Search results “Forecast data mining”
Time Series Data Mining Forecasting with Weka
I am sorry for my poor english. I hope it helps you. when i take the data mining course, i had searched it but i couldnt. So i decided to share this video with you.
Views: 21953 Web Educator
Sales Forecasting with Excel and the SQL Server 2012 Data Mining Add-in Tutorial
Use the Excel Data Mining add-in for SQL 2012 Analysis Services. See how simple it is to build a predictive model that forecasts sales or other values based on historical data.
Views: 12532 Edward Kench
Forecast the Price of Gold with Excel and SQL Server - Data Mining Tutorial
Learn about data mining with SQL Server 2012 Analysis Services and Excel 2013, using historical gold pricing data, to predict future prices. To follow this tutorial, you should have SSAS and the Data Mining Add-in for Excel.
Views: 6251 Edward Kench
SSAS: Forecast Video Tutorial (Data Mining Table Analysis Tool)
In this tutorial we will learn how to use the Forecast Table Analysis Tool for Excel 2007. See the video transcript: http://msdn.microsoft.com/en-us/library/dd299423.aspx
Views: 18573 sqlserver
Using Data Mining in Forecasting Problems
In this presentation, Analytics 2012 keynote speaker, Tim Rey from Dow Chemical Company, shares methodologies for using data mining to get the most value out of time series data.
Views: 8742 SAS Software
Introduction to Data Mining in SQL Server Analysis Services
Data mining is one of the key hidden gems inside of Analysis Services but has traditionally had a steep learning curve. In this session, you'll learn how to create a data mining model to predict who is the best customer for you and learn how to use other algorithms to spend your marketing model wisely. You'll also see how to use Time Series analysis for budget and forecast prediction. Finally, you'll learn how to integrate data mining into your application through SSIS or custom coding.
Views: 7424 PASStv
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: 53760 Orange Data Mining
Advanced Data Mining with Weka (1.4: Looking at forecasts)
Advanced Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 4: Looking at forecasts http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/JyCK84 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 4484 WekaMOOC
Microsoft Data Mining Demo -- Forecasting
Microsoft Data Mining Demo -- Forecasting with SQL Server 2008 and Excel 2007
Views: 8171 MarkTabNet
How to create a useful forecast model using Data Mining
Watch our latest video and learn how to build a forecast model regarding the price of aluminium for six months.
Views: 22 EX METRIX
Forecasting with Neural Networks: Part A
What is a neural network, neural network terminology, and setting up a network for time series forecasting This video supports the textbook Practical Time Series Forecasting. http://www.forecastingbook.com http://www.galitshmueli.com
Views: 12872 Galit Shmueli
Import data from web to excel - Sports | Statistics | Weather forecast
Import data into dynamic tables in Excel and stay up to date with latest information without browsing, Run time data or statistics analysis, maybe it be Sports information, weather forecast or statistics,connect link once and enjoy forever, illustrated with multiple examples.
Views: 18032 Excel to Excel
Gretl Tutorial 6: Modeling and Forecasting Time Series Data
In this video we run a linear regression on a time series dataset with time trend and seasonality dummies. Then, we perform and evaluate the accuracy of an in-sample forecast, as well as perform an out-of-sample (i.e., into the future) forecast. TABLE OF CONTENTS: 00:00 Introduction 00:12 What we will do in this Video 00:40 Data 01:14 Glimpse Data in Excel 01:46 Load Data in Gretl 03:20 Plot Time Series 03:54 Create Additional Variables 04:38 Run Model with All Data 05:34 In-Sample Forecast 06:40 Evaluating Quality of In-Sample Forecast 10:37 Out-of-Sample Forecast
Views: 40879 dataminingincae
Excel Forecasting Seasonal Data
How to use the Excel Data Analysis Tool Pack to forecast seasonal data. This is a video from our course; Excel for Decision Making Under Uncertainty. Click here to find out more: www.myonlinetraininghub.com/excel-for-decision-making-course
Views: 19931 MyOnlineTrainingHub
Different Data Mining Approaches for Forecasting Use of Bike Sharing System
R Codes are available on below link: https://github.com/mayurkmane/ADM-Project-A12-Group Document related to this data mining study is available on below link: https://www.dropbox.com/s/r5qw4mofej23gbg/Group-A12%20ADM%20Project.pdf?dl=0 https://ie.linkedin.com/in/mayurkmane
Views: 104 Mayur Mane
Data Mining Excel 2010 2013 Modelo Forecast
Data Mining Excel 2010 2013 Modelo Forecast
Views: 152 AddKw
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
Views: 483078 Siraj Raval
Excel 2013 - Data Mining - Forecast What If Scenario
Apresentação de como usar a ferramenta de mineração de dados do Excel 2013 para previsão de Forecast. Para mais acesse: http://excelb2b.com/
Views: 354 ExcelB2B
Excel at Data Mining - Your First Predictions
In this video, Billy Decker of StatSlice Systems shows you how to start data mining in 5 minutes with the Microsoft Excel data mining add-in*. In this example, we will create a set of predictions for new customers using a Logistic Regression models based upon old customers. For the example, we will be using a tutorial spreadsheet that can be found on Codeplex at: https://dataminingaddins.codeplex.com/releases/view/87029 *This tutorial assumes that you have already installed the data mining add-in for Excel and configured the add-in to be pointed at an instance of SQL Server to which you have access rights.
Views: 20818 StatSlice Systems
Time Series In R | Time Series Forecasting | Time Series Analysis | Data Science Training | Edureka
( Data Science Training - https://www.edureka.co/data-science ) In this Edureka YouTube live session, we will show you how to use the Time Series Analysis in R to predict the future! Below are the topics we will cover in this live session: 1. Why Time Series Analysis? 2. What is Time Series Analysis? 3. When Not to use Time Series Analysis? 4. Components of Time Series Algorithm 5. Demo on Time Series
Views: 65305 edureka!
Forecasting Time Series Data in R | Facebook's Prophet Package 2017 & Tom Brady's Wikipedia data
An example of using Facebook's recently released open source package prophet including, - data scraped from Tom Brady's Wikipedia page - getting Wikipedia trend data - time series plot - handling missing data and log transform - forecasting with Facebook's prophet - prediction - plot of actual versus forecast data - breaking and plotting forecast into trend, weekly seasonality & yearly seasonality components prophet procedure is an additive regression model with following components: - a piecewise linear or logistic growth curve trend - a yearly seasonal component modeled using Fourier series - a weekly seasonal component forecasting is an important tool related to analyzing big data or working in data science field. 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: 17769 Bharatendra Rai
Movie Success Prediction Using Data Mining Project
Get the project at http://nevonprojects.com/movie-success-prediction-using-data-mining/ The system predicts the success of a movie by mining past movie success data through a prediction methodology and data mining algorithms
Views: 17591 Nevon Projects
Prediction Using Weka Tool- Machine Learning Tutorial
Weka is an Open source Machine Learning Application which helps to predict the required data as per the given parameters
Predicting Football Matches Using Data With Jordan Tigani - Strata Europe 2014
A keynote address from Strata + Hadoop World Europe 2014 in Barcelona, "Predictive Analytics in the Cloud: Predicting Football." Watch more from Strata Europe 2014: http://goo.gl/uqw6WS Visit the Strata website to learn more: http://strataconf.com/strataeu2014/ Subscribe for more from the conference! http://goo.gl/szEauh How can you turn raw data into predictions? How can you take advantage of both cloud scalability and state-of-the-art Open Source Software? This talk shows how we built a model that correctly predicted the outcome of 14 of 16 games in the World Cup using Google’s Cloud Platform and tools like iPython and StatsModels. I’ll also demonstrate new tools to integrate iPython with Google’s cloud and how you can use the same tools to make your own predictions. About Jordan Tigani (Google): Jordan Tigani has more than 15 years of professional software development experience, the last 4 of which have been spent building BigQuery. Prior to joining Google, Jordan worked at a number of star-crossed startups, where he learned to make data-based predictions. He is a co-author of Google BigQuery Analytics. When not analyzing soccer matches, he can often be found playing in one. Stay Connected to O'Reilly Media by Email - http://goo.gl/YZSWbO Follow O'Reilly Media: http://plus.google.com/+oreillymedia https://www.facebook.com/OReilly https://twitter.com/OReillyMedia
Views: 82429 O'Reilly
Data Mining in SQL Server Analysis Services
Presenter: Brian Knight
Views: 97184 PASStv
Data Mining Tools Market Size, Status and Forecast 2018 2025
Visit Here : http://bit.ly/2z2jXOh Data Mining Tools market size was million US$ and it is expected to reach million US$ by the end of 2025, with a CAGR of during 2018-2025. This report focuses on the global Data Mining Tools status, future forecast, growth opportunity, key market and key players. The study objectives are to present the Data Mining Tools development in United States, Europe and China.
SQL Server 2005 Data Mining Forecasting in Excel
SQL Server 2005 Data Mining Forecasting in Excel
Views: 6887 GarryEdser
Time Series Forecasting Theory | AR, MA, ARMA, ARIMA | Data Science
In this video you will learn the theory of Time Series Forecasting. You will what is univariate time series analysis, AR, MA, ARMA & ARIMA modelling and how to use these models to do forecast. This will also help you learn ARCH, Garch, ECM Model & Panel data models. For training, consulting or help Contact : [email protected] For Study Packs : 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: 328650 Analytics University
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: 13344 Packt Video
Views: 182 Bharat Nagalia
Constructing Predictive Model Using IBM SPSS Modeler
This tutorial shows how to construct a predictive model using IBM SPSS Modeler. We use the Boston Housing dataset for our illustration. In addition, we also discuss how to evaluate the performance of the model using different nodes such as Graph Evaluation and Data Analysis Node. I hope you enjoy it and please let me know if you have any questions. Thanks for watching.
Views: 16615 IT_CHANNEL
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: 13401 Growth Tribe
Linear Regression - Machine Learning Fun and Easy
Linear Regression - Machine Learning Fun and Easy https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML Hi and welcome to a new lecture in the Fun and Easy Machine Learning Series. Today I’ll be talking about Linear Regression. We show you also how implement a linear regression in excel Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. Dependent Variable – Variable who’s values we want to explain or forecast Independent or explanatory Variable that Explains the other variable. Values are independent. Dependent variable can be denoted as y, so imagine a child always asking y is he dependent on his parents. And then you can imagine the X as your ex boyfriend/girlfriend who is independent because they don’t need or depend on you. A good way to remember it. Anyways Used for 2 Applications To Establish if there is a relation between 2 variables or see if there is statistically signification relationship between the two variables- • To see how increase in sin tax has an effect on how many cigarettes packs are consumed • Sleep hours vs test scores • Experience vs Salary • Pokemon vs Urban Density • House floor area vs House price Forecast new observations – Can use what we know to forecast unobserved values Here are some other examples of ways that linear regression can be applied. • So say the sales of ROI of Fidget spinners over time. • Stock price over time • Predict price of Bitcoin over time. Linear Regression is also known as the line of best fit The line of best fit can be represented by the linear equation y = a + bx or y = mx + b or y = b0+b1x You most likely learnt this in school. So b is is the intercept, if you increase this variable, your intercept moves up or down along the y axis. M is your slope or gradient, if you change this, then your line rotates along the intercept. Data is actually a series of x and y observations as shown on this scatter plot. They do not follow a straight line however they do follow a linear pattern hence the term linear regression Assuming we already have the best fit line, We can calculate the error term Epsilon. Also known as the Residual. And this is the term that we would like to minimize along all the points in the data series. So say if we have our linear equation but also represented in statisitical notation. The residual fit in to our equation as shown y = b0+b1x + e To learn more on Augmented Reality, IoT, Machine Learning FPGAs, Arduinos, PCB Design and Image Processing then Check out http://www.arduinostartups.com/ Please like and Subscribe for more videos :) -------------------------------------------------- Support us on Patreon http://bit.ly/PatreonArduinoStartups --------------------------------------------------
Views: 95265 Augmented Startups
Analytical Reporting Dashboard for Weather Data Mining Whitepaper- Part 1
Analytical Reporting Dashboard for Weather Data Mining Whitepaper Part 1- Summarizing the Data for Ready Analysis Finding and using reliable weather forecasts is becoming a key competitive advantage in many industries. To add to the complexity, the sheer number of forecasting services and their variability in the accuracy of their forecasts by geography, forecast timeframe and weather parameter makes it hard for many companies to choose the correct forecast. Decision-makers need an easy-to-use, actionable summary of the massive weather forecast and observation data This paper • This paper is part 1 of a two part series on building and using an analytical framework for determining forecast accuracy. • It focuses on issues related to getting the data ready for analysis in an analysis reporting tool such as Tableau • How MineWeather solves these problems and makes it easy for the users to summarize the data.
Views: 121 Macrosoft Inc
YouTube Data API - Data Mining #2
Data mining YouTube using youtube.search.list and youtube.videos.list to forecast the senate races of 2014. And quantifying our probability using 2012 senate races data and stats from YouTube during the same period. Github/NBViewer Link: http://nbviewer.ipython.org/github/twistedhardware/mltutorial/blob/master/notebooks/data-mining/2.%20YouTube%20Data.ipynb
Views: 6273 Roshan
Data Mining in MS-Excel: Credit Card Default Prediction
SOLUTION LINK: http://libraay.com/downloads/data-mining-in-ms-excel-credit-card-default-prediction/ Data Set Information: The training data contains 22500 observations with the predictor variables as well as the response variable. The test set contains 7500 observations with the response variable removed. Task: Predict the response variable (default status) for the test data. IMPORTANT: Please include the variable "ID" in the prediction, so that model accuracy can be evaluated. Variable descriptions: This research employed a binary variable, default payment (Yes = 1, No = 0), as the response variable. This study reviewed the literature and used the following 23 variables as explanatory variables: X1: Amount of the given credit (NT dollar): it includes both the individual consumer credit and his/her family (supplementary) credit. X2: Gender (1 = male; 2 = female). X3: Education (1 = graduate school; 2 = university; 3 = high school; 4 = others). X4: Marital status (1 = married; 2 = single; 3 = others). X5: Age (year). X6 - X11: History of past payment. We tracked the past monthly payment records (from April to September, 2005) as follows: X6 = the repayment status in September, 2005; X7 = the repayment status in August, 2005; . . .;X11 = the repayment status in April, 2005. The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months; . . .; 8 = payment delay for eight months; 9 = payment delay for nine months and above. X12-X17: Amount of bill statement (NT dollar). X12 = amount of bill statement in September, 2005; X13 = amount of bill statement in August, 2005; . . .; X17 = amount of bill statement in April, 2005. X18-X23: Amount of previous payment (NT dollar). X18 = amount paid in September, 2005; X19 = amount paid in August, 2005; . . .;X23 = amount paid in April, 2005.
Views: 55 Libraay Downloads
A Deep Hybrid Model for Weather Forecasting
Authors: Aditya Grover, Ashish Kapoor, Eric Horvitz Abstract: Weather forecasting is a canonical predictive challenge that has depended primarily on model-based methods. We explore new directions with forecasting weather as a data-intensive challenge that involves inferences across space and time. We study specifically the power of making predictions via a hybrid approach that combines discriminatively trained predictive models with a deep neural network that models the joint statistics of a set of weather-related variables. We show how the base model can be enhanced with spatial interpolation that uses learned long-range spatial dependencies. We also derive an efficient learning and inference procedure that allows for large scale optimization of the model parameters. We evaluate the methods with experiments on real-world meteorological data that highlight the promise of the approach. ACM DL: http://dl.acm.org/citation.cfm?id=2783275 DOI: http://dx.doi.org/10.1145/2783258.2783275
How to Use Time Series Data to Forecast
This is Lecture series on Time Series Analysis Chapter of Statistics. In this part, you will learn use of time series data for forecasting. Watch all statistics videos at http://svtuition.com/watch/#ST
Views: 2028 Svtuition
Forecasting - Linear regression - Example 1 - Part 1
In this video, you will learn how to find the demand forecast using linear regression.
Views: 59799 maxus knowledge
Data mining tutorial for beginners FREE Training 01
Published on Aug 2, 2014 1 intro data mining and scraping next tutorial here: http://youtu.be/gb4ufqFkT7A please comment below if you have any questions. Tq Category Education License Standard YouTube License
Views: 105444 Red Team Cyber Security
Views: 18403 Lucita287
Big Data: Mining Football Statistics
Data Mining Final Project for Big Data INSY 4970 at Auburn University
Views: 31707 wwl0002
Excel - Time Series Forecasting - Part 1 of 3
Part 2: http://www.youtube.com/watch?v=5C012eMSeIU&feature=youtu.be Part 3: http://www.youtube.com/watch?v=kcfiu-f88JQ&feature=youtu.be This is Part 1 of a 3 part "Time Series Forecasting in Excel" video lecture. Be sure to watch Parts 2 and 3 upon completing Part 1. The links for 2 and 3 are in the video as well as above.
Views: 755771 Jalayer Academy
MATHS & STATISTICS | Data mining tutorial from John Elder (1)
Top tips for data mining success! Watch John Elder present this short tutorial on how to get ahead in data mining. This is extracted from training material produced by Elder Research, Inc. For more information about statistical analysis and data mining, check out the brand new reference book from Elsevier: The Handbook of Statistical Analysis and Data Mining Applications (www.elsevierdirect.com/datamining).
Views: 33047 Elsevier Books
Stock Price Prediction | AI in Finance
Can AI be used in the financial sector? Of course! In fact, finance was one of the pioneering industries that started using AI in the early 80s for market prediction. Since then, major financial firms and hedge funds have adopted AI technologies for everything from portfolio optimization, to credit lending, to stock betting. In this video, we'll go over all the different ways AI can be used in applied finance, then build a stock price prediction algorithm in python using Keras and Tensorflow. Code for this video: https://github.com/llSourcell/AI_in_Finance 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://hackernoon.com/unsupervised-machine-learning-for-fun-profit-with-basket-clusters-17a1161e7aa1 https://www.datacamp.com/community/tutorials/finance-python-trading http://www.cuelogic.com/blog/python-in-finance-analytics-artificial-intelligence/ https://www.udacity.com/course/machine-learning-for-trading--ud501 https://www.oreilly.com/learning/algorithmic-trading-in-less-than-100-lines-of-python-code 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
Views: 104776 Siraj Raval
Machine Learning with Small Data Sets in the Age of Deep Learning
Dr. Lei Tang and Dr. Xin Xu will talk about how they apply machine learning with small data sets in sales management and forecast. The recent successes of machine learning and deep learning can be largely attributed to three factors: emergence of abundant data, development of innovative algorithms, and availability of machine learning tools and computing resources. Unfortunately, not all application spaces provide data sets large enough to be used in the usual or obvious ways. In this talk, Lei and Xin focus on one specific domain, enterprise sales, where data is often limited in volume, always noisy, and constantly evolving. They describe how machine learning, and in particular deep learning, can help, and how we address the data challenges described. They specifically discuss how to select model architectures appropriate for these limited data situations, for example, how deep our networks should be. By sifting through sales records and associated sales activities Lei and Xin enable identification of at-risk opportunities as well as project and estimated the time required to close each deal. This, in turn, contributes to the generation of a reliable business forecast for sales managers and executives. Lessons and findings learned through the process is shared. Speaker Bios: Dr. Lei Tang is the Chief Data Scientist at Clari Inc., a startup backed by Sequoia Capital and Bain Capital ventures, focusing on predictive analytics for sales execution and forecasting. Lei received his Ph.D. in computer science from Arizona State University in 2010, and B.S. from Fudan University, China. He is passionate about reshaping variety of businesses, driving business growth and decision through data science and machine learning. From 2012-2014, Lei was the lead data scientist at Demand Generation of @WalmartLabs, where he worked closely with marketing team to drive traffic to site, impacting hundreds of revenue each year. Before that, Lei had 2-year stint at advertising sciences in Yahoo! Labs, working on targeting, user profiling/segmentation by mining user behavioral, social and content information. Lei has co-authored one book on “community detection and mining in social media” (top-download in the corresponding data mining lecture series), held 4 patents, published over 30 papers at top-notch conferences and journals on data mining/machine learning, with over 4000 citations. Dr. Xin Xu is currently working as a data scientist in Clari. Before this, She received her Ph.D degree in Computer Engineering from North Carolina State University in 2015. She also did summer intern in Bell Labs and Akamai Technology in 2014 and 2015 respectively. Her current research interest mainly focuses on applying data mining, machine learning and advanced analytics to solve practical problems in sales domain.

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