propagation (gradient descent) is the most famous one. However, the final result of back-propagation training is significantly dependent on initial weight values. Genetic algorithm is a stochastic search tool based on evolutive principles, which can be used as a learning algorithm without limitations. The scope of genetically trained networks is examined through the problem of credit risk assessment in banking, the research area known as credit scoring. Compared to back-propagation algorithms, experimental results on well known benchmark problems in this area (Australian and German credit data), show certain advantages of the genetic learning networks.
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: 191556 MATLAB
Disease Prediction by Machine Learning over Big Data from Healthcare Communities
Disease Prediction by Machine Learning over Big Data from Healthcare Communities To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org With big data growth in biomedical and healthcare communities, accurate analysis of medical data benefits early disease detection, patient care and community services. However, the analysis accuracy is reduced when the quality of medical data is incomplete. Moreover, different regions exhibit unique characteristics of certain regional diseases, which may weaken the prediction of disease outbreaks. In this paper, we streamline machine learning algorithms for effective prediction of chronic disease outbreak in disease-frequent communities. We experiment the modified prediction models over real-life hospital data collected from central China in 2013-2015. To overcome the difficulty of incomplete data, we use a latent factor model to reconstruct the missing data. We experiment on a regional chronic disease of cerebral infarction. We propose a new convolutional neural network based multimodal disease risk prediction (CNN-MDRP) algorithm using structured and unstructured data from hospital. To the best of our knowledge, none of the existing work focused on both data types in the area of medical big data analytics. Compared to several typical prediction algorithms, the prediction accuracy of our proposed algorithm reaches 94.8% with a convergence speed which is faster than that of the CNN-based unimodal disease risk prediction (CNN-UDRP) algorithm.
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: 2925 Michael Rechenthin