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Machine Learning Algorithms | Machine Learning Tutorial | Data Science Training | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Machine Learning Algorithms Tutorial shall teach you what machine learning is, and the various ways in which you can use machine learning to solve a problem! Towards the end, you will learn how to prepare a dataset for model creation and validation and how you can create a model using any machine learning algorithm! In this Machine Learning Algorithms Tutorial video you will understand: 1) What is an Algorithm? 2) What is Machine Learning? 3) How is a problem solved using Machine Learning? 4) Types of Machine Learning 5) Machine Learning Algorithms 6) Demo Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #MachineLearningAlgorithms #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: 173425 edureka!
K mean clustering algorithm with solve example
 
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#kmean datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 451130 Last moment tuitions
Hierarchical Agglomerative Clustering [HAC - Single Link]
 
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Data Warehouse and Mining For more: http://www.anuradhabhatia.com
Views: 119737 Anuradha Bhatia
Apriori Algorithm with R Studio
 
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This is a video for RMD Sinhgad School of Engineering (BE-Computer) as a demonstration for one of the assignments of Business Analytics and Intelligence. Important Links: Ubuntu 16.04.2 LTS Download: https://www.ubuntu.com/download/desktop R installation instructions: https://www.datascienceriot.com/how-to-install-r-in-linux-ubuntu-16-04-xenial-xerus/kris/ R studio Download: https://www.rstudio.com/products/rstudio/download/ R Tutorial: http://tryr.codeschool.com/
Views: 7846 Varun Joshi
Data Science & Machine Learning - KNN Classification Exercise - DIY- 24 -of-50
 
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Data Science & Machine Learning - KNN Classification Exercise - DIY- 24 -of-50 Do it yourself Tutorial by Bharati DW Consultancy cell: +1-562-646-6746 (Cell & Whatsapp) email: [email protected] website: http://bharaticonsultancy.in/ Google Drive- https://drive.google.com/open?id=0ByQlW_DfZdxHeVBtTXllR0ZNcEU K – Nearest Neighbors (K-NN) Download the dataset k-NN algorithm, it uses Euclidean distance, which is the distance one would measure if it were possible to use a ruler to connect two points. http://archive.ics.uci.edu/ml/datasets/Glass+Identification Choose k- equal to the square root of the number of training sample. knn(train = train_data, test = test_data, cl = train_labels, k = num) You may have to use the normalize function. normalize {angle brace}- function(y) {return ((y - min(y)) / (max(y) - min(y)))} Data Science & Machine Learning - Getting Started - DIY- 1 -of-50 Data Science & Machine Learning - R Data Structures - DIY- 2 -of-50 Data Science & Machine Learning - R Data Structures - Factors - DIY- 3 -of-50 Data Science & Machine Learning - R Data Structures - List & Matrices - DIY- 4 -of-50 Data Science & Machine Learning - R Data Structures - Data Frames - DIY- 5 -of-50 Data Science & Machine Learning - Frequently used R commands - DIY- 6 -of-50 Data Science & Machine Learning - Frequently used R commands contd - DIY- 7 -of-50 Data Science & Machine Learning - Installing RStudio- DIY- 8 -of-50 Data Science & Machine Learning - R Data Visualization Basics - DIY- 9 -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(a) -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(b) -of-50 Data Science & Machine Learning - Multiple Linear Regression Model - DIY- 11 -of-50 Data Science & Machine Learning - Evaluate Model Performance - DIY- 12 -of-50 Data Science & Machine Learning - RMSE & R-Squared - DIY- 13 -of-50 Data Science & Machine Learning - Numeric Predictions using Regression Trees - DIY- 14 -of-50 Data Science & Machine Learning - Regression Decision Trees contd - DIY- 15 -of-50 Data Science & Machine Learning - Method Types in Regression Trees - DIY- 16 -of-50 Data Science & Machine Learning - Real Time Project 1 - DIY- 17 -of-50 Data Science & Machine Learning - KNN Classification - DIY- 21 -of-50 Data Science & Machine Learning - KNN Classification Hands on - DIY- 22 -of-50 Data Science & Machine Learning - KNN Classification HandsOn Contd - DIY- 23 -of-50 Data Science & Machine Learning - KNN Classification Exercise - DIY- 24 -of-50 Machine learning, data science, R programming, Deep Learning, Regression, Neural Network, R Data Structures, Data Frame, RMSE & R-Squared, Regression Trees, Decision Trees, Real-time scenario, KNN
Anomaly Detection: Algorithms, Explanations, Applications
 
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Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly "alarms" to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning. See more at https://www.microsoft.com/en-us/research/video/anomaly-detection-algorithms-explanations-applications/
Views: 17772 Microsoft Research
Data Mining Lecture -- Bayesian Classification | Naive Bayes Classifier | Solved Example (Eng-Hindi)
 
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In the bayesian classification The final ans doesn't matter in the calculation Because there is no need of value for the decision you have to simply identify which one is greater and therefore you can find the final result. -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 209032 Well Academy
Machine Learning Tutorial 25 - Intro to the ID3 Algorithm
 
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Best Machine Learning book: https://amzn.to/2MilWH0 (Fundamentals Of Machine Learning for Predictive Data Analytics). Machine Learning and Predictive Analytics. #MachineLearning This is the first video in the sequence on the ID3 Algorithm This online course covers big data analytics stages using machine learning and predictive analytics. Big data and predictive analytics is one of the most popular applications of machine learning and is foundational to getting deeper insights from data. Starting off, this course will cover machine learning algorithms, supervised learning, data planning, data cleaning, data visualization, models, and more. This self paced series is perfect if you are pursuing an online computer science degree, online data science degree, online artificial intelligence degree, or if you just want to get more machine learning experience. Enjoy! Check out the entire series here: https://www.youtube.com/playlist?list=PL_c9BZzLwBRIPaKlO5huuWQdcM3iYqF2w&playnext=1 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Support me! http://www.patreon.com/calebcurry Subscribe to my newsletter: http://bit.ly/JoinCCNewsletter Donate!: http://bit.ly/DonateCTVM2. ~~~~~~~~~~~~~~~Additional Links~~~~~~~~~~~~~~~ More content: http://CalebCurry.com Facebook: http://www.facebook.com/CalebTheVideoMaker Google+: https://plus.google.com/+CalebTheVideoMaker2 Twitter: http://twitter.com/calebCurry Amazing Web Hosting - http://bit.ly/ccbluehost (The best web hosting for a cheap price!)
Views: 3970 Caleb Curry
Data Science & Machine Learning - KNN Classification Hands on - DIY- 22 -of-50
 
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Data Science & Machine Learning - KNN Classification Hands on - DIY- 22 -of-50 Do it yourself Tutorial by Bharati DW Consultancy cell: +1-562-646-6746 (Cell & Whatsapp) email: [email protected] website: http://bharaticonsultancy.in/ Google Drive- https://drive.google.com/open?id=0ByQlW_DfZdxHeVBtTXllR0ZNcEU K – Nearest Neighbors (K-NN) The nearest neighbors is a very simple and effective approach to classification which are well-suited for classification tasks. k-NN utilizes information about an example's k-nearest neighbors to classify unlabeled examples. k- generally is an odd-number for nearest neighbors – in case of tie-breaker. k neighbors in the training data that are the "nearest" in similarity. The unlabeled test instance is then assigned the class of the majority of the k nearest neighbors. k-NN algorithm uses Euclidean distance, which is the distance one would measure if it were possible to use a ruler to connect two points. Choosing k- common practice is to begin with k equal to the square root of the number of training sample. knn(train = train_data, test = test_data, cl = train_labels, k = num) Use normalize function if the range of values are really normalize {angle brace}- function(y) {return ((y - min(y)) / (max(y) - min(y)))} Citation Policy: If you publish material based on databases obtained from this repository, then, in your acknowledgements, please note the assistance you received by using this repository. This will help others to obtain the same data sets and replicate your experiments. We suggest the following pseudo-APA reference format for referring to this repository: Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science. Here is a BiBTeX citation as well: @misc{Lichman:2013 , author = "M. Lichman", year = "2013", title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences" } Data Science & Machine Learning - Getting Started - DIY- 1 -of-50 Data Science & Machine Learning - R Data Structures - DIY- 2 -of-50 Data Science & Machine Learning - R Data Structures - Factors - DIY- 3 -of-50 Data Science & Machine Learning - R Data Structures - List & Matrices - DIY- 4 -of-50 Data Science & Machine Learning - R Data Structures - Data Frames - DIY- 5 -of-50 Data Science & Machine Learning - Frequently used R commands - DIY- 6 -of-50 Data Science & Machine Learning - Frequently used R commands contd - DIY- 7 -of-50 Data Science & Machine Learning - Installing RStudio- DIY- 8 -of-50 Data Science & Machine Learning - R Data Visualization Basics - DIY- 9 -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(a) -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(b) -of-50 Data Science & Machine Learning - Multiple Linear Regression Model - DIY- 11 -of-50 Data Science & Machine Learning - Evaluate Model Performance - DIY- 12 -of-50 Data Science & Machine Learning - RMSE & R-Squared - DIY- 13 -of-50 Data Science & Machine Learning - Numeric Predictions using Regression Trees - DIY- 14 -of-50 Data Science & Machine Learning - Regression Decision Trees contd - DIY- 15 -of-50 Data Science & Machine Learning - Method Types in Regression Trees - DIY- 16 -of-50 Data Science & Machine Learning - Real Time Project 1 - DIY- 17 -of-50 Data Science & Machine Learning - KNN Classification - DIY- 21 -of-50 Data Science & Machine Learning - KNN Classification Hands on - DIY- 22 -of-50 Machine learning, data science, R programming, Deep Learning, Regression, Neural Network, R Data Structures, Data Frame, RMSE & R-Squared, Regression Trees, Decision Trees, Real-time scenario, KNN
Views: 1129 BharatiDWConsultancy
How do I select features for Machine Learning?
 
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Selecting the "best" features for your Machine Learning model will result in a better performing, easier to understand, and faster running model. But how do you know which features to select? In this video, I'll discuss 7 feature selection tactics used by the pros that you can apply to your own model. At the end, I'll give you my top 3 tips for effective feature selection. WANT TO JOIN MY NEXT WEBCAST? Become a member ($5/month): https://www.patreon.com/dataschool === RELATED RESOURCES === Dimensionality reduction presentation: https://www.youtube.com/watch?v=ioXKxulmwVQ Feature selection in scikit-learn: http://scikit-learn.org/stable/modules/feature_selection.html Sequential Feature Selector from mlxtend: http://rasbt.github.io/mlxtend/user_guide/feature_selection/SequentialFeatureSelector/ == WANT TO GET BETTER AT MACHINE LEARNING? == 1) WATCH my scikit-learn video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A 2) SUBSCRIBE for more videos: https://www.youtube.com/dataschool?sub_confirmation=1 3) ENROLL in my Machine Learning course: https://www.dataschool.io/learn/ 4) LET'S CONNECT! - Newsletter: https://www.dataschool.io/subscribe/ - Twitter: https://twitter.com/justmarkham - Facebook: https://www.facebook.com/DataScienceSchool/ - LinkedIn: https://www.linkedin.com/in/justmarkham/
Views: 17308 Data School
How to do the Titanic Kaggle competition in R - Part 1
 
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As part of submitting to Data Science Dojo's Kaggle competition you need to create a model out of the titanic data set. We will show you how to do this using RStudio. Titanic Data Set: https://www.kaggle.com/c/titanic Download RStudio: https://www.rstudio.com/products/rstudio -- Learn more about Data Science Dojo here: https://hubs.ly/H0hz71Q0 Watch the latest video tutorials here: https://hubs.ly/H0hz78h0 See what our past attendees are saying here: https://hubs.ly/H0hz72N0 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 800 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- 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: 55950 Data Science Dojo
Data Science & Machine Learning - KNN Classification HandsOn Contd - DIY- 23 -of-50
 
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Data Science & Machine Learning - KNN Classification HandsOn Contd - DIY- 23 -of-50 Do it yourself Tutorial by Bharati DW Consultancy cell: +1-562-646-6746 (Cell & Whatsapp) email: [email protected] website: http://bharaticonsultancy.in/ Google Drive- https://drive.google.com/open?id=0ByQlW_DfZdxHeVBtTXllR0ZNcEU K – Nearest Neighbors (K-NN) The nearest neighbors is a very simple and effective approach to classification which are well-suited for classification tasks. k-NN utilizes information about an example's k-nearest neighbors to classify unlabeled examples. k- generally is an odd-number for nearest neighbors – in case of tie-breaker. k neighbors in the training data that are the "nearest" in similarity. The unlabeled test instance is then assigned the class of the majority of the k nearest neighbors. k-NN algorithm uses Euclidean distance, which is the distance one would measure if it were possible to use a ruler to connect two points. Choosing k- common practice is to begin with k equal to the square root of the number of training sample. knn(train = train_data, test = test_data, cl = train_labels, k = num) Use normalize function if the range of values are really normalize {angle brace}- function(y) {return ((y - min(y)) / (max(y) - min(y)))} Data Science & Machine Learning - Getting Started - DIY- 1 -of-50 Data Science & Machine Learning - R Data Structures - DIY- 2 -of-50 Data Science & Machine Learning - R Data Structures - Factors - DIY- 3 -of-50 Data Science & Machine Learning - R Data Structures - List & Matrices - DIY- 4 -of-50 Data Science & Machine Learning - R Data Structures - Data Frames - DIY- 5 -of-50 Data Science & Machine Learning - Frequently used R commands - DIY- 6 -of-50 Data Science & Machine Learning - Frequently used R commands contd - DIY- 7 -of-50 Data Science & Machine Learning - Installing RStudio- DIY- 8 -of-50 Data Science & Machine Learning - R Data Visualization Basics - DIY- 9 -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(a) -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(b) -of-50 Data Science & Machine Learning - Multiple Linear Regression Model - DIY- 11 -of-50 Data Science & Machine Learning - Evaluate Model Performance - DIY- 12 -of-50 Data Science & Machine Learning - RMSE & R-Squared - DIY- 13 -of-50 Data Science & Machine Learning - Numeric Predictions using Regression Trees - DIY- 14 -of-50 Data Science & Machine Learning - Regression Decision Trees contd - DIY- 15 -of-50 Data Science & Machine Learning - Method Types in Regression Trees - DIY- 16 -of-50 Data Science & Machine Learning - Real Time Project 1 - DIY- 17 -of-50 Data Science & Machine Learning - KNN Classification - DIY- 21 -of-50 Data Science & Machine Learning - KNN Classification Hands on - DIY- 22 -of-50 Data Science & Machine Learning - KNN Classification HandsOn Contd - DIY- 23 -of-50 Machine learning, data science, R programming, Deep Learning, Regression, Neural Network, R Data Structures, Data Frame, RMSE & R-Squared, Regression Trees, Decision Trees, Real-time scenario, KNN
Top Ten Machine Learning Algorithms | The Bad, The good, The Better data
 
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Provides an overview of top 10 machine learning algorithms for beginners and discussion about data quality. Becoming Data Scientist: https://goo.gl/JWyyQc Introductory R Videos: https://goo.gl/NZ55SJ Machine Learning videos: https://goo.gl/WHHqWP Deep Learning with TensorFlow: https://goo.gl/5VtSuC Image Analysis & Classification: https://goo.gl/Md3fMi Text mining: https://goo.gl/7FJGmd Data Visualization: https://goo.gl/Q7Q2A8 Playlist: https://goo.gl/iwbhnE
Views: 1720 Bharatendra Rai
Decision Tree Model for Regression Problem in R | Data Science
 
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Decision is a supervised learning algorithm that can used to predict values based on factors. It can be used for both regression & classification problems. It is very easy to understand a decision tree model as opposed to models like linear regression, logistic regression, random forest, boosting/bagging, neural network etc. Contact us : [email protected] ANalytics Study Pack : https://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: 6822 Analytics University
Using Machine Learning for Predicting NFL Games | Data Dialogs 2016
 
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You are a HUGE football fan. Every week you pick winners in an NFL pick-em' league. Somehow, all that fan experience doesn't translate into consistently winning your league. Perhaps you need a more systematic approach that takes some of the emotion out of it. Where to start? Betting spreads provide a consistent and robust mechanism for encapsulating the variables and predicting outcomes of NFL games. In a weekly confidence pool, spreads also perform very well as opposed to intuition-based guessing and "knowledge" from years of being a fan. Can we do better? In this talk, we will discuss an approach to use machine learning algorithms to make improvements on the spread method of ranking winners on a weekly basis as an exercise in winning your friendly neighborhood confidence pool. https://datadialogs.ischool.berkeley.edu/2016/schedule/using-machine-learning-predicting-nfl-games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amit Bhattacharyya Senior Data Scientist Teachers Pay Teachers Amit is the Senior Data Scientist at Teachers Pay Teachers, an online marketplace for teachers to buy, sell and share original educational resources. At TpT, Amit works on developing both technical and modeling infrastructure to analyze customer behavior and ways to more effectively connect buyers and sellers. Amit also teaches in the MIDS program at the UC Berkeley School of Information. He received a Ph.D. in physics from Indiana Universtiy. Previously, he did a two-year stint in advertising, and worked as a quantitative analyst at various banks and hedge funds for twelve years. In his spare time, he likes to plan skiing and backpacking trips, and dabble with machine learning algorithms for fantasy football.
Agglomerative Clustering Algorithm explained with example. (Hindi-Eng). | DWM | ML.
 
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In this video u will learn about Agglomerative Clustering in DWM and ML. One of the most important topic from university exam point of view. 70-90% chances of coming in University exams for both Dataware house n Mining and Machine learning subjects. Please like, share and SUBSCRIBE to my channel and also press 🔔 icon for new video updates. 👇👇 https://www.youtube.com/ajstutorial?sub_confirmation=1 Also check out my previous videos. Dataware house and Mining Playlist. 1. Decision Tree 2. K means 3. Apriori 4. OLAP Operations CLICK ON BELOW LINK. 👇👇 https://www.youtube.com/playlist?list=PLj7shZlROgtn5KLL8z-glQh3m8u-OTnnB For Notes click on below link. 👇👇 https://drive.google.com/file/d/1-_NY5frluhchiMQOhnkuRwIN3UVewwde/view?usp=drivesdk For Topic Suggestions fill the Google form 👇👇 https://forms.gle/GTySmxSSvKQGVaWK8 Follow me on Instagram. 👇👇 https://www.instagram.com/ajs_tutorial/ Music by - Zella Day - https://youtu.be/pZzVgNn3y68
Views: 161 AJS Tutorial
Text Classification Using Naive Bayes
 
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This is a low math introduction and tutorial to classifying text using Naive Bayes. One of the most seminal methods to do so.
Views: 101874 Francisco Iacobelli
Random Forest Using R: Step by Step Tutorial
 
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You can download the "Credit Card Dataset" from the below link: https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients Learn Data Science & Machine Learning by doing! Hands On Experience Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science! This course is for those : 1. Who wants to be Data Scientist 2. Who are working as analyst / software developer but wants to be Data Scientist What is Data Science ? Data science is used to extract patterns or insights from data to predict future or to understand customer behavior and so on. Data science is a "concept to unify statistics, data analysis and their related methods" in order to "understand and analyze actual phenomena" with data Mining large amounts of structured and unstructured data to identify patterns can help an organization to reduce costs, increase efficiencies, recognize new market opportunities and increase the organization's competitive advantage. Some Data Science and machine learning Applications Netflix uses data science & machine learning to mine movie viewing patterns to understand what drives user interest, and uses that to make decisions on which Netflix original series to produce. Companies like Flipkart and Amazon uses data science and machine learning to understand the customer shopping behavior to do better recommendations. Gmail's spam filter uses data science (machine learning algorithm) to process incoming mail and determines if a message is junk or not.. Proctor & Gamble utilizes data science (machine learning ) models to more clearly understand future demand, which help plan for production levels more optimally. Why Programming Won't Work in some Cases?? Have you ever thought of the scenario where all the cars will be moving without a driver that means something like automated machines say for example automatic washing machine. But there is a difference. 1. For automatic washing machine,we can write programs for the washing machine functionality. 2. For automated cars without drivers in high traffic.Just imagine ,how complex and dangerous it will be when someone starts coding /programming for such functionalities.For cars to automate we would require something which is called "Machine Learning " In this course, we are first going to first discuss Data Structures,etc. in R like : 1. Vectors 2. Matrices 3. Data Frames 4. Factors 5. Numerical/Categorical Variables 6. List 7. How to convert matrix into data frame Programming in R Data Visualization Then implementation/working of machine learning models like 1. Linear Regression 2. Decision Tree 3. Random Forest 4.Neural Networks 5. Deep learning 6. H2o framework 7. Cross validation /How to avoid Over fitting 8. Dimensionality Reduction Techniques All the materials for this data science & machine learning course are FREE. You can download and install R, with simple commands on Windows, Linux, or Mac. This course focuses on "how to build and understand", not just "how to use".It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally.
Views: 2286 Machine Learning TV
12. Clustering
 
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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 discusses clustering. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 94361 MIT OpenCourseWare
Data science : R Predictive analytics with Decision Tree
 
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R - Decision Tree. Advertisements. Decision tree is a graph to represent choices and their results in form of a tree. The nodes in the graph represent an event or choice and the edges of the graph represent the decision rules or conditions. It is mostly used in Machine Learning and Data Mining applications using R. Video list in Tamil https://goo.gl/Pz2BPn Video list in English https://goo.gl/26f6T1 Data Download - http://atozknowledge.com/downloads/r/data1.csv YouTube channel link www.youtube.com/atozknowledgevideos Website http://atozknowledge.com/ Technology in Tamil & English
Views: 1030 atoz knowledge
Eclat Association Rule Learning - Fun and Easy Machine Learning Tutorial
 
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Eclat Association Rule Learning - Fun and Easy Machine Learning Tutorial ►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 Limited Time - Discount Coupon Hey guys and welcome to another fun and easy machine tutorial on Eclat. Today we are going to be analyzing what video games get sold more frequently using an associated rule algorithm called Eclat. The Eclat algorithm which is an acronym for Equivalence CLAss Transformation is used to perform itemset mining. Itemset mining let us find frequent patterns in data like if a consumer buys Halo, he also buys Gears of War. This type of pattern is called association rules and is used in many application domains such as recommender systems. In the previous lecture we discussed the Apriori Algorithm. Eclat is one of the algorithms which is meant to improve the Efficiency of Apriori. Eclat is a depth-first search algorithm using set intersection. It is a naturally elegant algorithm suitable for both sequential as well as parallel execution with locality-enhancing properties. It was first introduced by Zaki, Parthasarathy, Li and Ogihara in a series of papers written in 1997. ------------------------------------------------------------ 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: 6831 Augmented Startups
How to Perform K-Means Clustering in R Statistical Computing
 
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In this video I go over how to perform k-means clustering using r statistical computing. Clustering analysis is performed and the results are interpreted. http://www.influxity.com
Views: 206834 Influxity
The Best Way to Prepare a Dataset Easily
 
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In this video, I go over the 3 steps you need to prepare a dataset to be fed into a machine learning model. (selecting the data, processing it, and transforming it). The example I use is preparing a dataset of brain scans to classify whether or not someone is meditating. The challenge for this video is here: https://github.com/llSourcell/prepare_dataset_challenge Carl's winning code: https://github.com/av80r/coaster_racer_coding_challenge Rohan's runner-up code: https://github.com/rhnvrm/universe-coaster-racer-challenge Come join other Wizards in our Slack channel: http://wizards.herokuapp.com/ Dataset sources I talked about: https://github.com/caesar0301/awesome-public-datasets https://www.kaggle.com/datasets http://reddit.com/r/datasets More learning resources: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-prepare-data http://machinelearningmastery.com/how-to-prepare-data-for-machine-learning/ https://www.youtube.com/watch?v=kSslGdST2Ms http://freecontent.manning.com/real-world-machine-learning-pre-processing-data-for-modeling/ http://docs.aws.amazon.com/machine-learning/latest/dg/step-1-download-edit-and-upload-data.html http://paginas.fe.up.pt/~ec/files_1112/week_03_Data_Preparation.pdf Please subscribe! And like. And comment. That's what keeps me going. 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: 193027 Siraj Raval
Machine Learning Tutorial 10 - Binning Data
 
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Best Machine Learning book: https://amzn.to/2MilWH0 (Fundamentals Of Machine Learning for Predictive Data Analytics). Machine Learning and Predictive Analytics. #MachineLearning Features are the term used for the columns in the analytics base table (ABT). There is a particular type of feature known as a continuous feature. These are features that have a very high cardinality because the allowed values (domain) is on a spectrum. We can convert these continuous features to categorical features through a process called binning. This online course covers big data analytics stages using machine learning and predictive analytics. Big data and predictive analytics is one of the most popular applications of machine learning and is foundational to getting deeper insights from data. Starting off, this course will cover machine learning algorithms, supervised learning, data planning, data cleaning, data visualization, models, and more. This self paced series is perfect if you are pursuing an online computer science degree, online data science degree, online artificial intelligence degree, or if you just want to get more machine learning experience. Enjoy! Check out the entire series here: https://www.youtube.com/playlist?list=PL_c9BZzLwBRIPaKlO5huuWQdcM3iYqF2w&playnext=1 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Support me! http://www.patreon.com/calebcurry Subscribe to my newsletter: http://bit.ly/JoinCCNewsletter Donate!: http://bit.ly/DonateCTVM2. ~~~~~~~~~~~~~~~Additional Links~~~~~~~~~~~~~~~ More content: http://CalebCurry.com Facebook: http://www.facebook.com/CalebTheVideoMaker Google+: https://plus.google.com/+CalebTheVideoMaker2 Twitter: http://twitter.com/calebCurry Amazing Web Hosting - http://bit.ly/ccbluehost (The best web hosting for a cheap price!)
Views: 6357 Caleb Curry
List of Machine Learning Algorithm(Regression, Decision Tree, Association Rule Mining)  Part 18
 
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This video will explain List of different Machine learning Algorithm and short introduction of each one. Learning Style way : Supervised Learning Unsupervised Learning Similarity : Instance-based Regression  Regularization  Decision Tree Algorithms Bayesian Algorithms Clustering Algorithms Association Rule Learning Algorithms Neural Network Algorithms Dimensionality Reduction Deep Learning Ensemble Algorithms NPL, Genetic, Recommender system, Graphical Models Thank You
Views: 1776 MyStudy
Data Mining  Association Rule - Basic Concepts
 
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short introduction on Association Rule with definition & Example, are explained. Association rules are if/then statements used to find relationship between unrelated data in information repository or relational database. Parts of Association rule is explained with 2 measurements support and confidence. types of association rule such as single dimensional Association Rule,Multi dimensional Association rules and Hybrid Association rules are explained with Examples. Names of Association rule algorithm and fields where association rule is used is also mentioned.
9.1: Genetic Algorithm: Introduction - The Nature of Code
 
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Welcome to part 1 of a new series of videos focused on Evolutionary Computing, and more specifically, Genetic Algorithms. In this tutorial, I introduce the concept of a genetic algorithm, how it can be used to approach "search" problems and how it relates to brute force algorithms. Support this channel on Patreon: https://patreon.com/codingtrain Send me your questions and coding challenges!: https://github.com/CodingTrain/Rainbow-Topics Contact: https://twitter.com/shiffman Links discussed in this video: The Nature of Code: http://natureofcode.com/ BoxCar2D: http://boxcar2d.com/ Source Code for the Video Lessons: https://github.com/CodingTrain/Rainbow-Code p5.js: https://p5js.org/ Processing: https://processing.org For More Genetic Algorithm videos: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6bJM3VgzjNV5YxVxUwzALHV For More Nature of Code videos: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6aFlwukCmDf0-1-uSR7mklK Help us caption & translate this video! http://amara.org/v/Sld6/
Views: 225111 The Coding Train
Let’s Write a Decision Tree Classifier from Scratch - Machine Learning Recipes #8
 
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Hey everyone! Glad to be back! Decision Tree classifiers are intuitive, interpretable, and one of my favorite supervised learning algorithms. In this episode, I’ll walk you through writing a Decision Tree classifier from scratch, in pure Python. I’ll introduce concepts including Decision Tree Learning, Gini Impurity, and Information Gain. Then, we’ll code it all up. Understanding how to accomplish this was helpful to me when I studied Machine Learning for the first time, and I hope it will prove useful to you as well. You can find the code from this video here: https://goo.gl/UdZoNr https://goo.gl/ZpWYzt Books! Hands-On Machine Learning with Scikit-Learn and TensorFlow https://goo.gl/kM0anQ Follow Josh on Twitter: https://twitter.com/random_forests Check out more Machine Learning Recipes here: https://goo.gl/KewA03 Subscribe to the Google Developers channel: http://goo.gl/mQyv5L
Views: 231157 Google Developers
K Means Clustering in R
 
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This video tutorial shows you how to use the means function in R to do K-Means clustering. You will need to know how to read in data, subset data and plot items in order to use this video
Views: 47513 Ed Boone
R Programming For Beginners | R Language Tutorial | R Tutorial For Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R Programming Tutorial For Beginners (R Tutorial Blog: https://goo.gl/mia382) will help you in understanding the fundamentals of R and will help you build a strong foundation in R. Below are the topics covered in this tutorial: 1. Variables 2. Data types 3. Operators 4. Conditional Statements 5. Loops 6. Strings 7. Functions Check out our R Playlist: https://goo.gl/huUh7Y Subscribe to our channel to get video updates. Hit the subscribe button above. #R #Rtutorial #Ronlinetraining #Rforbeginners #Rprogramming How it Works? 1. This is a 5 Week Instructor led Online Course, 30 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. During our Data Analytics with R Certification training, our instructors will help you: 1. Understand concepts around Business Intelligence and Business Analytics 2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others 3. Apply various supervised machine learning techniques 4. Perform Analysis of Variance (ANOVA) 5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc 6. Use various packages in R to create fancy plots 7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights - - - - - - - - - - - - - - - - - - - Who should go for this course? This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics. - - - - - - - - - - - - - - - - Why learn Data Analytics with R? The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists. Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Telegram: https://t.me/edurekaupdates
Views: 409041 edureka!
Decision Tree Learning using ID3 Algorithm | Artificial intelligence | Machine Learning
 
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#askfaizan | #syedfaizanahmad | #decisiontree PlayList : Artificial Intelligence : https://www.youtube.com/playlist?list=PLhwpdymnbXz4fEjqBoJbvLTIqfZJfXjbH Bayesian Network in Artificial Intelligence | Bayesian Belief Network | https://youtu.be/0U5xH4b7nPc Decision Tree Learning using ID3 Algorithm | Artificial intelligence https://youtu.be/pvTejBgiF3I Supervised Learning and Unsupervised Learning | Learning in Artificial Intelligence https://youtu.be/Wn2JgBfAsSM Genetic Algorithm | Artificial Intelligence Tutorial in Hindi Urdu https://youtu.be/frB2zIpOOBk Comparison of Search Algorithm https://youtu.be/QMz7jwXDvwg Resolution in Artificial Intelligence | Resolution Rules in AI https://youtu.be/oQmqJPLqHZA Inference rules in Predicate logic https://youtu.be/Y8KCh4VRRwM Predicate logic in AI | First order logic in Artificial Intelligence https://youtu.be/sFINpc5KA3E Wumpus World Proving | Propositional logic Example https://youtu.be/bDu9iNJ8h58 PROPOSITIONAL LOGIC | Artificial Intelligence https://youtu.be/oUR11UUIDvA Knowledge based Agents | Logical agents https://youtu.be/Y7CS-1BfA6o Alpha Beta Pruning | Problem #2 https://youtu.be/QL-g1FDls74 A Decision tree represents a function that takes as input a vector of attribute values and returns a “decision”—a single output value. The input and output values can be discrete or continuous. A decision tree reaches its decision by performing a sequence of tests. There are many specific decision-tree algorithms. Notable ones include: ID3 (Iterative Dichotomiser 3) C4.5 (successor of ID3) CART (Classification And Regression Tree) CHAID (Chi-squared Automatic Interaction Detector). Performs multi-level splits when computing classification trees. MARS: extends decision trees to handle numerical data better. ID3 is one of the most common decision tree algorithm Dichotomisation means dividing into two completely opposite things. Algorithm iteratively divides attributes into two groups which are the most dominant attribute and others to construct a tree. Then, it calculates the Entropy and Information Gains of each attribute. In this way, the most dominant attribute can be founded. After then, the most dominant one is put on the tree as decision node. Entropy and Gain scores would be calculated again among the other attributes. Procedure continues until reaching a decision for that branch. algorithm steps: Calculate the entropy of every attribute using the data set S Entropy(S) = ∑ – p(I) . log2p(I) Split the set S into subsets using the attribute for which the resulting entropy (after splitting) is minimum (or, equivalently, information gain is maximum) Gain(S, A) = Entropy(S) – ∑ [ p(S|A) . Entropy(S|A) ] Make a decision tree node containing that attribute Recurse on subsets using remaining attributes. for Complete Artificial Intelligence Videos click on the link : https://www.youtube.com/playlist?list=PLhwpdymnbXz4fEjqBoJbvLTIqfZJfXjbH Thank you for watching share with your friends Follow on : Facebook page : https://www.facebook.com/askfaizan1/ Instagram page : https://www.instagram.com/ask_faizan/ Twitter : https://twitter.com/ask_faizan/
Views: 46934 Ask Faizan
Data Science & Machine Learning - Numeric Predictions using Regression Trees - DIY- 14 -of-50
 
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Data Science & Machine Learning - Numeric Predictions using Regression Trees - DIY- 14 -of-50 Do it yourself Tutorial by Bharati DW Consultancy cell: +1-562-646-6746 (Cell & Whatsapp) email: [email protected] [email protected] website: http://bharaticonsultancy.in/ Google Drive- https://drive.google.com/open?id=0ByQlW_DfZdxHeVBtTXllR0ZNcEU Regression trees Regression trees are similar to Flowchart / Decision tree. A tree consists of decision nodes, leaf nodes. A Sample Regression Tree output. Decision Trees are generally used for Classification. Regression Trees may also be used for Numeric Predictions. They bring together the ability of decision trees to model and predict numeric data. They may make predictions using the average values of examples that reach a leaf, and (or) creating a Linear model using the examples reaching a node. They may fit some types of data much better than Linear Regression Data Science & Machine Learning - Getting Started - DIY- 1 -of-50 Data Science & Machine Learning - R Data Structures - DIY- 2 -of-50 Data Science & Machine Learning - R Data Structures - Factors - DIY- 3 -of-50 Data Science & Machine Learning - R Data Structures - List & Matrices - DIY- 4 -of-50 Data Science & Machine Learning - R Data Structures - Data Frames - DIY- 5 -of-50 Data Science & Machine Learning - Frequently used R commands - DIY- 6 -of-50 Data Science & Machine Learning - Frequently used R commands contd - DIY- 7 -of-50 Data Science & Machine Learning - Installing RStudio- DIY- 8 -of-50 Data Science & Machine Learning - R Data Visualization Basics - DIY- 9 -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(a) -of-50 Data Science & Machine Learning - Linear Regression Model - DIY- 10(b) -of-50 Data Science & Machine Learning - Multiple Linear Regression Model - DIY- 11 -of-50 Data Science & Machine Learning - Evaluate Model Performance - DIY- 12 -of-50 Data Science & Machine Learning - RMSE & R-Squared - DIY- 13 -of-50 Data Science & Machine Learning - Numeric Predictions using Regression Trees - DIY- 14 -of-50 Machine learning, data science, R programming, Deep Learning, Regression, Neural Network, R Data Structures, Data Frame, RMSE & R-Squared, Regression Trees, Decision Trees
Views: 1327 BharatiDWConsultancy
Data Mining with Weka (3.6: Nearest neighbor)
 
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Data Mining with Weka: online course from the University of Waikato Class 3 - Lesson 6: Nearest neighbor http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/YjZnrh https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 47829 WekaMOOC
L1: Data Warehousing and Data Mining |Introduction to Warehousing| What is Mining| Tutorial in Hindi
 
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Join My official Whatsapp group by following link https://chat.whatsapp.com/F9XFi6QYFYOGA9JGw4gc1o L1: Data Warehousing and Data Mining | What is Warehousing| What is Mining| Tutorial in Hindi Namaskar, In the Today's lecture i will cover Introduction to Data Warehousing and Data Mining of subject Data Warehousing and Data Mining which is one of the important subject of computer science and engineering Syllabus Unit1: Data Warehousing: Overview, Definition, Data Warehousing Components, Building a Data Warehouse, Warehouse Database, Mapping the Data Warehouse to a Multiprocessor Architecture, Difference between Database System and Data Warehouse, Multi Dimensional Data Model, Data Cubes, Stars, Snow Flakes, Fact Constellations, Concept. Unit 2: Data Warehouse Process and Technology: Warehousing Strategy, Warehouse /management and Support Processes, Warehouse Planning and Implementation, Hardware and Operating Systems for Data Warehousing, Client/Server Computing Model & Data Warehousing. Parallel Processors & Cluster Systems, Distributed DBMS implementations, Warehousing Software, Warehouse Schema Design. Unit 3: Data Mining: Overview, Motivation, Definition & Functionalities, Data Processing, Form of Data Pre-processing, Data Cleaning: Missing Values, Noisy Data, (Binning, Clustering, Regression, Computer and Human inspection), Inconsistent Data, Data Integration and Transformation. Data Reduction:-Data Cube Aggregation, Dimensionality reduction, Data Compression, Numerosity Reduction, Discretization and Concept hierarchy generation, Decision Tree. Unit 4: Classification: Definition, Data Generalization, Analytical Characterization, Analysis of attribute relevance, Mining Class comparisons, Statistical measures in large Databases, Statistical-Based Algorithms, Distance-Based Algorithms, Decision Tree-Based Algorithms. Clustering: Introduction, Similarity and Distance Measures, Hierarchical and Partitional Algorithms. Hierarchical Clustering- CURE and Chameleon. Density Based Methods-DBSCAN, OPTICS. Grid Based Methods- STING, CLIQUE. Model Based Method –Statistical Approach, Association rules: Introduction, Large Item sets, Basic Algorithms, Parallel and Distributed Algorithms, Neural Network approach. Unit 5: Data Visualization and Overall Perspective: Aggregation, Historical information, Query Facility, OLAP function and Tools. OLAP Servers, ROLAP, MOLAP, HOLAP, Data Mining interface, Security, Backup and Recovery, Tuning Data Warehouse, Testing Data Warehouse. Warehousing applications and Recent Trends: Types of Warehousing Applications, Web Mining, Spatial Mining and Temporal Mining I am Sandeep Vishwakarma (www.universityacademy.in) from Raj Kumar Goel Institute of Technology Ghaziabad. I have started a YouTube Channel Namely “University Academy” Teaching Training and Informative. On This channel am providing following services. 1 . Teaching: Video Lecture of B.Tech./ M.Tech. Technical Subject who provide you deep knowledge of particular subject. Compiler Design: https://www.youtube.com/playlist?list=PL-JvKqQx2Ate5DWhppx-MUOtGNA4S3spT Principle of Programming Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdIkEFDrqsHyKWzb5PWniI1 Theory of Automata and Formal Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdhlS7j6jFoEnxmUEEsH9KH 2. Training: Video Playlist of Some software course like Android, Hadoop, Big Data, IoT, R programming, Python, C programming, Java etc. Android App Development: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdBj8aS-3WCVgfQ3LJFiqIr 3. Informative: On this Section we provide video on deep knowledge of upcoming technology, Innovation, tech news and other informative. Tech News: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdFG5kMueyK5DZvGzG615ks Other: https://www.youtube.com/playlist?list=PL-JvKqQx2AtfQWfBddeH_zVp2fK4V5orf Download You Can Download All Video Lecture, Lecture Notes, Lab Manuals and Many More from my Website: http://www.universityacademy.in/ Regards University Academy UniversityAcademy Website: http://www.universityacademy.in/ YouTube: https://www.youtube.com/c/UniversityAcademy Facebook: https://www.facebook.com/UniversityAcademyOfficial Twitter https://twitter.com/UniAcadofficial Instagram https://www.instagram.com/universityacademyofficial Google+: https://plus.google.com/+UniversityAcademy
Views: 3886 University Academy
Association analysis: Frequent Patterns, Support, Confidence and Association Rules
 
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This lecture provides the introductory concepts of Frequent pattern mining in transnational databases.
Views: 66754 StudyKorner
Data Structures and Algorithms Complete Tutorial Computer Education for All
 
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Computer Education for all provides complete lectures series on Data Structure and Applications which covers Introduction to Data Structure and its Types including all Steps involves in Data Structures:- Data Structure and algorithm Linear Data Structures and Non-Linear Data Structure on Stack Data Structure on Arrays Data Structure on Queue Data Structure on Linked List Data Structure on Tree Data Structure on Graphs Abstract Data Types Introduction to Algorithms Classifications of Algorithms Algorithm Analysis Algorithm Growth Function Array Operations Two dimensional Arrays Three Dimensional Arrays Multidimensional arrays Matrix operations Operations on linked lists Applications of linked lists Doubly linked lists Introductions to stacks Operations on stack Array based implementation of stack Queue Data Structures Operations on Queues Linked list based implementation of queues Application of Trees Binary Trees Types of Binary Trees Implementation of Binary Trees Binary Tree Traversal Preorder Post order In order Binary Search Tree Introduction to Sorting Analysis of Sorting Algorithms Bubble Sort Selection Sort Insertion Sort Shell Sort Heap Sort Merge Sort Quick Sort Applications of Graphs Matrix representation of Graphs Implementations of Graphs Breadth First Search Topological Sorting Subscribe for More https://www.youtube.com/channel/UCiV37YIYars6msmIQXopIeQ Find us on Facebook: https://web.facebook.com/Computer-Education-for-All-1484033978567298 Java Programming Complete Tutorial for Beginners to Advance | Complete Java Training for all https://youtu.be/gg2PG3TwLx4
kNN Machine Learning Algorithm - Excel
 
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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: 71675 Jalayer Academy
IS 640 R Data Mining Project Solutions
 
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SOLUTION LINK: http://libraay.com/downloads/is-640-r-data-mining-project-solutions/ Use Random Forests, Neural Networks and Support Vector Machines to predict loan status (default or not). Understand the difference between in-sample fitting and out-of-sample predictive performance. Use two cross-validation methods to assess analytic model performance. Save this file on your desktop as yourlastname_640DM.docx. Load the Loan.csv data set into R. It lists the outcome of 850 loans. The data variables include loan status, credit grade (from excellent to poor), loan amount, loan age (in months), borrower’s interest rate and the debt to income ratio. Code loan status as a binary outcome (0 for current loans, 1 for late or default loans). Display the column names from the loan data set. Fit the loan data set using random forest function. Copy the trained random forest model and the confusion matrix from R and paste it below. [10 points] Randomly select 750 out of 850 loans as your training sample. Use the remaining 100 loans as your test set. Train the 2nd random forest model using the training set. Apply the 2nd model to the test set to predict loan status. Compare your predictions to the true loan statuses (using table function). Display the confusion matrix below. Based on this confusion matrix, what’s the overall misclassification rate? [10 points] Fit the loan data set using an artificial neural network. Use six neurons in the hidden layer of the ANN. Set maxit to 1000. Use table function to compare in-sample predictions to the true loan statuses. Display the confusion matrix below. [10 points]. Use the training sample (750 randomly selected loans) to build the 2nd artificial neural network. Use six neurons in the hidden layer of the ANN. Set maxit to 1000. Use table function to compare out-of-sample predictions to the true loan statuses (use the remaining 100 loans as your test set). Display the confusion matrix below. [10 points]. Use the training sample (750 randomly selected loans) to build a model of support vector machine. Use table function to compare the SVM’s out-of-sample predictions to the true loan statuses (use the remaining 100 loans as your test set). Display the confusion matrix below. [10 points]. Randomly shuffle the loan data set. Run 10-fold cross-validation to evaluate the out-of-sample performance of Random Forest, ANN and SVM. Based on your cross-validation results, which model has the best out-of-sample performance? Please briefly explain why. [30 points] Run leave-one-out cross-validation to evaluate the performance of random forest algorithm in predicting loan status. Why does it take much longer to run leave-one-out cross-validation than to run ten-fold cross-validation? Based on the result of your leave-one-out cross-validation, how many loans are misclassified by the random forest model?[20 points] Please save your word file as a pdf file named yourlastname_640DM.pdf. Submit the pdf file through the drop box in your Canvas account.
Views: 118 Libraay Downloads
Euclidean Distance - Practical Machine Learning Tutorial with Python p.15
 
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In the previous tutorial, we covered how to use the K Nearest Neighbors algorithm via Scikit-Learn to achieve 95% accuracy in predicting benign vs malignant tumors based on tumor attributes. Now, we're going to dig into how K Nearest Neighbors works so we have a full understanding of the algorithm itself, to better understand when it will and wont work for us. We will come back to our breast cancer dataset, using it on our custom-made K Nearest Neighbors algorithm and compare it to Scikit-Learn's, but we're going to start off with some very simple data first. K Nearest Neighbors boils down to proximity, not by group, but by individual points. Thus, all this algorithm is actually doing is computing distance between points, and then picking the most popular class of the top K classes of points nearest to it. There are various ways to compute distance on a plane, many of which you can use here, but the most accepted version is Euclidean Distance, named after Euclid, a famous mathematician who is popularly referred to as the father of Geometry, and he definitely wrote the book (The Elements) on it. https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 96485 sentdex
What is a HashTable Data Structure - Introduction to Hash Tables , Part 0
 
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This tutorial is an introduction to hash tables. A hash table is a data structure that is used to implement an associative array. This video explains some of the basic concepts regarding hash tables, and also discusses one method (chaining) that can be used to avoid collisions. Wan't to learn C++? I highly recommend this book http://amzn.to/1PftaSt Donate http://bit.ly/17vCDFx STILL NEED MORE HELP? Connect one-on-one with a Programming Tutor. Click the link below: https://trk.justanswer.com/aff_c?offer_id=2&aff_id=8012&url_id=238 :)
Views: 815108 Paul Programming
Time Series Analysis in Python | Time Series Forecasting | Data Science with Python | Edureka
 
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** Python Data Science Training : https://www.edureka.co/python ** This Edureka Video on Time Series Analysis n Python will give you all the information you need to do Time Series Analysis and Forecasting in Python. Below are the topics covered in this tutorial: 1. Why Time Series? 2. What is Time Series? 3. Components of Time Series 4. When not to use Time Series 5. What is Stationarity? 6. ARIMA Model 7. Demo: Forecast Future Subscribe to our channel to get video updates. Hit the subscribe button above. Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm #timeseries #timeseriespython #machinelearningalgorithms - - - - - - - - - - - - - - - - - About the Course Edureka’s Course on Python helps you gain expertise in various machine learning algorithms such as regression, clustering, decision trees, random forest, Naïve Bayes and Q-Learning. Throughout the Python Certification Course, you’ll be solving real life case studies on Media, Healthcare, Social Media, Aviation, HR. During our Python Certification Training, our instructors will help you to: 1. Master the basic and advanced concepts of Python 2. Gain insight into the 'Roles' played by a Machine Learning Engineer 3. Automate data analysis using python 4. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 5. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 6. Explain Time Series and it’s related concepts 7. Perform Text Mining and Sentimental analysis 8. Gain expertise to handle business in future, living the present 9. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. 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
Views: 83854 edureka!
Naive Bayes Classifier in R
 
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Implementation of Naive Bayes Classifier in R using dataset mushroom from the UCI repository. You may wanna add pakages e1071 and rminer in R because they were not present in R x64 3.3.1 by default. Music - Daft Punk - Instant Crush ft. Julian Casblancas
R Tutorial For Beginners | R Programming Tutorial l R Language For Beginners | R Training | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R Tutorial (R Tutorial Blog: https://goo.gl/mia382) will help you in understanding the fundamentals of R tool and help you build a strong foundation in R. Below are the topics covered in this tutorial: 1. Why do we need Analytics ? 2. What is Business Analytics ? 3. Why R ? 4. Variables in R 5. Data Operator 6. Data Types 7. Flow Control 8. Plotting a graph in R Check out our R Playlist: https://goo.gl/huUh7Y Subscribe to our channel to get video updates. Hit the subscribe button above. #R #Rtutorial #Ronlinetraining #Rforbeginners #Rprogramming How it Works? 1. This is a 5 Week Instructor led Online Course, 30 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course edureka's Data Analytics with R training course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc. During our Data Analytics with R Certification training, our instructors will help you: 1. Understand concepts around Business Intelligence and Business Analytics 2. Explore Recommendation Systems with functions like Association Rule Mining , user-based collaborative filtering and Item-based collaborative filtering among others 3. Apply various supervised machine learning techniques 4. Perform Analysis of Variance (ANOVA) 5. Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc 6. Use various packages in R to create fancy plots 7. Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights - - - - - - - - - - - - - - - - - - - Who should go for this course? This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics. - - - - - - - - - - - - - - - - Why learn Data Analytics with R? The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists. Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Telegram: https://t.me/edurekaupdates
Views: 521257 edureka!
L3: Data Warehousing and Data Mining |Characteristics | Advantage | Evolution of Database Technology
 
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Join My official Whatsapp group by following link https://chat.whatsapp.com/F9XFi6QYFYOGA9JGw4gc1o L2: Data Warehousing and Data Mining |Characteristics | Advantage |Evolution of Database Technology Namaskar, In Today's lecture, i will cover Characteristics, Advantage, Evaluation of Database Technology of subject Data Warehousing and Data Mining which is one of the important subjects of computer science and engineering I am Sandeep Vishwakarma (www.universityacademy.in) from Raj Kumar Goel Institute of Technology Ghaziabad. I have started a YouTube Channel Namely “University Academy” Teaching Training and Informative. On This channel am providing following services. 1 . Teaching: Video Lecture of B.Tech./ M.Tech. Technical Subject who provide you deep knowledge of particular subject. Compiler Design: https://www.youtube.com/playlist?list=PL-JvKqQx2Ate5DWhppx-MUOtGNA4S3spT Principle of Programming Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdIkEFDrqsHyKWzb5PWniI1 Theory of Automata and Formal Language: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdhlS7j6jFoEnxmUEEsH9KH 2. Training: Video Playlist of Some software course like Android, Hadoop, Big Data, IoT, R programming, Python, C programming, Java etc. Android App Development: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdBj8aS-3WCVgfQ3LJFiqIr 3. Informative: On this Section we provide video on deep knowledge of upcoming technology, Innovation, tech news and other informative. Tech News: https://www.youtube.com/playlist?list=PL-JvKqQx2AtdFG5kMueyK5DZvGzG615ks Other: https://www.youtube.com/playlist?list=PL-JvKqQx2AtfQWfBddeH_zVp2fK4V5orf Download You Can Download All Video Lecture, Lecture Notes, Lab Manuals and Many More from my Website: http://www.universityacademy.in/ Regards University Academy UniversityAcademy Website: http://www.universityacademy.in/ YouTube: https://www.youtube.com/c/UniversityAcademy Facebook: https://www.facebook.com/UniversityAcademyOfficial Twitter https://twitter.com/UniAcadofficial Instagram https://www.instagram.com/universityacademyofficial Google+: https://plus.google.com/+UniversityAcademy
Views: 915 University Academy
Intro and Getting Stock Price Data - Python Programming for Finance p.1
 
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Welcome to a Python for Finance tutorial series. In this series, we're going to run through the basics of importing financial (stock) data into Python using the Pandas framework. From here, we'll manipulate the data and attempt to come up with some sort of system for investing in companies, apply some machine learning, even some deep learning, and then learn how to back-test a strategy. I assume you know the fundamentals of Python. If you're not sure if that's you, click the fundamentals link, look at some of the topics in the series, and make a judgement call. If at any point you are stuck in this series or confused on a topic or concept, feel free to ask for help and I will do my best to help. https://pythonprogramming.net https://twitter.com/sentdex https://www.facebook.com/pythonprogramming.net/ https://plus.google.com/+sentdex
Views: 335649 sentdex
[Hindi] K Nearest Neighbor Classification In Python - Machine Learning Tutorials Using Python Hindi
 
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This video is a part of my Machine Learning Using Python Playlist - https://www.youtube.com/playlist?list=PLu0W_9lII9ai6fAMHp-acBmJONT7Y4BSG ►Click here to subscribe - https://www.youtube.com/channel/UCeVMnSShP_Iviwkknt83cww Best Hindi Videos For Learning Programming: ►Learn Python In One Video - https://www.youtube.com/watch?v=qHJjMvHLJdg ►Learn JavaScript in One Video - https://www.youtube.com/watch?v=onbBV0uFVpo ►Learn PHP In One Video - https://www.youtube.com/watch?v=xW7ro3lwaCI ►Machine Learning Using Python - https://www.youtube.com/playlist?list=PLu0W_9lII9ai6fAMHp-acBmJONT7Y4BSG ►Creating & Hosting A Website (Tech Blog) Using Python - https://www.youtube.com/playlist?list=PLu0W_9lII9agAiWp6Y41ueUKx1VcTRxmf ►Advanced Python Tutorials - https://www.youtube.com/playlist?list=PLu0W_9lII9aiJWQ7VhY712fuimEpQZYp4 ►Object Oriented Programming In Python - https://www.youtube.com/playlist?list=PLu0W_9lII9ahfRrhFcoB-4lpp9YaBmdCP ►Python Data Science and Big Data Tutorials - https://www.youtube.com/playlist?list=PLu0W_9lII9agK8pojo23OHiNz3Jm6VQCH Follow Me On Social Media ►Website (created using Flask) - https://www.codewithharry.com ►Facebook - https://www.facebook.com/CodeWithHarry ►Instagram (Guaranteed Replies :)) - https://www.instagram.com/haris_magical/ ►Personal Facebook A/c - https://www.facebook.com/geekyharis Twitter - https://twitter.com/Haris_Is_Here
Views: 2739 CodeWithHarry
Twitter Sentiment Analysis - Learn Python for Data Science #2
 
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In this video we'll be building our own Twitter Sentiment Analyzer in just 14 lines of Python. It will be able to search twitter for a list of tweets about any topic we want, then analyze each tweet to see how positive or negative it's emotion is. The coding challenge for this video is here: https://github.com/llSourcell/twitter_sentiment_challenge Naresh's winning code from last episode: https://github.com/Naresh1318/GenderClassifier/blob/master/Run_Code.py Victor's Runner up code from last episode: https://github.com/Victor-Mazzei/ml-gender-python/blob/master/gender.py I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ More on TextBlob: https://textblob.readthedocs.io/en/dev/ Great info on Sentiment Analysis: https://www.quora.com/How-does-sentiment-analysis-work Great sentiment analysis api: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis Read over these course notes if you wanna become an NLP god: http://cs224d.stanford.edu/syllabus.html Best book to become a Python god: https://learnpythonthehardway.org/ Please share this video, like, comment and subscribe! That's what keeps me going. Feel free to support me on Patreon: https://www.patreon.com/user?u=3191693 Two Minute Papers Link: https://www.youtube.com/playlist?list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e 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: 281830 Siraj Raval
K Means Clustering Algorithm | K Means Clustering Example | Machine Learning Algorithm | Intellipaat
 
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This data science tutorial video from Intellipaat will help you learn K-means clustering algorithm with example. K-means clustering also comes under the domain of unsupervised learning in machine learning algorithm wherein the data is not labelled. This means that there are no groups or categories within which the data falls. So in such a scenario we use K-means clustering to classify the data based on their affinity to be within a certain group which is assigned by the number K. So this K-means clustering algorithm essentially creates a cluster of data points around the K groups based on similar features. Intellipaat Data Science course:- https://intellipaat.com/data-scientist-course-training/ Intellipaat data science tutorial video includes the various aspects of data science, K-means algorithm in machine learning, clustering in machine learning, K groups based on features, algorithm in machine learning, labels for the training data, running the K-means algorithm, segregating the data into K number of clusters and so on. Interested to learn more about Data Science? Please check similar blogs here:- https://goo.gl/94cLeV Watch complete Data Science tutorials here:- https://goo.gl/XHuUPc Are you looking for something more? Enroll in our Data Science course & become a certified Data Science Professional (https://goo.gl/yaU9Lf). It is a 40 hrs instructor led Data Science training provided by Intellipaat which is completely aligned with industry standards and certification bodies. If you’ve enjoyed this introduction to K Means to clustering In r tutorial, Like the video and Subscribe to our channel for more similar informative Data Science tutorials. Got any questions about Data Science training? Ask us in the comment section below. ---------------------------- Intellipaat Edge 1. 24*7 Life time Access & Support 2. Flexible Class Schedule 3. Job Assistance 4. Mentors with +14 yrs 5. Industry Oriented Course ware 6. Life time free Course Upgrade ------------------------------ Why should you watch this K Means clustering In r video? Today a majority of the data is unlabeled. So due to this it is hard to find valuable insights from it. This is where unsupervised learning comes into the picture and K-means clustering is an important algorithm within the domain of unsupervised learning. It brings order within huge amounts of unlabeled data and creates clusters of data around the K centroids. K-means clustering is very important to machine learning and as a direct consequence to data science and data analysis. As part of this data science tutorial you will be performing K-means clustering and analysis using a real-world data set. This data science tutorial will also give you the K-means clustering examples. If you want to learn K-means clustering, then this video from Intellipaat will help you do just that and help you implement K-means clustering algorithm. Upon finishing watching this video you will be well-versed in K-means clustering and its deployment for data science applications. Why Data Science is important? Data Science is taking over each and every industry domain. Machine Learning and especially Deep Learning are the most important aspects of Data Science that are being deployed everywhere from search engines to online movie recommendations. Taking the Intellipaat Data Science training & Data Science course can help professionals to build a solid career in a rising technology domain and get the best jobs in top organizations. Why should you opt for a Data Science career? If you want to fast-track your career then you should strongly consider Data Science. The reason for this is that it is one of the fastest growing technology. There is a huge demand for Data Scientist. The salaries for Data Scientist is fantastic.There is a huge growth opportunity in this domain as well. Hence this Intellipaat data science with r tutorial is your stepping stone to a successful career! #KMeansClusteringAlgorithm #KMeansClusteringExample #MachineLearningAlgorithm ------------------------------ For more Information: Please write us to [email protected], or call us at: +91- 7847955955 Website: https://goo.gl/VL4h3Q Facebook: https://www.facebook.com/intellipaatonline LinkedIn: https://www.linkedin.com/in/intellipaat/ Twitter: https://twitter.com/Intellipaat
Views: 1136 Intellipaat