What is PRICE OPTIMIZATION? What does PRICE OPTIMIZATION mean? PRICE OPTIMIZATION meaning - PRICE OPTIMIZATION definition - PRICE OPTIMIZATION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Price optimization is the use of mathematical analysis by a company to determine how customers will respond to different prices for its products and services through different channels. It is also used to determine the prices that the company determines will best meet its objectives such as maximizing operating profit. The data used in price optimization includes operating costs, inventories and historic prices and sales. Price optimization practice has been implemented in industries including retail, banking, airlines, casinos, hotels, car rental, cruise lines and insurance industries. Price optimization utilizes analysis of big data to predict the behavior of potential buyers to different prices. Companies use price optimization models to determine pricing structures for initial pricing, promotional pricing and discount pricing. Price optimization uses calculations to visualize how demand varies at different price points and combines that data with cost and inventory levels to develop a profitable price point for that product or service. This model is also used to evaluate pricing for different customer segments by simulating how targeted customers will respond to price changes with data-driven scenarios. Price optimization starts with a segmentation of customers. A seller then estimates how customers in different segments will respond to different prices offered through different channels. Given this information, determining the prices that best meet corporate goals can be formulated and solved as a constrained optimization process. The form of the optimization is determined by the underlying structure of the pricing problem. If capacity is constrained and perishable and customer willingness-to-pay increases over time, then the underlying problem is classified as a yield management or revenue management problem. If capacity is constrained and perishable and customer willingness-to-pay decreases over time, then the underlying problem is one of markdown management. If capacity is not constrained and prices cannot be tailored to the characteristics of a particular customer, then the problem is one of list-pricing. If prices can be tailored to the characteristics of an arriving customer then the underlying problem is sometimes called customized pricing. Pricing and Revenue Optimization written by Dr. Robert L. Phillips discusses the economics behind pricing optimization, how it is used as a corporate process, its relationship to supply constraints and how it is perceived by the consumer. In the book, pricing optimization is recognized as an important application for quantitative analysis and there is increased interest in learning its techniques among different industries. Manfred Krafft and Murali K. Mantrala discuss the use of price optimization software in the retail industry and the paradigm shift from price optimization to pricing process improvement in their book Retailing in the 21st Century: Current and Future Trends. The book mentions that the research conducted on price optimization by its traditional definition is not applicable to the retail industry, thus they recommend retailers adopt a process view of pricing. In 2009, the NAW Institute for Distribution Excellence and Texas A&M University’s Industrial Distribution Program conducted a research study titled Pricing Optimization: Striking the Right Balance for Margin Advantage which investigated price optimization and best practices in wholesale distribution. The study recommended wholesalers practice complexity management to provide structure and consistency with regards to pricing in order to improve margins.
Views: 1034 The Audiopedia
In this video you will learn how to optimally determine price of competing products that maximizes profit CONTACT [email protected] Find all free videos & study packs available with us here: http://analyticuniversity.com
Views: 5662 Analytics University
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: 18238 Growth Tribe
Pricing Decision Analytics | Ryan Air Case Study. The above video is from Jigsaw Academy - Analytics for Leaders course- http://www.jigsawacademy.com/analytics-for-leaders. Jigsaw Academy is an award winning premier online analytics training institute that aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training to develop business-ready professionals Follow us on: https://www.facebook.com/jigsawacademy https://twitter.com/jigsawacademy http://jigsawacademy.com/
Views: 6465 Jigsaw Academy
Solving Optimization Models with Analytic Solver Platform
Views: 8213 Sam Burer
For the full presentation contact [email protected] Broadscale Predictive Modeling Optimization in Marketing and Retail was presented by Felipe Fernandez at the 2012 Salford Analytics and Data Mining Conference (ADMC) in San Diego, CA.
Views: 486 Salford Systems
Support Vector Machine (SVM) - 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 ►MACHINE LEARNING COURSES -http://augmentedstartups.info/machine-learning-courses ------------------------------------------------------------------------ A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. To understand SVM’s a bit better, Lets first take a look at why they are called support vector machines. So say we got some sample data over here of features that classify whether a observed picture is a dog or a cat, so we can for example look at snout length or and ear geometry if we assume that dogs generally have longer snouts and cat have much more pointy ear shapes. So how do we decide where to draw our decision boundary? Well we can draw it over here or here or like this. Any of these would be fine, but what would be the best? If we do not have the optimal decision boundary we could incorrectly mis-classify a dog with a cat. So if we draw an arbitrary separation line and we use intuition to draw it somewhere between this data point for the dog class and this data point of the cat class. These points are known as support Vectors – Which are defined as data points that the margin pushes up against or points that are closest to the opposing class. So the algorithm basically implies that only support vector are important whereas other training examples are ‘ignorable’. An example of this is so that if you have our case of a dog that looks like a cat or cat that is groomed like a dog, we want our classifier to look at these extremes and set our margins based on these support vectors. ------------------------------------------------------------ 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: 174727 Augmented Startups
kNN, k Nearest Neighbors Machine Learning Algorithm tutorial. Follow this link for an entire Intro course on Machine Learning using R, did I mention it's FREE: https://www.youtube.com/playlist?list=PLjPbBibKHH18I0mDb_H4uP3egypHIsvMn Also, be sure to check out my channel for over 300 tutorials on Excel, R, Statistics, basic Math, and more.
Views: 66860 Jalayer Academy
Speaker: Mao Ting Description By segmenting customers into groups with distinct patterns, businesses can target them more effectively with customized marketing and product features. I'll dive into a few machine learning and statistical techniques to extract insights from customer data, and demonstrate how to execute them on real data using Python and open-source libraries. Abstract I will go through clustering and decision tree analysis using sciki-learn and two-sample t test using scipy. We will learn the intuition for each technique, the math behind them, and how to implement them and evaluate the results using Python. I will be using open-source data for the demonstration, and show what insights you can extract from actual data using these techniques. Event Page: https://pycon.sg Produced by Engineers.SG Help us caption & translate this video! http://amara.org/v/P6SD/
Views: 16778 Engineers.SG
( 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: 66678 edureka!
This is the sixth and final part of short presentations on price optimization. Each part is about 10-12 minutes long. What you will learn is how with optimized prices, and the processes necessary for price optimization, companies can fight commoditization, downward pricing pressure and increase both sales and profits. This part covers: + How data mining and regression analysis of your sales data will discover underperforming areas where profits and sales leaks out.
Views: 266 AtengaInc
SQL Server Integration Services (SSIS) can be used to apply Data Mining predictions. This tutorial demonstrates how to use the SSIS "Data Mining Query" to predictive the risk of having a vehicle using profile information stored in a SQL Server table. I also have a comprehensive 60 minute T-SQL course available at Udemy : https://www.udemy.com/t-sql-for-data-analysts/?couponCode=ANALYTICS50%25OFF
Views: 7764 Steve Fox
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 Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 159114 Siraj Raval
Random Forest - 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 ►MACHINE LEARNING COURSES -http://augmentedstartups.info/machine-learning-courses ------------------------------------------------------------------------ Hey Guys, and welcome to another Fun and Easy Machine Learning Algorithm on Random Forests. Random forest algorithm is a one of the most popular and most powerful supervised Machine Learning algorithm in Machine Learning that is capable of performing both regression and classification tasks. As the name suggest, this algorithm creates the forest with a number of decision trees. In general, the more trees in the forest the more robust the prediction. In the same way in the random forest classifier, the higher the number of trees in the forest gives the high accuracy results. To model multiple decision trees to create the forest you are not going to use the same method of constructing the decision with information gain or gini index approach, amongst other algorithms. If you are not aware of the concepts of decision tree classifier, Please check out my lecture here on Decision Tree CART for Machine learning. You will need to know how the decision tree classifier works before you can learn the working nature of the random forest algorithm. ------------------------------------------------------------ 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: 207826 Augmented Startups
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: 3120 Galit Shmueli
Includes an example with, - brief definition of what is svm? - svm classification model - svm classification plot - interpretation - tuning or hyperparameter optimization - best model selection - confusion matrix - misclassification rate Machine Learning videos: https://goo.gl/WHHqWP svm is an important machine learning 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: 38049 Bharatendra Rai
Pricing is one of the most important strategic leverages companies can use to define its competitive position. Pricing optimization results a primary focus for companies in any industry and data science is bringing cutting edge new tools to enhance it. In this event 3 Data Scientists from Data Reply will give some practical examples of pricing optimization strategies implemented in some real-world consulting projects applied to different industries. “Online pricing: from theory to application” by Giovanni Corradini, Data Scientist, Data Reply Abstract: Multi-Armed Bandit algorithms are populating the world of e-commerce. How do they work? Giovanni will share the basic of this field and an application of a state-of-the-art algorithm on real world simulation of the ticket industry. Bio: Giovanni is a Data Scientist at Data Reply. He holds a MSc in Applied Statistics - Mathematical Engineering from Politecnico di Milano. He has a background in statistics, machine learning and data mining and he provides decision making support to industries in many different fields.
Views: 128 Data Science Milan
Can we predict the outcome of a football game given a dataset of past games? That's the question that we'll answer in this episode by using the scikit-learn machine learning library as our predictive tool. Code for this video: https://github.com/llSourcell/Predicting_Winning_Teams Please Subscribe! And like. And comment. More learning resources: https://arxiv.org/pdf/1511.05837.pdf https://doctorspin.me/digital-strategy/machine-learning/ https://dashee87.github.io/football/python/predicting-football-results-with-statistical-modelling/ http://data-informed.com/predict-winners-big-games-machine-learning/ https://github.com/ihaque/fantasy https://www.credera.com/blog/business-intelligence/using-machine-learning-predict-nfl-games/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?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: 93732 Siraj Raval
If you have questions or comments on the contents of this video, please email us at [email protected] There has been considerable change in the relationships between customers and companies. Customers are in control of the relationships with their vendors and are not afraid to switch to a new provider if they do not feel they are receiving the service they deserve. Companies now have the ability to know their customers and market to them on a personalized basis using data mining and predictive analytics technologies. Predictive Analytics unlock insights that enable companies to add new customers and grow their existing business by improving their understanding of what their customers want. It uncovers hidden insights in customer data to create more personalized customer experiences that win more business while reducing costs and increasing customer loyalty. Predictive Analytics enable the very sharpest competitive edge. They deliver powerful, unique, qualitative differentiation by providing your enterprise a proprietary source of business intelligence with which to compete in Operations, Customer or Threat & Fraud applications in your organization. A predictive model generated from your data taps into experience to which only your company is privy, since it is unique to your prospect list and to the product and marketing message to which your customers respond (both positively and negatively). Therefore, the model's intelligence and insights are outside the reaches of common knowledge, and the top prospects it flags compose a customized, proprietary contact list. View this informative webinar to learn more about how Predictive Analytics are making a difference in the insurance industry through focused target marketing, and more efficient fraudulent claim detection. We discuss a detailed use-case for a real-world insurance company examining how specific customer attributes were used as indicators for fraud prediction.
Views: 12741 LPA Software Solutions
MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag introduces supervised learning with nearest neighbor classification using feature scaling and decision trees. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 39319 MIT OpenCourseWare
See what's new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1 Download a trial: https://goo.gl/PSa78r A key challenge with the growing volume of measured data in the energy sector is the preparation of the data for analysis. This challenge comes from data being stored in multiple locations, in multiple formats, and with multiple sampling rates. This presentation considers the collection of time-series data sets from multiple sources including Excel files, SQL databases, and data historians. Techniques for preprocessing the data sets are shown, including synchronizing the data sets to a common time reference, assessing data quality, and dealing with bad data. We then show how subsets of the data can be extracted to simplify further analysis. About the Presenter: Abhaya is an Application Engineer at MathWorks Australia where he applies methods from the fields of mathematical and physical modelling, optimisation, signal processing, statistics and data analysis across a range of industries. Abhaya holds a Ph.D. and a B.E. (Software Engineering) both from the University of Sydney, Australia. In his research he focused on array signal processing for audio and acoustics and he designed, developed and built a dual concentric spherical microphone array for broadband sound field recording and beam forming.
Views: 51286 MATLAB
Clicked here http://www.MBAbullshit.com/ and OMG wow! I'm SHOCKED how easy.. No wonder others goin crazy sharing this??? Share it with your other friends too! Fun MBAbullshit.com is filled with easy quick video tutorial reviews on topics for MBA, BBA, and business college students on lots of topics from Finance or Financial Management, Quantitative Analysis, Managerial Economics, Strategic Management, Accounting, and many others. Cut through the bullshit to understand MBA!(Coming soon!) http://www.youtube.com/watch?v=a5yWr1hr6QY
Views: 551362 MBAbullshitDotCom
Can we predict the price of Microsoft stock using Machine Learning? We'll train the Random Forest, Linear Regression, and Perceptron models on many years of historical price data as well as sentiment from news headlines to find out! Code for this video: https://github.com/llSourcell/Stock_Market_Prediction Please Subscribe! And like. And comment. That's what keeps me going. Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology More learning resources: https://www.quantinsti.com/blog/machine-learning-trading-predict-stock-prices-regression/ https://medium.com/@TalPerry/deep-learning-the-stock-market-df853d139e02 https://iknowfirst.com/rsar-machine-learning-trading-stock-market-and-chaos https://www.udacity.com/course/machine-learning-for-trading--ud501 https://quant.stackexchange.com/questions/111/how-can-i-go-about-applying-machine-learning-algorithms-to-stock-markets https://quant.stackexchange.com/questions/111/how-can-i-go-about-applying-machine-learning-algorithms-to-stock-markets http://eugenezhulenev.com/blog/2014/11/14/stock-price-prediction-with-big-data-and-machine-learning/ https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data https://www.linkedin.com/pulse/deep-learning-stock-price-prediction-explained-joe-ellsworth If you're wondering why my voice sounds weird, it's because i was down with Traveler's Diarrhea from my recent trip to India. It's such a debilitating sickness, but the show must go on. And yes, thankfully I'm better now :) Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?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: 79901 Siraj Raval
There's a lot of discussions around service-based pricing and how good data analytics could be a requirement to create the right price points and packages which will appeal to subscribers. RCR Wireless News' Roberta Prescott interviewed Fergus Wills, director of analytics product management at Openwave Mobility.
Views: RCR Wireless News
There are many ways to see the similarities between items. These are techniques that fall under the general umbrella of association. The outcome of this type of technique, in simple terms, is a set of rules that can be understood as “if this, then that”. Code download link - https://goo.gl/mAJ7dC Data Set download link - https://goo.gl/Rtkg5e Video list in Tamil https://goo.gl/Pz2BPn Video list in Englisg https://goo.gl/26f6T1 YouTube channel link www.youtube.com/atozknowledgevideos Website http://atozknowledge.com/ Technology in Tamil & English
Views: 2893 atoz knowledge
For those already familiar with machine learning, this webinar will share some insights on how to better leverage the output of those techniques to improve overall decision-making. For those familiar with optimization, this webinar provides an introduction on how machine learning can improve inputs to and decisions from your optimization models. Specifically, this webinar will: - introduce the use of predictive analytics within Python - use example predictions as inputs into an optimization model to generate superior decision recommendations - then transform the resulting prototype into a ready-to-use application using the Opalytics platform We’ll illustrate our example with Python implementations of the statistical model and the optimization model.
Views: 2571 GurobiVideos
Register for our upcoming webinars - https://amzn.to/2DRvIMs. Learn how to effectively estimate the costs of running your specific project on AWS with our Simple Monthly Calculator. Includes examples that will review architecture, example usage of services, cost breakdown for each service, and the total estimated monthly charge. Presenter: Frank Arrigo, Solutions Architect Manager, Amazon Web Services"
Views: 844 Amazon Web Services
More Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 1: Simple neural networks http://weka.waikato.ac.nz/ Slides (PDF): http://goo.gl/rDuMqu https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 22524 WekaMOOC
FINDING PROFIT IN THE BIG DATA HAYSTACK-1: Olin Professors share recent research. Seethu Seetharaman, marketing professor and Director of Olin's Center for Customer Analytics & Big Data discusses his work with a national super market chain's data and pricing.
Views: 227 OlinBusinessSchool
An example of how to calculate linear regression line using least squares. A step by step tutorial showing how to develop a linear regression equation. Use of colors and animations. Like us on: http://www.facebook.com/PartyMoreStudyLess Related Videos Playlist on Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C SPSS Using Regression http://www.youtube.com/playlist?list=PLWtoq-EhUJe2Z8wz0jnmrbc6S3IwoUPgL Like us on: http://www.facebook.com/PartyMoreStudyLess David Longstreet Professor of the Universe Professor of the Universe: David Longstreet http://www.linkedin.com/in/davidlongstreet/ MyBookSucks.Com
Views: 752065 statisticsfun
In this Python Statistical Modeling Lecture, we learn how to fit model to data using Numpy and Statismodels. Ordinary least square is discussed and ANOVa is performed using Python 3 in Jupyterlab. 🔷🔷🔷🔷🔷🔷🔷 Jupyter Notebooks and Data Sets for Practice: https://github.com/theengineeringworld/statistics-using-python 🔷🔷🔷🔷🔷🔷🔷 Learning Predictive Analytics With Python, Analyzing Election Data With Pandas [Python Statistics] https://youtu.be/sNg8VnMOAfw Hypothesis Testing, p-value & Confidence Intervals, Exploratory Data Analysis In Python Statistics https://youtu.be/kz1IXqcFVCo Python Graph Visualization, Statistics For Data Analytics [ Python Bar Graph Example Tutorial ] https://youtu.be/3KofFIhtjNE Data Cleaning Steps and Methods, How to Clean Data for Analysis With Pandas In Python [Example] 🐼 https://youtu.be/GMxCL0PBHzA Data Wrangling With Python Using Pandas, Data Science For Beginners, Statistics Using Python 🐍🐼 https://youtu.be/tqv3sL67sC8 Cleaning Data In Python Using Pandas In Data Mining Example, Statistics With Python For Data Science https://youtu.be/xcKXmXilaSw Cleaning Data In Python For Statistical Analysis Using Pandas, Big Data & Data Science For Beginners https://youtu.be/4own4ojgbnQ Exploratory Data Analysis In Python, Interactive Data Visualization [Course] With Python and Pandas https://youtu.be/VdWfB30QTYI Python Describe Statistics, Exploratory Data Analysis Using Pandas & NumPy [Descriptive Statistics] https://youtu.be/6SeJH0p7n44 Data Visualization In Python, [ Plots Of Two Variables ] Statistics & Data Analysis With Python 🐍 https://youtu.be/uufMAMUEAaQ Python Graph Visualization, Exploratory Data Analysis With Pandas & Matplotlib [ Python Statistic ] https://youtu.be/Eb9eD4aNS7o Python Data Visualization [ Graphing Categorical Data ] Pandas Data Analysis & Statistics Tutorial https://youtu.be/M1h0pPFVy0E Exploratory Data Analysis In Python, Email Analytics With Pandas [ Predictive Analytics Python ] 🔴 https://youtu.be/03OJrdbhor0 Learning Predictive Analytics With Python, Analyzing Election Data With Pandas [Python Statistics] https://youtu.be/sNg8VnMOAfw 🔷🔷🔷🔷🔷🔷🔷 *** Complete Python Programming Playlists *** * Python Data Science https://www.youtube.com/watch?v=Uct_EbThV1E&list=PLZ7s-Z1aAtmIbaEj_PtUqkqdmI1k7libK * NumPy Data Science Essential Training with Python 3 https://www.youtube.com/playlist?list=PLZ7s-Z1aAtmIRpnGQGMTvV3AGdDK37d2b * Python 3.6.4 Tutorial can be fund here: https://www.youtube.com/watch?v=D0FrzbmWoys&list=PLZ7s-Z1aAtmKVb0fpKyINNeSbFSNkLTjQ * Python Smart Programming in Jupyter Notebook: https://www.youtube.com/watch?v=FkJI8np1gV8&list=PLZ7s-Z1aAtmIVV0dp08_X-yDGrIlTExd2 * Python Coding Interview: https://www.youtube.com/watch?v=wwtzs7vTG50&list=PLZ7s-Z1aAtmJqtN1A3ydeMk0JoD3Lvt9g 📌📌📌📌📌📌📌📌📌📌
Views: 803 TheEngineeringWorld
Netflix is well-known for its data-driven recommendations that seek to customize the user experience for every subscriber. But data science at Netflix extends far beyond that - from optimizing streaming and content caching to informing decisions about the TV shows and films available on the service. The talk covered work done by Becky and the Content Data Science team at Netflix, which seeks to evaluate where Netflix should spend their next content dollar using machine learning and predictive models. The Data Incubator is a data science education company based in NYC, DC, and SF with both corporate training as well as recruiting services. For data science corporate training, we offer customized, in-house corporate training solutions in data and analytics. For data science hiring, we run a free 8 week fellowship training PhDs to become data scientists. The fellowship selects 2% of its 2000+ quarterly applicants and is free for Fellows. Hiring companies (including EBay, Capital One, Pfizer) pay a recruiting fee only if they successfully hire. You can read about us on Harvard Business Review, VentureBeat, or The Next Web, or read about our alumni at LinkedIn, Palantir or the NYTimes. http://thedataincubator.com About the speakers: Dr. Becky Tucker is a Senior Data Scientist at Netflix, a streaming media and entertainment company based in Los Gatos, CA. She holds a PhD in Physics from Caltech. At Netflix, Becky works on models that predict the demand for TV shows and movies. Michael Li founded The Data Incubator, a New York-based training program that turns talented PhDs from academia into workplace-ready data scientists and quants. The program is free to Fellows, employers engage with the Incubator as hiring partners. Previously, he worked as a data scientist (Foursquare), Wall Street quant (D.E. Shaw, J.P. Morgan), and a rocket scientist (NASA). He completed his PhD at Princeton as a Hertz fellow and read Part III Maths at Cambridge as a Marshall Scholar. At Foursquare, Michael discovered that his favorite part of the job was teaching and mentoring smart people about data science. He decided to build a startup to focus on what he really loves. Michael lives in New York, where he enjoys the Opera, rock climbing, and attending geeky data science events.
Views: 14997 The Data Incubator
A three parameter (a,b,c) model y = a + b/x + c ln(x) is fit to a set of data with the Python APMonitor package. This tutorial walks through the process of installing the solver, setting up the objective (normalized sum of squared errors), adjusting the parameter values to minimize the SSE, and plotting the results.
Views: 28317 APMonitor.com
Provides perturbation analysis with r, and includes, - linear model and vif - Perturbation Analysis with Numerical Independent Variables - Perturbation Analysis with Numerical & Categorical Independent Variables - Making Estimates Repeatable - Reclassification Probabilities 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: 1202 Bharatendra Rai
Introduction to Greedy Method What are Feasible and Optimal Solutions General Method of Greedy Examples to Explain Greedy Method Buy C++ course on Udemy.com Price: $10.99 (₹750) URL : https://www.udemy.com/cpp-deep-dive/?couponCode=LEARNCPP Course covers All the topics from Basics to Advance level. Every topic is covered in greater detail with suitable examples. Suitable for Academics and Industry
Views: 149898 Abdul Bari
** Data Science Master's Program: https://www.edureka.co/masters-program/data-scientist-certification ** This "Data Science with R" video by Edureka will help you to understand different Data Science concepts from scratch. The video starts with giving a brief introduction to data science followed by different case Studies. This tutorial will comprise of these topics: 1)What is Data Science 2)Data Manipulation, followed by a case study on Data Manipulation 3)Data Visualization, followed by a case study on Data Visualization 4)Machine Learning, followed by a case study on Machine Learning Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience 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: 12133 edureka!
Modern machine learning libraries make model building look deceptively easy. An unnecessary emphasis (admittedly, annoying to the speaker) on tools like R, Python, SparkML, and techniques like deep learning is prevalent. Relying on tools and techniques while ignoring the fundamentals is the wrong approach to model building. Real-world machine learning requires hard work, discipline and rigor. Development of robust models requires due diligence during data acquisition phase and an obsession with data quality. Feature engineering, choice of evaluation metrics and an understanding of the model bias/variance trade-off is often more important than the choice of tools. Experienced machine learning engineers spend most of their time dealing with data-related issues, model evaluation and parameter tuning while spending only a fraction of their time in actual model building. This is the 80/20 rule. Unlike most talks these days, this talk is not about deep learning. We will ignore the hype and strictly focus on fundamentals of building robust machine learning models. Learn more here: https://www.meetup.com/data-science-dojo/events/241205377/ -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8wyL0 See what our past attendees are saying here: https://hubs.ly/H0f8wpF0 -- Like Us: https://www.facebook.com/datasciencedojo/ Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data-science-dojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo/ Vimeo: https://vimeo.com/datasciencedojo
Views: 5021 Data Science Dojo
Broad scale predictive modeling and marketing optimization in retail sales KDD 2011 Dan Steinberg Felipe Fernandez Martinez The challenge of predicting retail sales on a product-by-product basis throughout a network of retail stores has been researched intensively by applied econometricians and statisticians for decades. The principal tools of analysis have been linear regression with Bayesian inspired adjustments to stabilize demand curve estimates. The scale of such analytics can be challenging as retailers often work with more than 100,000 products (SKUs) and typically operate networks of hundreds of brick and mortar stores. Department and grocery stores are excellent examples but fast food restaurants also require such detailed predictive modeling systems. Depending on the objectives of the company, predictions may be required for blocks of time spanning a week or more, or, as in the case of fast food operators, predictions are required for each 15-minute time interval of the operating day. The authors have modernized industry standard approaches to such predictive modeling by leveraging advanced data mining techniques. These techniques are more adept in detecting nonlinear response and accommodating interactions and automatically sifting through hundreds if not thousands of potential factors influencing sales outcomes. Results show that conventional statistical models miss a substantial fraction of the explainable variance while the new methods dominate in terms of performance and speed of model development. Accurate prediction is required for reliable planning and logistics, and optimization. Optimization with respect to pricing, promotion and assortment can be asked for relative to a variety of objectives (e.g. revenue, profits) and short term and long-term optimization may result in different decisions being taken. A unique challenge for retailers is the large number of constraints to which complex retail organizations are subject. Contracts and special understandings with valued suppliers severely constrain a retailer's flexibility. For example, certain products may not be promotable (or discounted) in isolation, and others (say from competitors) may not be promoted jointly, and the costs of goods sold may well depend on the quantities contracted. We discuss how we have resolved such challenges via a cycle of prediction and simulation to develop a flexible high-speed system for handling arbitrary constraints, arbitrary objectives, and achieve new levels of predictive accuracy and reliability.
Views: 15 Research in Science and Technology
Table of Contents: 00:15 - Previous trainings 00:56 - Problem Statement 01:16 - Simple Histogram calculation 02:37 - Data parallelism 04:10 - Optimized code 05:39 - Strength reduction 06:07 - Performance results 07:16 - About next section
Views: 1226 Vadim Karpusenko
Imagine taking historical stock market data and using data science to more accurately predict future stock values. This is precisely the aim of the Microsoft Time Series data mining algorithm.. MSBI - SSAS - Data Mining - Time Series. In this video you will learn the theory of Time Series Forecasting. You will what is univariate time series analysis, AR, MA, ARMA vesves ARIMA modelling and how to use these models to do forecast.. 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: 603 Fidela Aretha
What the Wizard produces: Churn example The new Wizard feature provided with RapidMiner version 6 allows processes to be created quickly for a number of typical uses encountered in various industries. These processes give easy to understand results. The processes that are created represent good practice and show some interesting techniques. This video gives a step by step commentary through the Churn example to help explain how it works. Topics covered Running the Wizard to create the process Stepping through the process
Views: 67 Markus Hofmann
Data science is a priority for your businesses, and data science teams are under more pressure than ever to deliver ROI. What does it look like to deliver business value? Watch this example of how a data science team can help multiple departments prepare for an upcoming storm, reducing the financial and operational impact on the company. IBM’s Irina Saburova and Rosie Pongracz will take you through a demonstration of how the Finance, Operations and Fraud departments benefit from the use of a diverse set of tools and techniques. Video Chapters: 01:28 - Chapter 1 "Use case scenario overview" Duration (2:26) 03:54 - Chapter 2 "Forecasting" Duration (1:40) 05:34 - Chapter 3 "Optimization" Duration (1:47) 07:21 - Chapter 4 "Fraud Detection" Duration (1:34) 08:55 - Chapter 5 "Streamlining procurement and claim management" Duration (1:35) 10:30 - Chapter 6 "Summary" Duration (2:07) Request a consultation - https://www.ibm.com/analytics/common/scheduler/expert-advice Learn more - https://www.ibm.com/products/data-science-experience
Views: 848 IBM Analytics
Time-Series Forecast in the Energy Sector with Automated Machine Learning Stefano Casasso, Data Scientist at Predictive Layer SwissAI Machine Learning Meetup 2018.10.15 1. What is Time Series Analysis? 2. What are the common mistakes in time series analysis? 3. How to treat features in time series analysis? 4. Application example from the energy sector Abstract: Time series forecasting using machine learning (ML) presents additional challenges compared to other "static" ML tasks. From data cleaning to feature engineering, from model building to model validation: in each of these steps the time component has to be handled with care in order to avoid overfitting and bias. In this presentation, all these tasks are discussed using "real world" examples taken from the energy sector, namely electricity consumption/production. Predictive Layer (PL) is a company based in Rolle (Switzerland) which has built its business model around times series forecasting. PL is currently active in the sector of energy, finance, retail, transport and supply chain optimization. http://www.predictivelayer.com Bio: Stefano Casasso studied experimental particle physics in Turin, Italy, where he obtained his Ph.D. with a thesis on the newly observed Higgs boson at the Large Hadron Collider at CERN, Geneva. He spent 3 more years at CERN as a research associate for the Imperial College, London analyzing the data of the CMS particle detector in search of production of so-called "supersymmetric particles". Since 2 years he turned into data science in the private sector. After a short period in Zürich, working in the IoT sector, he joined Predictive Layer where he is specializing in the analysis of time series data and predictive modeling. In particular, he is responsible for the projects in the energy sector, where he applies his skills to forecast energy demand, renewable energy production, and electricity price. https://www.linkedin.com/in/stefano-casasso/ ## Organizers ## SwissAI Machine Learning Meetup is one of the larges AI meetups in Switzerland, with regular meetings, great speakers invited from academia and industry and over 1200 members from Lake Geneva Area. For more information and future events visit https://www.SwissAI.org Pawel Rosikiewicz, Founder of SwissAI,, Event Organiser https://www.linkedin.com/in/pawel-rosikiewicz/ Juraj Korček, Data Scientist and ML Engineer, Event co-organiser and Interviews https://www.linkedin.com/in/korcekjuraj/ Ieva Vaišnoraitė-Navikienė, ML Engineer, Event co-organiser https://www.linkedin.com/in/ieva-vaisnoraite-navikiene/ Matteo Pagliardini, Senior ML Engineer, Event co-organiser https://www.linkedin.com/in/matteo-pagliardini/ Clement Charollais, EPFL, Camera Operator and Movie Editing https://www.linkedin.com/in/clément-charollais-b7209177/ Sponsors: École Polytechnique Fédérale de Lausanne (EPFL) https://www.epfl.ch Innovaoud https://www.innovaud.ch SamurAI - Data Science Services https://www.samurai.team
Views: 410 SwissAI
This video concludes our Introduction to Text Analytics with R and covers: – Optimizing our model for the best generalizability on new/unseen data. – Discussion of the sensitivity/specificity tradeoff of our optimized model. – Potential next steps regarding feature engineering and algorithm selection for additional gains in effectiveness. – For those that are interested, a collection of resources for further study to broaden and deepen their text analytics skills. About the Series This data science tutorial introduces the viewer to the exciting world of text analytics with R programming. As exemplified by the popularity of blogging and social media, textual data if far from dead – it is increasing exponentially! Not surprisingly, knowledge of text analytics is a critical skill for data scientists if this wealth of information is to be harvested and incorporated into data products. This data science training provides introductory coverage of the following tools and techniques: – Tokenization, stemming, and n-grams – The bag-of-words and vector space models – Feature engineering for textual data (e.g. cosine similarity between documents) – Feature extraction using singular value decomposition (SVD) – Training classification models using textual data – Evaluating accuracy of the trained classification models The data and R code used in this series is available here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Introduction%20to%20Text%20Analytics%20with%20R -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f5Kdm0 See what our past attendees are saying here: https://hubs.ly/H0f5K_v0 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 3282 Data Science Dojo